bluuebunny/crossref_metadata_2025_split huggingface dataset:
ESTIMATING THE ROOT OF A NONPARAMETRIC REGRESSION FUNCTION IN A ROBUST FASHION
Authors: He Xuming 
Published: February 2008

SummaryFor a nonparametric regression model y = m(x)+e with n independent observations, we analyze a robust method of finding the root of m(x) based on an M‐estimation first discussed by Härdle & Gasser (1984). It is shown here that the robustness properties (minimaxity and breakdown function) of such an estimate are quite analogous to those of an M ‐estimator in the simple location model, but the rate of convergence is somewhat limited due to the nonparametric nature of the problem.
A CONDITIONAL DERIVATION OF RESIDUAL MAXIMUM LIKELIHOOD
Authors: Verbyla A.P. 
Published: February 2008

SummaryPatterson & Thompson (1971) introduced residual maximum likelihood estimation in the case of unbalanced incomplete block designs. Harville (1974) and Cooper & Thompson (1977) give alternative derivations of the likelihood function. The purpose of this note is to provide another derivation of the likelihood function which may be useful in teaching.
ESTIMATION OF REALIZED SIGNAL TO NOISE RATIO FOR A PAIR OF MULTIVARIATE SIGNALS
Authors: Khattree Ravindra , Gupta Rameshwar D. 
Published: February 2008

SummaryConsider the case of classifying an incoming message as one of two known p‐dimension signals or as a pure noise. Let the noise co‐variance matrix (assumed to be same in all the three cases) be unknown. We consider the problem of estimation of “realized signal to noise ratio matrix”, which is an index of discriminatory power, under various loss functions. Optimum estimators are obtained under these loss functions. Finally, an attempt is made to provide a lower confidence bound for the realized signal to noise ratio matrix. In the process, the probability distribution of the smaller eigenvalue of a 2 × 2 confluent hypergeometric random matrix is obtained.
CORRECTING FOR MEASUREMENT ERROR IN AN EXPOSURE‐RESPONSE RELATIONSHIP BASED ON DICHOTOMISING A CONTINUOUS DEPENDENT VARIABLE
Authors: Irwig Les M. , Groeneveld Hennie T. , Simpson Judy M. 
Published: February 2008

SummaryRandom error in a continuous outcome variable does not affect its regression on a predictor. However, when a continuous outcome variable is dichotomised, random measurement error results in a flatter exposure‐response relationship with a higher intercept. Although this consequence is similar to the effect of misclassification in a binary outcome variable, it cannot be corrected using techniques appropriate for binary data. Conditional distributions of the measurements of the continuous outcome variable can be corrected if the reliability coefficient of the measurements can be estimated. An unbiased estimate of the exposure‐response relationship is then easily calculated. This procedure is demonstrated using data on the relationship between smoking and the development of airway obstruction.
ON INCOMPLETE BLOCK DESIGNS FOR SYMMETRIC PARALLEL LINE ASSAYS
Authors: Gupta Sudhir , Mukerjee Rahul 
Published: February 2008

SummaryIncomplete block designs for symmetric parallel line assays, which estimate Lp, L1and L1/1 with full efficiency, are constructed in a unified manner. Necessary and sufficient conditions are given for existence. A complete solution is presented when the number of doses is even. A general systematic method of construction is given when the number of doses is odd, and all possible designs for up to 15 doses of each preparation are tabulated.
IS THE ASSUMPTION OF UNIFORM INTRA‐CLASS CORRELATION EVER NEEDED?
Authors: Brewer K.R.W. , Tam S.M. 
Published: February 2008

SummaryWhen modelling a finite population it is sometimes assumed that the residuals from the regression model expectations are distributed with a uniform non‐zero intra‐class correlation. It is shown that if a certain vector is spanned by the columns of the design matrix (in the homoskedastic case this vector corresponds to the inclusion of a constant term) then such a model is underidentified and the assumption of a known non‐zero correlation has almost no impact on the results of the regression analysis. When this vector is not spanned by the columns of the design matrix, a simpler alternative model can usually be fitted equally well to observations from any single population. The only exception occurs when the the intra‐class correlation required is negative in sign.
ON THE ANALYSIS AND APPLICATION OF MEASURES OF LINKAGE DISEQUILIBRIUM
Authors: Lo Sing Kai 
Published: February 2008

SummaryThe maximum likelihood, jackknife and bootstrap estimators of linkage disequilibrium, a measure of association in population genetics, are derived and compared. It is found that for point estimation, the resampling methods generate almost identical mean square errors. The maximum likelihood estimator could have bigger or smaller mean square errors depending on the parameters of the underlying population. However the bootstrap confidence interval is superior to the other two as the length of the intervals is shorter or the probability that the 95% confidence intervals include the true parameter is closer to 0.95. Although the standardised measure of linkage disequilibrium has a range from ‐1 to 1 regardless of marginal frequencies, it is shown that the distribution of this standardised measure is still not allele frequency independent under the multinomial sampling scheme.
ROBUST RIDGE REGRESSION BASED ON AN M‐ESTIMATOR
Authors: SILVAPULLE MERVYN J. 
Published: February 2008

SummaryConsider the linear regression model y=β01 +Xβ+ in the usual notation. It is argued that the class of ordinary ridge estimators obtained by shrinking the least squares estimator by the matrix (X1X + kI)‐1X'X is sensitive to outliers in the ^variable. To overcome this problem, we propose a new class of ridge‐type M‐estimators, obtained by shrinking an M‐estimator (instead of the least squares estimator) by the same matrix. Since the optimal value of the ridge parameter k is unknown, we suggest a procedure for choosing it adaptively. In a reasonably large scale simulation study with a particular M‐estimator, we found that if the conditions are such that the M‐estimator is more efficient than the least squares estimator then the corresponding ridge‐type M‐estimator proposed here is better, in terms of a Mean Squared Error criteria, than the ordinary ridge estimator with k chosen suitably. An example illustrates that the estimators proposed here are less sensitive to outliers in the y‐variable than ordinary ridge estimators.
THE GROUPING PROBLEM IN DISTRIBUTION‐FREE PLANAR REGRESSION
Authors: Brown B.M. , Pella M.J. 
Published: February 2008

SummaryIn the planar regression model having two slope parameters and identically distributed errors, exact distribution‐free inference about one parameter may be carried out by grouping the observations, eliminating the nuisance parameter and reducing the model to simple linear regression, allowing exact distribution‐free methods for slope to be employed. This model reduction involves a loss of efficiency: the choice of an optimal grouping to minimize efficiency loss is discussed.
EXPERIMENTAL SEARCH FOR SEXUAL ISOLATION IN FLIES
Authors: Lewgoy Flavio , Stark Alan E. 
Published: February 2008

SummaryIncipient sexual isolation between two strains of D. metanogaster was tested by male choice experiments. During ten years (150 generations) one strain, named Se, was reared in a food medium containing 10‐‐4 M sodium selenite, while the other, named Co, was kept under normal conditions. Both strains were derived from the same natural population. There was no evidence of preference for a mate of the same strain. There was an interaction between type of female and duration of time available for mating. Se males inseminated fewer females and fewer Se females were inseminated overall.
ESTIMATION OF A DISTRIBUTION FUNCTION DOMINATING STOCHASTICALLY A KNOWN DISTRIBUTION FUNCTION
Authors: Puri Prem S. , Singh Harshinder 
Published: February 2008

SummaryThis paper considers the problem of estimating a cumulative distribution function (cdf), when it is known a priori to dominate a known cdf. The estimator considered is obtained by adjusting the empirical cdf using the prior information. This adjusted estimator is shown to be consistent, its limiting distribution is found, and its mean squared error (MSE) is shown to be smaller than the MSE of the empirical cdf. Its asymptotic efficiency (compared to the empirical cdf) is also found.
UNBIASED ESTIMATION OF PARAMETRE FUNCTIONS IN SAMPLING FROM ONE‐ AND TWO‐TRUNCATION PARAMETER FAMILIES
Authors: Patel S.R. , Bhatt M.B. 
Published: February 2008

SummaryWe consider the problem of uniformly minimum variance unbiased (UMVU) estimation of U‐estimable functions of three unknown truncation parameters based on two independent random samples: one from a two‐truncation parameter family and the other from a one‐truncation parameter family. In particular, we obtain the UMVU estimator of the functional Pr{Y > X} and the shortest confidence intervals for some parametric functions.
ON THE FOURTH ROOT TRANSFORMATION OF CHI‐SQUARE
Authors: Goria M.N. 
Published: February 2008

SummaryWe show that, within the family of power transformations of a Chisquare variable, the square and fourth roots minimize Pearson's index of kurtosis. Two new transtormations of the fourth root, a symmetrized‐truncated version and its linear combination with the square root are also studied. The first transformation shows a considerable improvement over the fourth root while the second one turns out to be even more accurate than Hilferty‐Wilson's cube root transformation.
CONVERSATIONS WITH EDWIN J.G. PITMAN
Authors: Brown Bruce M. , Williams Evan J. 
Published: February 2008

Edwin Pitman was born on 29th October, 1897, in Melbourne, Victoria. He was Professor of Mathematics at the University of Tasmania from 1926 until his retirement in 1962. His publications on distribution‐free methods in Statistics established a sound basis for the development of these methods. His theoretical papers gave a sound mathematical treatment of the concepts of intrinsic accuracy and sufficiency, and a definitive treatment of estimation problems for location and scale parameters. The concept of asymptotic relative efficiency is due to him. He was elected a Fellow of the Australian Academy of Science (FAA) in 1954, a Fellow of the Institute of Mathematical Statistics in 1948, President of the Australian Mathematical Society in 1958 and 1959, and Vice‐President of the International Statistical Institute in 1960. He was elected an Honorary Life Member of the Statistical Society of Australia in 1966, an Honorary Fellow of the Royal Statistical Society in 1965, and an Honorary Life Member of the Australian Mathematical Society in 1968.In 1977, in honour of his eightieth birthday, the University of Tasmania conferred on him an Honorary Doctorate of Science, and the Statistical Society of Australia instituted the Pitman Medal (1979), to be awarded to a member of the Society for high distinction in Statistics.The conversations recorded here took place in May, 1991, at Pitman's home in Hobart, Tasmania.
ASYMPTOTIC MINIMAX PROPERTIES OF L‐ESTIMATORS OF SCALE
Authors: Wu Eden K.H. , Leung Denis H.Y. 
Published: February 2008

SummaryThis paper asks whether or not the efficient L‐estimator of scale corresponding to the least informative distribution in ε‐contamination and Kol‐mogorov neighbourhoods of certain distributions possesses the saddlepoint property. This is of interest since the saddlepoint property implies the mini‐max property, namely, that the supremum of the relative asymptotic variance of an L‐estimator is minimized by the efficient estimator corresponding to that member of the distributional class with minimum Fisher information for scale. Our findings are negative in all cases investigated.
TESTING SPHERICITY UNDER A MIXTURE MODEL
Authors: Amey Alphonse K.A. , Gupta A.K. 
Published: February 2008

SummaryThe robustness of Mauchly's sphericity test criterion when sampling from a mixture of two multivariate normal distributions is studied. The distribution of the sphericity test criterion when the sample covariance matrix has a non‐central Wishart density of rank one is derived in terms of Meijer's G‐functions; its distribution under the mixture model is then deduced. The robustness is studied by computing actual significance levels of the test under the mixture model using the critical values under the usual normal model.
ESTIMATION OF SPATIAL ARMA MODELS
Authors: Huang D. , Anh V.V. 
Published: February 2008

SummarySpatial ARMA models are considered using the nonsymmetric half plane ordering on a lattice of data. A method is given for the estimation of the orders and the coefficients of such models under an identifiability condition and the condition that the beat linear predictor is the best predictor in the mean square sense. Under these conditions, the strong consistency of the estimators ia established. The usual methods for ARMA modelling in Time Series Analysis require estimation of the innovations. The method of this paper introduces an inveree model complementary to the original model so that the estimation of the innovations is avoided. This leads to a substantial reduction in the computational complexity in the two‐dimensional case.
AN EXACT FORMULA FOR THE SYMMETRICAL INCOMPLETE BETA FUNCTION WHERE THE PARAMETER IS AN INTEGER OR HALF‐INTEGER
Authors: Saunders L.R. 
Published: February 2008

SummaryA finite series for computing the symmetrical incomplete beta function with parameter q= 0.5, 1, 1.5, is derived. It is more efficient than existing algorithms for the general case, and obviates reference to the Pearson‐Johnson tables and subsequent interpolation. This is advantageous in applications to statistical quality assurance, especially in computer simulations of acceptance schemes.
TESTING HYPOTHESES ABOUT THE COMMON MEAN OF TWO NORMAL POPULATIONS
Authors: Weerakkody Govinda J. 
Published: February 2008

SummaryThree tests are considered concerning the common mean of two normal populations: (1) an F test based on a sample from one population, (2) a test based on the addition of the F statistics from independent samples from two popultions (proposed), and (3) a test based on the maximum of the F statistics from two independent samples from two populations. A condition under which test (2) is locally more powerful than test (1) is given. As the test statistic in test (2) does not follow a standard distribution, a formula for approximating the observed significance level is provided. A simulation study is used to compare the power of these tests.
ASYMPTOTIC INFERENCE FOR A CLASS OF CHAIN BINOMIAL MODELS
Authors: Huggins R.M. 
Published: February 2008

SummaryChain binomial models axe commonly used to model the spread of an epidemic through a population. This paper shows that for a flexible class of chain binomial models an approximate maximum likelihood estimator of the infection rate, derived from a Poisson approximation to the binomial distribution, has an asymptotically normal distribution, as do some other related'estimators.
THE OPTIMAL CLASSIFICATION RULE FOR EXPONENTIAL POPULATIONS
Authors: Adegboye O. Sola 
Published: February 2008

SummaryLet π1 and π2 be two exponential populations with location parameters α and β respectively. It is shown that the probability of misclassification for the optimal rule, RE, depends on α and β only through their ratio α/β=r, and that the smaller the ratio r (i.e. the smaller α is compared to β), the greater the superiority of the optimal rule RE over the commonly used rule RN. When α and β are unknown, the sample‐based version RE(s) of RE exhibits the same pattern of superiority over the sample‐based RN(s) of RN.
CANONICAL VARIATE ANALYSIS WITH SPATIALLY‐CORRELATED DATA
Authors: Campbell N.A. , Kiiveri H.T. 
Published: February 2008

SummaryA general canonical variate model is derived when the observations are spatially correlated. For spatial covariance structures resulting from dependence of a pixel on its nearest neighbours, the solution reduces to an analysis of neighbour‐corrected values. The usual analysis, in which spatial correlation is ignored, gives similar canonical vectors but over‐estimates the canonical roots. A formula for approximating the reduction in the canonical roots to adjust for the spatial correlation is given.
RANDOMIZED SEARCH STRATEGIES FOR FINDING OPTIMAL OR NEAR OPTIMAL BLOCK AND ROW‐COLUMN DESIGNS
Authors: Venables W.N. , Eccleston J.A. 
Published: February 2008

SummaryWe consider a family of effective and efficient strategies for generating experimental designs of several types with high efficiency. These strategies employ randomized search directions and at some stages allow the possibility of taking steps in a direction of decreasing efficiency in an effort to avoid local optima. Hence our strategies have some affinity with the simulated annealing algorithm of combinatorial optimization. The methods work well and compare favourably with other search strategies. We have implemented them for incomplete block designs, optionally resolvable, and for row‐column designs.
A CUSUM SCHEME BASED ON THE EXPONENTIAL DISTRIBUTION FOR SURVEDLLANCE OF RARE CONGENITAL MALFORMATIONS
Authors: MATHERS CD. , HARRIS R.S. , LANCASTER P.A.L. 
Published: February 2008

SummaryA method of monitoring the incidence of malformations is described. It is suitable for systems where the number of births between successive malformations is known or can be estimated with reasonable accuracy. The method utilises a cusum technique based on the exponential distribution to detect an increase in the incidence of malformations above a baseline level. Adequate information to enable the implementation of the method is presented. The proposed method compares favourably with others such as the Poisson cusum and the modified sets technique.
EMPIRICAL EVIDENCE FOR ADAPTIVE CONFIDENCE INTERVALS AND IDENTIFICATION OF OUTLIERS USING METHODS OF TRIMMING
Authors: CLARKE BRENTON R. 
Published: February 2008

SummaryIn an attempt to apply robust procedures, conventional t‐tables are used to approximate critical values of a Studentized t‐statistic which is formed from the ratio of a trimmed mean to the square root of a suitably normed Winsorized sum of squared deviations. It is shown here that the approximation is poor if the proportion of trimming is chosen to depend on the data. Instead a data dependent alternative is given which uses adaptive trimming proportions and confidence intervals based on trimmed likelihood statistics. Resulting statistics have high efficiency at the normal model, proper coverage for confidence intervals, yet retain breakdown point one half. Average lengths of confidence intervals are competitive with those of recent Studentized confidence intervals based on the biweight over a range of underlying distributions. In addition, the adaptive trimming is used to identify potential outliers. Evidence in the form of simulations and data analysis support the new adaptive trimming approach.
ROBUST M‐ESTIMATION OF THE INTRACLASS CORRELATION COEFFICIENT
Authors: Bansal Naveen K. , Bhandary M. 
Published: February 2008

SummaryRobust M‐estimators of intraclass correlation coefficient, location and scale parameters are defined for familial data. It is shown that these estimators are strongly consistent. Also the asymptotic distributions of these estimators are derived when the underlying distribution is elliptically and permutationally symmetric.
THE SAMPLING PERFORMANCE OF INEQUALITY RESTRICTED AND PRE‐TEST ESTIMATORS IN A MIS‐SPECIFIED LINEAR MODEL
Authors: Wan Alan T.K. 
Published: February 2008

SummaryThe literature on the sampling properties of the inequality restricted and pre‐test estimators typically assumes a properly specified model and focuses on the estimation of the regression coefficient vector. In this paper, we derive and evaluate the risk functions of these estimators for both the prediction vector and the disturbance variance in a model which is mis‐specified through the exclusion of relevant regressors. The results suggest that unrestricted estimation is generally preferable to pre‐testing or naively imposing restrictions.
AN ALGORITHM FOR THE CONSTRUCTION OF OPTIMAL OR NEAR‐OPTIMAL CHANGE‐OVER DESIGNS
Authors: Eccleston John A. , Street Deborah J. 
Published: February 2008

SummaryThis paper presents an algorithm for the construction of optimal or near optimal change‐over designs for arbitrary numbers of treatments, periods and units. Previous research on optimality has been either theoretical or has resulted in limited tabulations of small optimal designs. The algorithm consists of a number of steps:first find an optimal direct treatment effects design, ignoring residual effects, and then optimise this class of designs with respect to residual effects. Poor designs are avoided by judicious application of the (M, S)‐optimality criterion, and modifications of it, to appropriate matrices. The performance of the algorithm is illustrated by examples.
LOCATION OF PARTICULAR POINTS IN NON‐PARAMETRIC REGRESSION ANALYSIS
Authors: Boularan Joel , Ferre Louis , Vieu Philippe 
Published: February 2008

SummaryIn the random‐design non‐parametric regression model, the locations of particular values of the regression function or its derivatives are estimated. This paper investigates several stochastic modes of convergence and finds their rate of convergence under regularity assumptions, for a wide class of non‐parametric estimators. The approach finds two natural fields of application: estimation of zeros/extrema and non‐parametric absolute calibration.
LOG‐LINEAR MODELS FOR MEAN AND DISPERSION IN MIXED POISSON REGRESSION MODELS
Authors: Ven R. Van de , Weber N.C. 
Published: February 2008

SummaryThis paper is concerned with the analysis of repeated measures count data overdispersed relative to a Poisson distribution, with the overdispersion possibly heterogeneous. To accommodate the overdispersion, the Poisson random variable is compounded with a gamma random variable, and both the mean of the Poisson and the variance of the gamma are modelled using log linear models. Maximum likelihood estimates (MLE) are then obtained. The paper also gives extended quasi‐likelihood estimates for a more general class of compounding distributions which are shown to be approximations to the MLEs obtained for the gamma case. The theory is illustrated by modelling the determination of asbestos fibre intensity on membrane filters mounted on microscope slides.
COMPARISON OF QUASI‐LIKELIHOOD MODELS FOR OVERDISPERSION
Authors: III N. David Yanez , Wilson Jeffrey R. 
Published: February 2008

SummaryMethods for modelling overdispersed data are compared. These methods are considered to be of two kinds: a likelihood based approach and a method‐of‐moments based approach. The likelihood method facilitates computation of maximum likelihood estimates which can be obtained through the same algorithm as that of weighted least squares. The quasi‐likelihood or moment approaches seem to be appropriate when severe overdispersion may be present. The comparisons are made via analyses of the Ames Salmonella Reverse Mutagenicity Assay (Margolin et a/., 1981) and a seed dataset (Crow‐der, 1978).
QUALITY CONTROL METHODS IN LARGE‐SCALE ASSESSMENT PROCEDURES USING ‘DOUBLE‐MARKING’ OR ‘PARTIAL DOUBLE‐MARKING’
Authors: Sparks Ross , Ballantyne Roy 
Published: February 2008

SummaryEnsuring a standard of assessment in situations where marking panels are used is fraught with difficulties, particularly where essay‐type responses are to be marked. This paper discusses statistical process control procedures, similar to those used in industry, to help moderate marking quality when ‘double‐marking’ or ‘partial double‐marking’ are used. When questions are assessed by the same two markers, the scores assigned to responses by each marker may be adjusted so that their assessments are on average equal in terms of location and scale. The paper also discusses methods of controlling sequential assessment, and demonstrates the application of these techniques in evaluating marker consistency, using data from school leaving examinations in geography.