inferences on multivariate measures of interclass and intraclass correlations in familial data summary multivariate measures of interclass and intraclass correlations are introduced to assess the degree of resemblance between family members with respect to more than one characteristic unified estimators for the multivariate measures are proposed as the eigenvalues of certain random matrices constructed by the matrices of the weighted sums of squares and products of observations asymptotic distributions of the proposed estimators are derived under the assumption of multivariate normality the asymptotic results produce approximate confidence intervals and test procedures in a simple way

a nonlinear functional for general scalar hyperbolic conservation laws abstract a generalized entropy functional was introduced in t p liu t yang a new entropy functional for scalar conservation laws comm pure appl math 52 1999 1427 1442 for the scalar hyperbolic conservation laws with convex flux function this functional was crucially used in the functional approach to the l 1 stability study on the system of hyperbolic conservation laws when each characteristic field is either genuinely nonlinear or linearly degenerate however how to construct the generalized entropy functional for scalar conservation laws with general flux and then how to apply the functional approach to the l 1 study on general systems are still open in this paper we construct a new nonlinear functional which gives some partial answer to this question and we expect the analysis will shed some light on the future investigation in this direction

l 1 stability estimates for n x n conservation laws let u t f u x 0 be a strictly hyperbolic n times n system of conservation laws each characteristic field being linearly degenerate or genuinely nonlinear in this paper we explicitly define a functional phi phi u v equivalent to the l 1 distance which is almost decreasing i e phi big u t v t big phi big u s v s big leq o ve cdot t s quad hbox for all t s geq 0 for every pair of e approximate solutions u v with small total variation generated by a wave front tracking algorithm the small parameter e here controls the errors in the wave speeds the maximum size of rarefaction fronts and the total strength of all non physical waves in u and in v from the above estimate it follows that front tracking approximations converge to a unique limit solution depending lipschitz continuously on the initial data in the vec l 1 norm this provides a new proof of the existence of the standard riemann semigroup generated by a n n system of conservation laws

vanishing viscosity solutions of nonlinear hyperbolic systems we consider the cauchy problem for a strictly hyperbolic n n system in one space dimension ut a u ux 0 assuming that the initial data have small total variation we show that the solutions of the viscous approximations ut a u ux euxx are defined globally in time and satisfy uniform bv estimates independent of e moreover they depend continuously on the initial data in the l 1 distance with a lipschitz constant independent of t e letting e 0 these viscous solutions converge to a unique limit depending lipschitz continuously on the initial data in the conservative case where a df is the jacobian

a bootstrap recipe for post model selection inference under linear regression models summarywe propose a general bootstrap recipe for estimating the distributions of post model selection least squares estimators under a linear regression model the recipe constrains residual bootstrapping within the most parsimonious approximately correct models to yield a distribution estimator which is consistent provided any wrong candidate model is sufficiently separated from the approximately correct ones our theory applies to a broad class of model selection methods based on information criteria or sparse estimation the empirical performance of our procedure is illustrated with simulated data

scaling up cosine interesting pattern discovery a depth first method this paper presents an efficient algorithm called cosminer t for interesting pattern discovery the widely used cosine similarity found to possess the null invariance property and the anti cross support pattern property is adopted as the interestingness measure in cosminer t cosminer t is generally an fp growth like depth first traversal algorithm that rests on an important property of the cosine similarity the conditional anti monotone property camp the combined use of camp and the depth first support ascending traversal strategy enables the pre pruning of uninteresting patterns during the mining process of cosminer t extensive experiments demonstrate the high efficiency of cosminer t in interesting pattern discovery in comparison to the breath first strategy and the post evaluation strategy in particular cosminer t shows its capability in suppressing the generation of cross support patterns and discovering rare but truly interesting patterns finally an interesting case of landmark recognition is presented to illustrate the value of cosine interesting patterns found by cosminer t in real world applications

weak solutions of general systems of hyperbolic conservation laws in this paper we establish the existence theory for general system of hyperbolic conservation laws and obtain the uniform l1 boundness for the solutions the existence theory generalizes the classical glimm theory for systems for which each characteristic field is either genuinely nonlinear or linearly degenerate in the sense of lax we construct the solutions by the glimm scheme through the wave tracing method one of the key elements is a new way of measuring the potential interaction of the waves of the same characteristic family involving the angle between waves a new analysis is introduced to verify the consistency of the wave tracing procedure the entropy functional is used to study the l1 boundedness

looking for a structural characterization of the sparseness measure of frequent closed itemset contexts it is widely recognized that the performances of frequent pattern mining algorithms are closely dependent on data being handled i e sparse or dense the same situation applies to the efficiency of concise representations of frequently occurring patterns with respect to the extraction task and the obtained compactness rates as well as for other data mining techniques such as clustering and for the mining algorithms of different pattern classes such as hypergraphs in this paper we raise a fundamental question how can we formally define the sparseness of an arbitrary context and assess its value as an answer based on the framework of the succinct system of minimal generators we present an innovative characterization of context sparseness as well as a new sparseness measure which results from the aggregation of two complementary measures namely the succinctness and compactness measures of each equivalence class induced by the galois closure operator experiments carried out mainly attain a finer classification of benchmark contexts and then confirm our viewpoint that the dense and sparse labels are not absolute

the variable selection problem abstract the problem of variable selection is one of the most pervasive model selection problems in statistical applications often referred to as the problem of subset selection it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables or predictors but there is uncertainty about which subset to use this vignette reviews some of the key developments that have led to the wide variety of approaches for this problem

estimation of interclass correlations in familial data summary the maximum likelihood estimates of the parent child interclass correlation and other parameters from familial data when the families have unequal numbers of offspring is given also given are two alternative sets of estimates which are easier to compute than the maximum likelihood estimates using these estimates a test for the interclass correlation to be zero requires only the ordinary student s t distribution similarly other tests use only known distributions in the analysis of familial data the degree of mother sib resemblance is measured by the interclass correlation the correlation between two different types of family member several estimates of this correlation have been proposed in the literature see rosner donner hennekens 1977 for details and their monte carlo comparisons of mean squared errors in particular they gave maximum likelihood estimates when the sib sizes are equal however when the sib sizes are not equal rosner 1979 proposed an algorithm for finding the maximum likelihood estimates which involves iterative maximization of an implicit function of two parameters this algorithm is difficult to implement and may not even converge for some sets of data recently mak ng 1981 used the linear model approach of kempthorne tandan 1953 to obtain the maximum likelihood estimates this reduces the problem to iterative maximization of a function of one parameter however nothing is known about the convergence of the procedure another iterative method of finding the maximum likelihood estimates has been given by smith 1980 in this paper an alternative approach is given which requires solving only one equation also two alternative sets of estimates are given these estimates are easier to compute than the maximum likelihood estimates using these estimates a test for the interclass correlation to be zero is given it requires only the ordinary student s t distribution similarly other tests use only known results

variable selection for multivariate failure time data in this paper we proposed a penalised pseudo partial likelihood method for variable selection with multivariate failure time data with a growing number of regression coefficients under certain regularity conditions we show the consistency and asymptotic normality of the penalised likelihood estimators we further demonstrate that for certain penalty functions with proper choices of regularisation parameters the resulting estimator can correctly identify the true model as if it were known in advance based on a simple approximation of the penalty function the proposed method can be easily carried out with the newton raphson algorithm we conduct extensive monte carlo simulation studies to assess the finite sample performance of the proposed procedures we illustrate the proposed method by analysing a dataset from the framingham heart study

efficient mining of weighted interesting patterns with a strong weight and or support affinity most algorithms for frequent pattern mining use a support constraint to prune the combinatorial search space but support based pruning is not enough after mining datasets to obtain frequent patterns the resulting patterns can have weak affinity although the minimum support can be increased it is not effective for finding correlated patterns with increased weight and or support affinity interesting measures have been proposed to detect correlated patterns but any approach does not consider both support and weight in this paper we present a new strategy weighted interesting pattern mining wip in which a new measure weight confidence is suggested to mine correlated patterns with the weight affinity a weight range is used to decide weight boundaries and an h confidence serves to identify support affinity patterns in wip without additional computation cost original h confidence is used instead of the upper bound of h confidence for performance improvement wip not only gives a balance between the two measures of weight and support but also considers weight affinity and or support affinity between items within patterns so more correlated patterns can be detected to our knowledge ours is the first work specifically to consider weight affinity between items of patterns a comprehensive performance study shows that wip is efficient and scalable for finding affinity patterns moreover it generates fewer but more valuable patterns with the correlation to decrease the number of thresholds w confidence h confidence and weighted support can be used selectively according to requirement of applications

cross entropy dissimilarity measures and characterizations of quadratic entropy a unified approach is given for constructing cross entropy and dissimilarity measures between probability distributions based on a given entropy function or a diversity measure special properties of quadratic entropy introduced by rao 7 are described in particular it is shown that the square root of the jensen difference dissimilarity measure arising out of a quadratic entropy provides a metric on a probability space several characterizations of quadratic entropy are obtained

convergence rates for the bootstrapped product limit process on etablit des taux d approximation forte pour le processus limite produit du bootstrap et le processus quantile correspondant

maximum likelihood estimation of 2 d superimposed exponential signals the problem of maximum likelihood estimation of the parameters i e frequencies amplitudes and noise variance of 2 d superimposed exponential signals is considered in this paper a polynomial rooting approach is proposed to obtain the estimates of the 2 d frequencies strong consistency and limiting distribution are established for the estimates of the parameters furthermore the covariance matrix of the limiting distribution attains the cramer rao lower bound

multitarget angle tracking an algorithm for data association the authors consider the problem of tracking several targets in terms of the directions of arrival doa of signals using uniform linear sensor array outputs the observations in any short interval of time around a given time point spl tau enable them to estimate the doa of the targets but not identify the target to which each estimate corresponds they propose an algorithm to set up such a correspondence between estimates and targets at each time point in a sequential manner starting from an initial position of the targets once this is done the trajectories of the different targets can be obtained by plotting the estimates against time

a brief biography and appreciation of calyampudi radhakrishna rao with a bibliography of his books and papers we present a brief biography and appreciation of calyampudi radhakrishna rao to whom this fifth special issue on linear algebra and statistics of linear algebra and its applications is dedicated in celebration of his 75th birthday a bibliography of his books and papers is also included

linguistic modelling of imperfect spatial information as a basis for simplifying spatial analysis spatial analysis is at the basis of several decision processes in fields such as urban planning and environmental management in order to carry it out effectively more flexible geographic information systems are needed to be able to represent and manage the imperfection that invariably affects geographic information starting from the consideration that experts usually deal with imperfect spatial information through linguistic terms e g they identify the approximate position of a phenomenon on a map and classify spatial properties through linguistic labels this contribution proposes the use of linguistic granule of information to represent and manage imperfect spatial information a fuzzy object based data model is proposed as a tool for supporting spatial analysis based on the management of linguistic granule in particular the problem of defining methods to manage imperfect information depending on the type of imperfection is discussed finally an example of spatial analysis applied to support a decision problem in environmental impact assessment is described

well posedness theory for hyperbolic conservation laws the paper presents a well posedness theory for the initial value problem for a general system of hyperbolic conservation laws we will start with the refinement of glimm s existence theory and discuss the principle of nonlinear through wave tracing our main goal is to introduce a nonlinear functional for two solutions with the property that it is equivalent to the l1 x distance between the two solutions and is time decreasing moreover the functional is constructed explicitly in terms of the wave patterns of the solutions through the nonlinear superposition it consists of a linear term measuring the l1 x distance a quadratic term measuring the coupling of waves and distance and a generalized entropy functional 1999 john wiley sons inc

fuzzy association rules and the extended mining algorithms this paper focuses on the notion of fuzzy association rules that are of the form x y where either x or y is a collection of fuzzy sets the work stems from two observations first in generalized association rule mining the taxonomies concerned may not be crisp but fuzzy e g tomato could be regarded as both fruit and vegetable each at a different degree second managers often refer to decision rules in terms of linguistic expressions that may or may not be the nodes of the taxonomies e g very expensive cloth tropical fruit the paper deals with the fuzziness based upon fuzzy taxonomies that reflect partial belongings among itemsets as well as upon the extended settings for the degree of support and the degree of confidence apriori algorithms are extended accordingly to discover association rules across the higher level taxonomic nodes which are fuzzy sets in general as a result the discovered rules are fuzzy rules furthermore linguistic hedges are also incorporated in mining fuzzy rules to express more meaningful knowledge moreover the extended algorithms are tested with the synthetic data revealing similar computational complexities to that of the classical algorithm finally the extended algorithms are applied to a real data set with an explanation of the semantics of discovered fuzzy rules

bootstrap modeling of a class of nonstationary signals the problem of modeling polynomial phase signals is considered techniques based on the bootstrap and multiple hypotheses tests for optimal model selection of constant amplitude polynomial phase signals embedded in stationary noise are developed phase parameter estimators based on both the least squares method and the polynomial phase transform are used the proposed techniques are compared with existing ones including akaike s 1978 1974 information criterion and rissanen s 1978 minimum description length criterion and are shown to outperform these procedures for the considered small sample sizes

a model for describing and composing direction relations between overlapping and contained regions current models of direction relations are not designed to describe direction information inside the minimum bounding rectangle mbr of a reference region thus the direction relations between overlapping and contained regions cannot be effectively described and derived to resolve this problem a new model of direction relations namely interior boundary direction relations is proposed in this study to describe direction concepts relative to the interior or boundary of a region such as east part of a region west border of a region a line goes through east part of a region and etc by combining the interior and exterior direction relations three types of compositions of direction relations are investigated 1 composing two interior direction relations which can be used to derive the interior or exterior direction relations between two regions with the same parent region 2 composing an interior direction relation with an exterior direction relation and 3 composing an interior with an exterior direction relation the results indicate that the new interior boundary direction relations and its compositions with exterior direction relations are powerful in describing and deriving direction relations between overlapped and contained regions

iconic indexing by 2 d strings in this paper we describe a new way of representing a symbolic picture by a two dimensional string a picture query can also be specified as a 2 d string the problem of pictorial information retrieval then becomes a problem of 2 d subsequence matching we present algorithms for encoding a symbolic picture into its 2 d string representation reconstructing a picture from its 2 d string representation and matching a 2 d string with another 2 d string we also prove the necessary and sufficient conditions to characterize ambiguous pictures for reduced 2 d strings as well as normal 2 d strings this approach thus allows an efficient and natural way to construct iconic indexes for pictures

identification of non linear additive autoregressive models summary we propose a lag selection method for non linear additive autoregressive models that is based on spline estimation and the bayes information criterion the additive structure of the autoregression function is used to overcome the curse of dimensionality whereas the spline estimators effectively take into account such a structure in estimation a stepwise procedure is suggested to implement the method proposed a comprehensive monte carlo study demonstrates good performance of the method proposed and a substantial computational advantage over existing local polynomial based methods consistency of the lag selection method based on the bayes information criterion is established under the assumption that the observations are from a stochastic process that is strictly stationary and strongly mixing which provides the first theoretical result of this kind for spline smoothing of weakly dependent data

asymptotic behavior of maximum likelihood estimates of superimposed exponential signals strong consistency and asymptotic normality are derived for the maximum likelihood estimates mles of the unknown parameters omega sub 1 omega sub p alpha sub 1 alpha sub p and sigma sup 2 in the superimposed exponential model for signals y sub t sigma alpha exp it omega sub k e sub t where the summation is from k 1 to p t 0 1 n 1 and sigma sup 2 is the variance of the complex normal distribution of e sub t as a by product it is found that the mles of the parameters attain the cramer rao lower bound for the asymptotic covariance matrix

better bootstrap confidence intervals abstract we consider the problem of setting approximate confidence intervals for a single parameter in a multiparameter family the standard approximate intervals based on maximum likelihood theory can be quite misleading in practice tricks based on transformations bias corrections and so forth are often used to improve their accuracy the bootstrap confidence intervals discussed in this article automatically incorporate such tricks without requiring the statistician to think them through for each new application at the price of a considerable increase in computational effort the new intervals incorporate an improvement over previously suggested methods which results in second order correctness in a wide variety of problems in addition to parametric families bootstrap intervals are also developed for nonparametric situations

a model selection approach for the identification of quantitative trait loci in experimental crosses summary we consider the problem of identifying the genetic loci called quantitative trait loci qtls contributing to variation in a quantitative trait with data on an experimental cross a large number of different statistical approaches to this problem have been described most make use of multiple tests of hypotheses and many consider models allowing only a single qtl we feel that the problem is best viewed as one of model selection we discuss the use of model selection ideas to identify qtls in experimental crosses we focus on a back cross experiment with strictly additive qtls and concentrate on identifying qtls considering the estimation of their effects and precise locations of secondary importance we present the results of a simulation study to compare the performances of the more prominent methods

extended bayesian information criteria for model selection with large model spaces summary the ordinary bayesian information criterion is too liberal for model selection when the model space is large in this paper we re examine the bayesian paradigm for model selection and propose an extended family of bayesian information criteria which take into account both the number of unknown parameters and the complexity of the model space their consistency is established in particular allowing the number of covariates to increase to infinity with the sample size their performance in various situations is evaluated by simulation studies it is demonstrated that the extended bayesian information criteria incur a small loss in the positive selection rate but tightly control the false discovery rate a desirable property in many applications the extended bayesian information criteria are extremely useful for variable selection in problems with a moderate sample size but with a huge number of covariates especially in genome wide association studies which are now an active area in genetics research

the rate of strong uniform consistency for the product limit estimator the maximal deviation of the product limit estimate from the estimated distribution function is investigated as a consequence of a functional law of the iterated logarithm the log log law is proved for this deviation on appropriate half lines with the precise constants this result implies that the log log law need not hold in general for the maximal deviation on the whole line then a general asymptotic order of magnitude is determined for the latter deviation this order depends on the joint behaviour of the censoring and censored distributions in a well defined way corresponding to specific joint behaviours several limsup results are deduced as consequence including all the previously known log log type laws in improved form the results are also extended to the variable censoring model

mining spatial association rules in image databases in this paper we propose a novel spatial mining algorithm called 9dlt miner to mine the spatial association rules from an image database where every image is represented by the 9dlt representation the proposed method consists of two phases first we find all frequent patterns of length one next we use frequent k patterns k 1 to generate all candidate k 1 patterns for each candidate pattern generated we scan the database to count the pattern s support and check if it is frequent the steps in the second phase are repeated until no more frequent patterns can be found since our proposed algorithm prunes most of impossible candidates it is more efficient than the apriori algorithm the experiment results show that 9dlt miner runs 2 5 times faster than the apriori algorithm

a new glimm functional and convergence rate of glimm scheme for general systems of hyperbolic conservation laws in this paper we introduce a new glimm functional for general systems of hyperbolic conservation laws this new functional is consistent with the classical glimm functional for the case when each characteristic field is either genuinely nonlinear or linearly degenerate so that it can be viewed as optimal in some sense with this new functional the consistency of the glimm scheme is proved clearly for general systems moreover the convergence rate of the glimm scheme is shown to be the same as the one obtained in bressan marson arch ration mech anal 142 2 155 176 1998 for systems with each characteristic field being genuinely nonlinear or linearly degenerate

generalized inverse of linear transformations a geometric approach abstract a generalized inverse of a linear transformation a where and are arbitrary finite dimensional vector spaces is defined using only geometrical concepts of linear transformations the inverse is uniquely defined in terms of specified subspaces l m and a linear transformation n satisfying some conditions such an inverse is called the lm n inverse a moore penrose type inverse is obtained by choosing n 0 some optimization problems are considered by choosing and as inner product spaces our results extend without any major modification of proofs to bounded linear operators with closed range on hilbert spaces

effects of estimated noise covariance matrix in optimal signal detection there is loss of efficiency when an estimated noise covariance matrix is used in the place of the unknown true noise covariance matrix in the construction of the optimum filter for signal detection in the case of detecting a single signal specified by a real or a complex vector we investigate the extent of this loss by obtaining an exact confidence bound for the realized signal to noise ratio we also give an estimate of this ratio which is useful in optimum selection of features some of these results are extended to the case of discrimination between a number of given signals

joint variable selection for fixed and random effects in linear mixed effects models it is of great practical interest to simultaneously identify the important predictors that correspond to both the fixed and random effects components in a linear mixed effects lme model typical approaches perform selection separately on each of the fixed and random effect components however changing the structure of one set of effects can lead to different choices of variables for the other set of effects we propose simultaneous selection of the fixed and random factors in an lme model using a modified cholesky decomposition our method is based on a penalized joint log likelihood with an adaptive penalty for the selection and estimation of both the fixed and random effects it performs model selection by allowing fixed effects or standard deviations of random effects to be exactly zero a constrained expectation maximization algorithm is then used to obtain the final estimates it is further shown that the proposed penalized estimator enjoys the oracle property in that asymptotically it performs as well as if the true model was known beforehand we demonstrate the performance of our method based on a simulation study and a real data example

estimation of interclass and intraclass correlations in multivariate familial data asymptotically normal estimators of interclass and intraclass correlations are derived for more than two quantitative characteristics of parent and siblings in a simple random sampling of families that have different numbers of offspring these estimators are proposed as an alternative to the maximum likelihood estimators which can be found only by iterative methods requiring prohibitively large amounts of computation the asymptotic variances of the proposed estimators are also given in an illustrative example these easily computable estimators are seen to be comparable to the corresponding maximum likelihood estimators

approximate efficiency of a selection procedure for the number of regression variables we first show a simplification and a refinement of the result obtained by shibata 1976 it is an evaluation of the mean squared error of estimates of regression parameters when the number of regression variables is selected by a certain procedure the evaluation yields a good approximation to the efficiency of a selection procedure when the number of regression variables is small the approximate efficiency of fpe akaike 1970 aic akaike 1973 or cp mallows 1973 turns out to be not satisfactorily high although they are all asymptotically efficient in the sense of shibata 1981 based on such an approximation a procedure for choosing a out of the family fpe bhansali downham 1977 is suggested a further generalization is also developed consider a regression model y xji k l e where y yl yj is a vector of observations x is an n x k design matrix and f k fl1 fk 0 0 is a vector of regression parameters for simplicity we assume e el ej is a vector of independent normally distributed random variables with mean zero and variance a we call the above model model k in this paper we are concerned only with the problem of selecting the number k from a given range 1 k k under the model k we have a least squares estimate of the regression parameters 43 k h f1k which is a solution of x k x k k x k y here x k is an n x k submatrix consisting of the first k column vectors of the design matrix x hereafter f k is occasionally considered as the corresponding k dimensional vector with the undefined entries being zero suppose that observations yl yn come from a model ko 1 ko k with the regression parameter b such that f3k0 0 our main concern is the mean squared error

the inefficiency of least squares extensions of the kantorovich inequality abstract four different measures of inefficiency of the simple least squares estimator in the general gauss markoff model are considered previous work on the bounds to some of these measures is briefly reviewed and new bounds are obtained for a particular measure

generalized inverses of partitioned matrices useful in statistical applications abstract generalized inverses of a partitioned matrix are characterized under some rank conditions on the block matrices in the partitions

robust nonparametric methods the terms nonparametric statistics and distributionfree methods have historically referred to a collection of statistical tests whose null distributions do not depend on the underlying distribution of the data the area has been expanded to include estimates and confidence intervals derived from the tests and includes asymptotically distribution free tests for complex models as well it is now generally recognized that these statistical methods are highly efficient over large sets of possible models and are robust as well this was not always the case the earliest work was by hotelling and pabst in 1936 references to works in this paragraph can be found in the books cited in the next paragraph on rank correlation and by friedman in 1937 on rank tests in a two way design followed in 1945 with the introduction of the signed rank and rank sum tests by wilcoxon in 1947 mann and whitney extended the ideas of wilcoxon all of these rank tests were considered quick and dirty but not competitive with the presumably more efficient t tests this perspective was about to change in 1948 pitman developed efficiency concepts in a set of lecture notes that although never published were widely disseminated during the 1950s and 1960s lehmann working with hodges and with his students at berkeley showed that rank tests are surprisingly efficient and robust in fact the wilcoxon rank tests are essentially efficient at the normal model and can be much more efficient than least squares methods when the underlying distribution of the data has heavy tails in the early 1960s hodges and lehmann derived estimates and confidence intervals from rank test statistics and also introduced aligned rank tests for use in the regression model in 1967 hajek and sid published their seminal book on the theory of rank tests it is impossible to mention all of the contributions to this early development hence we cite several key mono

adaptive estimated maximum entropy distribution model the estimation of distribution algorithm eda model is an optimization procedure through learning and sampling a conditional probabilistic function the use of conditional density function permits multivariate dependency modelling which is not captured in a population based representation like the classical genetic algorithms the gaussian model is a simple and widely used model for density estimation however an assumption of normality is not realistic for many real life problems alternatively the maximum entropy model can be used which makes no assumption of a normal distribution one disadvantage of the maximum entropy model is the learning cost of its parameters this paper proposes an adaptive estimated maximum entropy distribution adaptive meed model which aims to reduce learning complexity of building a model adaptive meed exploits the fact that samples have a low average fitness in the early stage but they gradually converge to an optima towards the end of the search hence it is not necessary to inference the model with a full account of observed constraints in the early stage of the search the proposed model attempts to estimate the density function with a dynamic set of samples and active constraints in addition the proposed model includes a global sampling function to address the issue of a missing mutation operator the ergodic convergence properties of the proposed model are discussed with the markov chain analysis the preliminary experimental evaluation shows that the proposed model performs well against genetic algorithms on several clustering problems

an association based case reduction technique for case based reasoning case based reasoning cbr is a type of problem solving technique which uses previous cases to solve new unseen and different problems although a larger number of cases in the memory can improve the coverage of the problem space the retrieval efficiency will be downgraded if the size of the case base grows to an unacceptable level in cbr systems the tradeoff between the number of cases stored in the case base and the retrieval efficiency is a critical issue this paper addresses the problem of case base maintenance by developing a new technique the association based case reduction technique acrt to reduce the size of the case base in order to enhance the efficiency while maintaining or even improving the accuracy of the cbr the experiments on 12 uci datasets and an actual case from taiwan s hospital have shown superior generalization accuracy for cbr with acrt cbr acrt as well as a greater solving efficiency

a strongly consistent information criterion for linear model selection based on m estimation in this paper a linear model selection procedure based on m estimation is proposed which includes many classical model selection criteria as its special cases it is shown that the proposed criterion is strongly consistent under certain mild conditions for instance without assuming normality of the distribution of the random errors the results from a simulation study are also presented

pattern recognition based on scale invariant discriminant functions some probability models for classifying individuals as belonging to one of two or more populations using scale invariant discriminant functions are considered the investigation is motivated by practical situations where the observed data on an individual are in the form of ratios of some basic measurements or measurements scaled by an unknown nonnegative number the probability models are obtained by considering a p vector random variable x with a known distribution and deriving the distribution of the random vector y g x 1 x where g x is a nonnegative measure of size such that g x g x for 0 explicit expressions are obtained for the densities of what are called angular gaussian compositional gaussian type 1 and compositional gaussian type 2 distributions

some comments on cp i study the typical configuration of a cp plot when the number of variables in the regression problem is large and there are many weak effects i show that a particular configuration that is very commonly seen can arise in a simple way i give a formula by means of which the risk incurred by the minimum cp rule can be estimated

composite likelihood bayesian information criteria for model selection in high dimensional data for high dimensional data sets with complicated dependency structures the full likelihood approach often leads to intractable computational complexity this imposes difficulty on model selection given that most traditionally used information criteria require evaluation of the full likelihood we propose a composite likelihood version of the bayes information criterion bic and establish its consistency property for the selection of the true underlying marginal model our proposed bic is shown to be selection consistent under some mild regularity conditions where the number of potential model parameters is allowed to increase to infinity at a certain rate of the sample size simulation studies demonstrate the empirical performance of this new bic especially for the scenario where the number of parameters increases with sample size technical proofs of our theoretical results are provided in the online supplemental materials

selection of covariables in the growth curve model summary the growth curve model introduced by potthoff roy 1964 is a generalization of the multivariate analysis of variance model and is often employed to analyze longitudinal data in which a single characteristic has been measured at p different occasions on each individual inferential problems of this model are studied by using analysis of covariance i e partitioning the p measurements into the measurements of q response variables and p q covariables rao 1965 1966 and grizzle allen 1969 discuss the possibility of using fewer than p q covariables in this paper we propose two types of formulation for the hypotheses of redundancy of a given set of covariables the likelihood ratio criteria are obtained for testing the hypotheses these results permit the use of information criteria such as aic for selection of the best subset of covariables

estimation of the interclass correlation coefficient summary a noniterative method for estimating the interclass correlation coefficient is derived from the technique of weighted sums of squares the asymptotic variance of this estimator is derived under the assumption of normality through extensive monte carlo simulations the sample variance of this estimator is found not to differ greatly from that of the maximum likelihood estimator an important problem in genetics is the estimation of the degree of resemblance between a mother and her children as measured by the interclass correlation coefficient because parents produce differing numbers of offspring during their reproductive period the maximum likelihood estimation of the interclass correlation presents computational difficulties requiring an iterative solution therefore a number of noniterative estimators have been proposed in the literature these estimators will be shown to be associated with special cases of the generalized estimator derived from the weighted sums of squares of measurements on parents and offspring because our purpose is to provide a comprehensive approach to the noniterative point and interval estimation problems for the interclass correlation coefficient the asymptotic variance of the proposed estimator is derived under the assumption of normality moreover the sample variances of two special cases of the proposed estimator are compared to that of the maximum likelihood estimator through monte carlo simulations by a theoretical argument it is shown that the estimator proposed by srivastava 1984 has a uniformly smaller asymptotic variance than the ensemble estimator proposed by rosner donner hennekens 1977 smith 1957 used the weighted sums of squares approach for the estimation problem concerning the intraclass correlation coefficient which measures the degree to which siblings resemble one another our results together with those of smith 1957 provide a unified approach to estimation problems concerning familial correlations among a parent and offspring

spike and slab variable selection frequentist and bayesian strategies variable selection in the linear regression model takes many apparent faces from both frequentist and bayesian standpoints in this paper we introduce a variable selection method referred to as a rescaled spike and slab model we study the importance of prior hierarchical specifications and draw connections to frequentist generalized ridge regression estimation specifically we study the usefulness of continuous bimodal priors to model hypervariance parameters and the effect scaling has on the posterior mean through its relationship to penalization several model selection strategies some frequentist and some bayesian in nature are developed and studied theoretically we demonstrate the importance of selective shrinkage for effective variable selection in terms of risk misclassification and show this is achieved using the posterior from a rescaled spike and slab model we also show how to verify a procedure s ability to reduce model uncertainty in finite samples using a specialized forward selection strategy using this tool we illustrate the effectiveness of rescaled spike and slab models in reducing model uncertainty

testing hypotheses about interclass correlations from familial data testing problems concerning interclass correlations from familial data are considered in the case where the number of siblings varies among families under the assumption of multivariate normality two test procedures are proposed for testing the hypothesis that an interclass correlation is equal to a specified value to compare the properties of the tests including a likelihood ratio test monte carlo experiments are performed several test statistics are derived for testing whether two variables about a parent and child are uncorrelated the proposed tests are compared with previous test procedures using monte carlo simulation a general procedure for finding confidence intervals for interclass correlations is also derived

cosine interesting pattern discovery recent years have witnessed an increasing interest in computing cosine similarity between high dimensional documents transactions and gene sequences etc most previous studies limited their scope to the pairs of items which cannot be adapted to the multi itemset cases therefore from a frequent pattern mining perspective there exists still a critical need for discovering interesting patterns whose cosine similarity values are above some given thresholds however the knottiest point of this problem is the cosine similarity has no anti monotone property to meet this challenge we propose the notions of conditional anti monotone property and support ascending set enumeration tree sa set we prove that the cosine similarity has the conditional anti monotone property and therefore can be used for the interesting pattern mining if the itemset traversal sequence is defined by the sa set we also identify the anti monotone property of an upper bound of the cosine similarity which can be used in further pruning the candidate itemsets an apriori like algorithm called cosminer is then put forward to mine the cosine interesting patterns from large scale multi item databases experimental results show that cosminer can efficiently identify interesting patterns using the conditional anti monotone property of the cosine similarity and the anti monotone property of its upper bound even at extremely low levels of support

bootstrap model selection abstract in a regression problem typically there are p explanatory variables possibly related to a response variable and we wish to select a subset of the p explanatory variables to fit a model between these variables and the response a bootstrap variable model selection procedure is to select the subset of variables by minimizing bootstrap estimates of the prediction error where the bootstrap estimates are constructed based on a data set of size n although the bootstrap estimates have good properties this bootstrap selection procedure is inconsistent in the sense that the probability of selecting the optimal subset of variables does not converge to 1 as n this inconsistency can be rectified by modifying the sampling method used in drawing bootstrap observations for bootstrapping pairs response explanatory variable it is found that instead of drawing n bootstrap observations a customary bootstrap sampling plan much less bootstrap observations should be sampled the bootstrap selection p

principal component analysis for multivariate familial data summary the use of a principal component analysis is considered for multivariate data on families with different numbers of siblings the coefficients in principal components are given as the eigenvectors of the weighted sums of squares and products matrix from the sibling data asymptotic distributions of the eigenvalues and eigenvectors of the estimated covariance matrix are obtained for an elliptical population asymptotic distributions of statistics associated with reduction of dimensionality are also derived the results can be used to construct approximate confidence intervals for the coefficients and variances of principal components

sibling and parent offspring correlation estimation with variable family size abstract a method for estimating intrafamilial correlations under variable family sizes involving weightings of paired data points is introduced classical methods of intrafamilial correlation estimation and those in current use are outlined and critically analyzed extensions of the proposed estimation method to more general data structures are delineated

intraclass correlation coefficients and bootstrap methods of hierarchical binary outcomes intraclass correlation coefficients are designed to assess consistency or conformity between two or more quantitative measurements when multistage cluster sampling is implemented no methods are readily available to estimate intraclass correlations of binomial distributed outcomes within a cluster because statistical distribution of the intraclass correlation coefficients could be complicated or unspecified we propose using a bootstrap method to estimate the standard error and confidence interval within the framework of a multilevel generalized linear model we compared the results derived from a parametric bootstrap method with those from a non parametric bootstrap method and found that the non parametric method is more robust for non parametric bootstrap sampling we showed that the effectiveness of sampling on the highest level is greater than that on lower levels to illustrate the effectiveness we analyse survey data in china and do simulation studies

proceedings of the second international tampere conference in statistics keynote addresses anderson t w multivariate linear relations box g e p with r d meyer some aspects of statistical design in quality improvement hannan e j the statistical theory of linear systems rao c r estimation in linear models with mixed effects a unified theory invited papers atkinson a c robust regression and unmasking transformations baksalary j k algebraic characterizations and statistical implications ofthe commutativity of orthogonal projectors conradsen k with b k nielsen textural features useful in classification of digital images kariya t mtv model and its application to prediction of stock prices khatri c g quadratic forms and null robustness for elliptical distributions mustonen s editorial approach in statistical computing perlman m d with t w anderson consistency of invariant tests for the multivariate analysis of variance pratt j w dividing the indivisible using simple symmetry to partition variance explained pukelsheim f majorization orderings for linear regression designs pukkila t with a kalijnen on the order determination of time series models rissanen j complexity and information in contingency tables soctt a j generalized linear models with survey data sinha b k with r das robust optimum invariant unbiased tests for variance components speed t p generalized variance component models styan g p h with r e hartwig partially ordered idempotent matrices contributed papers anderson a j b fortran8x as a language forstatisticalcomputation baldessari b on regression analysisundertheintrinsicinference model for stratified dependent normal random variables brannas k control in the duration model framework danial e j and katti s k the asymptotic variances of the necessary and sufficient conditions for the infinite divisibility of discrete distributions through multivariate analysis danial e j and katti s k computing the maximum likelihood estimates of the hyper poisson parameters interactively through apl computer language diebold f x and nerlove m factor structure in a multivariate arch model of exchange ratefluctuations farebrother r w simultaneous confidence intervals when the regressor matrix is unbalanced feuerverger a some fourier procedures for time domain analysis of parametric and semi parametric time series models gelfand a e estimation of a restricted variance ratio de gooijer j g and heuts r m j higher order moments of bilinear time series processes with symmetrically distributed errors ignatov z g and kaishev v k on the computation of distributions of serial correlation coefficients through b splines jajuga k elliptically symmetric distributions and their application to classification and regression kollo t asymptotic distributions of functions of means and covariances konakov v d nonparametric density estimation l approach korhonen p and narula s c on subjectivity in statistics koukouvinos c and kounias s optimal designs in linear models and hadamard matrices latour d puntanen s and styan g p h equalities and inequalities for the canonical correlations associated with some partitioned generalized inverses of a covariance matrix liski e p identifying influential data in a growth curves model manly b f j and mcalevey l a randomization alternative to the bonferroni inequality with multiple f tests mejza s experiments in incomplete split plot designs merikoski j k underestimating the largest eigenvalue of a covariance matrix militkt j kvsmr k and cap j comparison of some influence measures in nonlinear regression nordstrom k and von rosen d algebra of subspaces with applications to problems in statistics ososkov g a robust regression for the heavy contaminated sample pynnonen s selection of variables in nonlinear discriminant analysis by information criteria salem a m and wahab s a on the determination of the amplitude of quasi stationary phenomena schaffrin b less sensitive tests by introducing stochastic linear hypotheses schall r and dunne t t on outliers and influence in the general multivariate normal linear model trenkler g stahlecker p schipp b and hering f iterative improvements of a partial minimax estimator in regression analysis wernecke k d kalb g and stdrzebecker e on classification strategies in medical functional diagnostics

looking into the seeds of time discovering temporal patterns in large transaction sets this paper studies the problem of mining frequent itemsets along with their temporal patterns from large transaction sets a model is proposed in which users define a large set of temporal patterns that are interesting or meaningful to them a temporal pattern defines the set of time points where the user expects a discovered itemset to be frequent the model is general in that i no constraints are placed on the interesting patterns given by the users and ii two measures inclusiveness and exclusiveness are used to capture how well the temporal patterns match the time points given by the discovered itemsets intuitively these measures indicate to what extent a discovered itemset is frequent at time points included in a temporal pattern p but not at time points not in p using these two measures one is able to model many temporal data mining problems appeared in the literature as well as those that have not been studied by exploiting the relationship within and between itemset space and pattern space simultaneously a series of pruning techniques are developed to speed up the mining process experiments show that these pruning techniques allow one to obtain performance benefits up to 100 times over a direct extension of non temporal data mining algorithms

nonparametric estimation of specific occurrence exposure rate in risk and survival analysis abstract a cohort of individuals exposed to some risk is followed up to a point of time m and observations on two random variables y are recorded for each individual the variable refers to one of the four possible events that can occur for an individual in the period 0 m i dies of a specific disease say cancer ii dies of a natural cause iii withdraws from the study and iv is alive and still under study at time m the variable y refers to the time at which an event occurs based on such data for n individuals we consider the problem of estimation of a specific occurrence exposure rate soer which is a risk ratio defined as the ratio of probability of death due to cancer in the interval 0 m to the mean lifetime of all individuals up to the time point m the asymptotic distribution of a nonparametric estimator of soer is shown to be normal and the asymptotic variance involves unknown parameters various ways of bootstrapping are discussed for construction of confidence interva

a strongly consistent procedure for model selection in a regression problem summary we consider the multiple regression model yn xn en where yn and en are n vector random variables xn is an n x m matrix and 83 is an m vector of unknown regression parameters each component of 3 may be zero or nonzero which gives rise to 2 possible models for multiple regression we provide a decision rule for the choice of a model which is strongly consistent for the true model as n oo the result is proved under certain mild conditions for instance without assuming normality of the distribution of the components of en

a new nonlinear functional for general scalar conservation laws abstract the approach based on the construction of some nonlinear functionals was proved to be robust in the study of the well posedness theories of hyperbolic conservation laws especially in one space dimensional case in particular a generalized entropy functional was constructed in t p liu t yang a new entropy functional for scalar conservation laws comm pure appl math 52 1999 1427 1442 for the l 1 stability of weak solutions however this generalized functional is so far only defined for scalar equations with convex flux function in this paper we introduce a new nonlinear functional which is motivated by the new glimm functional introduced in j l hua z h jiang t yang a new glimm functional and convergence rate of glimm scheme for general systems of hyperbolic conservation laws preprint for general scalar conservation laws this functional improves the one given in h x liu t yang a nonlinear functional for general scalar hyperbolic conservation laws j differential equations 235 2 2007 658 667 and it can be viewed as a better attempt for the generalized entropy functional for general equations

the product limit estimator and the bootstrap some asymptotic representations the product limit estimator and its quantile process are represented as i i d mean processes with a remainder of ordern 3 4 logn 3 4 a s corresponding bootstrap versions of these representations are given which can help one visualize how the bootstrap procedure operates in this set up

tracking the direction of arrival of multiple moving targets the authors focus on the problem of tracking the direction of arrival doa of multiple moving targets the targets are assumed to be moving with constant accelerations subject to minor random perturbations and emitting narrow band signals that impinge on an array of passive sensors estimates of the doa vector of the targets are obtained from the sensor data based on the maximum likelihood ml principle in such a way that the association between the estimates made at different time points is maintained at each stage the current ml estimates of doa are treated as measurements and refined via a kalman filter tracking is accomplished without the need to perform unduly heavy computations an efficient strategy for dealing with closely spaced targets is also presented finally the performance of the tracking algorithm is illustrated via computer simulations

a new entropy functional for a scalar conservation law in this paper we introduce a new entropy functional for a scalar convex conservation law that generalizes the traditional concept of entropy of the second law of thermodynamics the generalization has two aspects the new entropy functional is defined not for one but for two solutions it is defined in terms of the l1 distance between the two solutions as well as the variations of each separate solution in addition it is decreasing in time even when the solutions contain no shocks and is therefore stronger than the traditional entropy even in the case when one of the solutions is zero 1999 john wiley sons inc

deriving two stage learning sequences from knowledge in fuzzy sequential pattern mining a fuzzy sequential pattern consisting of several fuzzy sets represents a frequently occurring behavior related to time and can be discovered from transaction bases an example is that large purchase amounts of one product were bought by customers after these consumers had bought small purchase amounts of another product recently hu et al 2003 proposed a fuzzy data mining method to discover fuzzy sequential patterns in this method consumers products preferences and consumers product buying orders related to purchase behaviors can be found in the fuzzy sequential pattern mining since for each decision problem there is a competence set consisting of ideas knowledge information and skills for solving that problem we consider knowledge found in fuzzy sequential pattern mining as a needed competence set for solving one decision problem this paper uses a known competence set expansion method the minimum spanning table method to find appropriate two stage learning sequences that can effectively acquire individual fuzzy knowledge sets found in the fuzzy sequential pattern mining a numerical example is used to show the usefulness of the proposed method

on the convexity of higher order jensen differences based on entropy functions corresp in an earlier work the authors introduced a divergence measure called the first order jensen difference or in short cal j divergence which is based on entropy functions of degree alpha this provided a generalization of the measure of mutual information based on shannon s entropy corresponding to alpha 1 it was shown that the first order cal j divergence is a convex function only when a is restricted to some range we define higher order jensen differences and show that they are convex functions only when the underlying entropy function is of degree two a statistical application requiring the convexity of higher order jensen differences is indicated

parametric or nonparametric a parametricness index for model selection in model selection literature two classes of criteria perform well asymptotically in different situations bayesian information criterion bic as a representative is consistent in selection when the true model is finite dimensional parametric scenario akaike s information criterion aic performs well in an asymptotic efficiency when the true model is infinite dimensional nonparametric scenario but there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in in this work we differentiate the two scenarios theoretically under some conditions we develop a measure parametricness index pi to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model which also hints on aic or bic is better suited for the data for the goal of estimating the regression function a consequence is that by switching between aic and bic based on the pi the resulting regression estimator is simultaneously asymptotically efficient for both parametric and nonparametric scenarios in addition we systematically investigate the behaviors of pi in simulation and real data and show its usefulness

some notes on the nonnegative garrote some notes on the use of the nonnegative garrote ng are given in this article the main result is that compared with other penalized least squares methods the ng has a natural selection of penalty function according to an estimator of prediction risk furthermore two natural estimators of the tuning parameter which only involve very simple computations are proposed corresponding to the akaike information criterion aic and bayesian information criterion bic respectively this indicates that to select tuning parameters it may be unnecessary to optimize a model selection criterion repeatedly several reasonable ng estimators with natural tuning parameters are proposed for multicollinearity problems and other problems simulation results and a real data analysis are reported for testing the results obtained previously

statistical data analysis and inference prologue c radhakrishna rao s contributions to statistics y dodge nonlinear estimation in linear models f hampel on the maximum number of factors in two construction methods for orthogonal arrays a s hedayat j stufken some geometrical aspects of data analysis and statistics j m oller inferences and test of hypotheses on bayes and empirical bayes two stage allocation procedures for selection problems s s gupta t c liang multi sample functional statistical data analysis e parzen discussing truth or falsity by computing a q value w schaafsma j tolboom b van der meulen linear pivotals and the bayes non bayes compromise g a barnard bayes modified minimax experimental design r v canfield a simple class of tests locally better than the score test t k chandra comparison of tests in the presence of a nuisance parameter r mukerjee tests for redundancy of some variables in multivariate analysis y fujikoshi model choice in the context of simultaneous inference t havranek o soudsky first zeros of empirical characteristic functions and extreme values of gaussian processes j husler parameter and variance estimation comparison of experiments with weighted distributions m j bayarri m h de groot optimal estimation for weighted distributions semi parametric models v p godambe m b rajarshi on efficiency for quasi likelihood and composite quasi likelihood methods c c heyde improved estimators of dispersion of an inverse gaussian distribution n pal b k sinha applications of edgeworth expansions to bootstrap a review g j babu dual poincare type inequalities via the cramer rao and the cauchy schwarz inequalities and related characterizations th cacoullos some optimality results on stein s two stage sampling j k ghosh r mukerjee the cramer frechet rao inequality for sequential estimation in non regular case z govindarajulu i vincze whither delete k jackknifing for smooth statistical functionals p k sen distributions weighted distributions and characterization three useful expressions for expectations involving a wishart matrix and its inverse g p h styan characterizations of distributions via moments of order statistics a survey and comparison of methods g d lin constancy of regression of a polynomial of sample average of positive random variables on their ratios characterizes gamma distribution a kagan probing encountered data meta analysis and weighted distribution methods g p patil c taillie further results on identification when the parameters are partially unknown s el khattabi f streit linear models and matrices weighted least squares estimation in the general gauss markov model j k baksalary s puntanen evaluating pre test predictors of success in linear regression models a cohen h b sackrowitz optimum invariant tests in mixed linear models with two variance components th mathew

an order clique based approach for mining maximal co locations most algorithms for mining spatial co locations adopt an apriori like approach to generate size k prevalence co locations after size k 1 prevalence co locations however generating and storing the co locations and table instances is costly a novel order clique based approach for mining maximal co locations is proposed in this paper the efficiency of the approach is achieved by two techniques 1 the spatial neighbor relationships and the size 2 prevalence co locations are compressed into extended prefix tree structures which allows the order clique based approach to mine candidate maximal co locations and co location instances and 2 the co location instances do not need to be stored after computing some characteristics of the corresponding co location which significantly reduces the execution time and space required for mining maximal co locations the performance study shows that the new method is efficient for mining both long and short co location patterns and is faster than some other methods in particular the join based method and the join less method

normalizing transformations of some statistics in multivariate analysis summary a general procedure for finding normalizing transformations is applied to various statistics in multivariate analysis it is shown that fisher s z transformation for a sample correlation coefficient in a normal sample and wilson hilferty s approximation for a chi squared variate can be derived by the same line of approach

on the convexity of some divergence measures based on entropy functions three measures of divergence between vectors in a convex set of a n dimensional real vector space are defined in terms of certain types of entropy functions and their convexity property is studied among other results a classification of the entropies of degree alpha is obtained by the convexity of these measures these results have applications in information theory and biological studies

asymptotic properties of criteria for selection of variables in multiple regression in normal linear regression analysis many model selection rules proposed from various viewpoints are available for the information criteria aic fpe c p pss and bic the asymptotic distribution of the selected model and the asymptotic quadratic risk based on each criterion are explicitly obtained

edgeworth expansion of a function of sample means many important statistics can be written as functions of sample means of vector variables a fundamental contribution to the edgeworth expansion for functions of sample means was made by bhattacharya and ghosh in their work the crucial cramer c condition is assumed on the joint distribution of all the components of the vector variable however in many practical situations only one or a few of the components satisfy conditionally this condition while the rest do not such a case is referred to as satisfying the partial cramer c condition the purpose of this paper is to establish edgeworth expansions for functions of sample means when only the partial cramer c condition is satisfied

mining maximal hyperclique pattern a hybrid search strategy a hyperclique pattern is a new type of association pattern that contains items which are highly affiliated with each other specifically the presence of an item in one transaction strongly implies the presence of every other item that belongs to the same hyperclique pattern in this paper we present an algorithm for mining maximal hyperclique patterns which specifies a more compact representation of hyperclique patterns and are desirable for many applications such as pattern based clustering our algorithm exploits key advantages of both the depth first search dfs strategy and the breadth first search bfs strategy indeed we adapt the equivalence pruning method one of the most efficient pruning methods of the dfs strategy into the process of the bfs strategy our experimental results show that the performance of our algorithm can be orders of magnitude faster than standard maximal frequent pattern mining algorithms particularly at low levels of support

the intraclass correlation coefficient distribution free definition and test a definition of the intraclass correlation coefficient is given on the basis of a general class of random effect model the conventional intraclass correlation coefficient and the intracluster correlation coefficient for binary data are both particular cases of the generalized coefficient we derive the score test for the hypothesis of null intraclass correlation in the exponential family the statistic does not depend on the particular distribution in this family and is related to the pairwise correlation coefficient the test can be adjusted for explanatory variables