bluuebunny/crossref_metadata_2025_split huggingface dataset:
POINT PROCESSES WITH A CLUSTERING TRANSITION
Authors: And David Gates , Westcott Mark 
Published: February 2008

summaryThe paper examines the properties of a simple pairwise interaction point process or distribution, when the potential is attractive. This leads to various degrees of clustering. Using measures such as the normalizing constant, number of close pairs and a generalized scan statistic, it is found that there are effectively three regimes for the model, determined by the relative magnitudes of numbers of pointsN, the point domainVand strength of interactionv.AsN vincreases, abrupt transitions between these regimes occur with the middle “critical” one holding for only a relatively small range ofN v‐values. It is possible to simulate the process reliably in one dimension, unlike the planar version, and confirmatory results are reported. A discrete version of the model, more susceptible to analysis, is introduced; it leads to the same conclusions and exhibits a criticality resembling, but distinct from, that in processes with phase transitions. Possible application of the discrete model to urbanization is described.
ON THE VARIANCE PROPERTIES OF STRESS RELEASE MODELS
Authors: Vere‐Jones D. 
Published: February 2008

summaryA class of piece‐wise linear stochastic processes is described, which can be used to model the gradual accumulation and sudden release of stress in earthquake models. Interest centres on the variance properties of the sequence of time points at which the sudden release (earthquakes) occur. Some general conditions are derived for the existence of moments, and under these conditions the variance properties are examined. It is shown that the point process of such events can have a bounded variance function, as is known to happen in the case of constant stress releases, only in this degenerate case. In the final section an example is examined which is closely related to the classical collective risk model.
THE ROLE OF CONDITIONAL INFERENCE
Authors: Becker Niels 
Published: February 2008

summaryThe fact that unconditional sampling theoretic inference is sometimes misleading is illustrated with some examples. Each of these examples involves a sequential sampling procedure and it is suggested here that meaningful inferences can be obtained in these examples by viewing the sequential components of the experiment as though they are separate and independent experiments. Inferences are then made with reference to hypothetical repetitions of the components of the current experiment, rather than referring to hypothetical repetitions of the experiment as a whole. In essence this involves combining the conditional inferences from the components as though they are independent. The approach involves the same likelihood and asymptotic likelihood inference remains essentially the same. However, the finite sampling properties of estimators and tests are changed. A by‐product of the use of this alternative reference set is that it resolves problems arising from the arbitrariness of stopping rules. This approach to sampling theoretic inference is not merely a device for overcoming difficulties arising in a few examples, it has relevance in all problems of statistical inference.
EFFECTS OF ERRORS IN INSPECTION ON A BINARY METHOD FOR ISOLATING NONCONFORMING ITEMS
Authors: Johnson Norman L. , Kotz Samuel 
Published: February 2008

summaryThe effects of inspection errors on the well‐known method of “successive halving” to determine nonconforming items is studied. The method is known to be near‐optimal when no more than one nonconforming item is present. We also study the properties of the procedure when there are two or more nonconforming items among those to be tested.
PARAMETER ESTIMATION AND APPLICATIONS FOR A GENERALISATION OF THE BETA‐BINOMIAL DISTRIBUTION
Authors: Danaher P.J. 
Published: February 2008

SummaryA three‐parameter generalisation of the beta‐binomial distribution (BBD) derived by Chandon (1976) is examined. We obtain the maximum likelihood estimates of the parameters and give the elements of the information matrix. To exhibit the applicability of the generalised distribution we show how it gives an improved fit over the BBD for magazine exposure and consumer purchasing data. Finally we derive an empirical Bayes estimate of a binomial proportion based on the generalised beta distribution used in this study.
THE RELATIONSHIPS BETWEEN SKEWNESS AND KURTOSIS
Authors: MacGillivray H.L. , Balanda K.P. 
Published: February 2008

SummaryTheoretical considerations of kurtosis, whether of partial orderings of distributions with respect to kurtosis or of measures of kurtosis, have tended to focus only on symmetric distributions. With reference to historical points and recent work on skewness and kurtosis, this paper defines anti‐skewness and uses it as a tool to discuss the concept of kurtosis in asymmetric univariate distributions. The discussion indicates that while kurtosis is best considered as a property of symmetrised versions of distributions, symmetrisation does not simply remove skewness. Skewness, anti‐skewness and kurtosis are all inter‐related aspects of shape. The Tukey g and h family and the Johnson Su family are considered as examples.
BOOTSTRAPPED MULTI‐DIMENSIONAL PRODUCT LIMIT PROCESS
Authors: Yandell Brian S. , Horváth Lajos 
Published: February 2008

SummaryWe approximate the limit process for a multivariate censored survival distribution using the bootstrap. The empirical process has a complicated covariance structure depending on the survival and censoring. The bootstrapped process provides a means to develop distribution‐free procedures including simultaneous confidence bands. Results extend to comparison of multivariate survival distributions. A gallstone study is examined in some detail.
RATIO‐TO‐SIZE ESTIMATORS OF MEAN PER SUBUNIT IN TWO‐STAGE SAMPLING OVER TWO OCCASIONS
Authors: Okafor Fabian C. 
Published: February 2008

SummaryWe propose here some strategies for estimating the population mean per subunit at the current occasion and change in mean from one occasion to the next based on two‐stage sampling on two successive occasions.Estimators of the mean in two‐stage sampling over successive occasions have so far been based on the knowledge of the total number of subunits (elements),M0in the population or on assumed equal sizes for the primary units. We have therefore given the ratio‐to‐size estimators for the population mean per subunit on the current occasion and the change in mean over the two occasions whereM0is not known. The results obtained also apply to the situation whereM1is correlated with the variable of interesty.
SOME MODELS FOR OVERDISPERSED BINOMIAL DATA
Authors: Anderson Dorothy A. 
Published: February 2008

SummaryVarious models are currently used to model overdispersed binomial data. It is not always clear which model is appropriate for a given situation. Here we examine the assumptions and discuss the problems and pitfalls of some of these models. We focus on clustered data with one level of nesting, briefly touching on more complex strata and longitudinal data. The estimation procedures are illustrated and some critical comments are made about the various models. We indicate which models are restrictive and how and which can be extended to model more complex situations. In addition some inadequacies in testing procedures are noted. Recommendations as to which models should be used, and when, are made.
MAXIMUM LIKELIHOOD ESTIMATION OF GROSS FLOWS
Authors: Chambers R.L. , Woyzbun L. , Pillig R. 
Published: February 2008

SummaryA method of maximum likelihood estimation of gross flows from overlapping stratified sample data is developed. The approach taken is model‐based and the EM algorithm is used to solve the estimation problem. Inference is thus based on information from the total sample at each time period. This can be contrasted with the conventional approach to gross flows estimation which only uses information from the overlapping sub‐sample. An application to estimation of flows of Australian cropping and livestock industries farms into and out of an “at risk” situation over the period 1979–84 is presented, as well as a discussion of extensions to more complex sampling situations.
ON THE HILDRETH‐HOUCK ESTIMATOR FOR RANDOM COEFFICIENT REGRESSION MODELS
Authors: Anh V. V. 
Published: February 2008

SummaryThis paper is concerned with the linear regression model in which the coefficients are random variables. The Hildreth‐Houck method is considered for estimating the model. Sufficient conditions for the consistency of the Hildreth‐Houck estimator are discussed and its asymptotic normality is established.
ANALYSIS OF VARIATIONS IN AN INFECTION RATE
Authors: Becker Niels , Yip Paul 
Published: February 2008

summaryIn a previous analysis of data from a smallpox epidemic in Abakaliki, Nigeria, it was found that the epidemic model based on uniform mixing was able to give an adequate description only when the infection rate was taken to be a function of time. Here we demonstrate that an alternative, and plausible, explanation is that the infection rate acting upon each given susceptible is constant over time, but that it varies between susceptibles. This variation in susceptibility of individuals can give the impression that the infection rate is declining over time, because highly susceptible individuals tend to be infected earlier so that the mean susceptibility of the remaining susceptibles is declining over time.The above example reveals that there is interest in exploring the nature of the dependence of the infection rate upon time. To this end we propose the nonparametric estimation of the infection rate and illustrate it with reference to the Abakaliki data.
COVARIANCE MODELS FOR LATTICE DATA
Authors: Kiivefu H. T. , Campbell N. A. 
Published: February 2008

summaryGiven observations on an m × n lattice, approximate maximum likelihood estimates are derived for a family of models including direct covariance, spatial moving average, conditional autoregressive and simultaneous autoregressive models. The approach involves expressing the (approximate) covariance matrix of the observed variables in terms of a linear combination of neighbour relationship matrices, raised to a power. The structure is such that the eigenvectors of the covariance matrix are independent of the parameters of interest. This result leads to a simple Fisher scoring type algorithm for estimating the parameters. The ideas are illustrated by fitting models to some remotely sensed data.
PARTIALLY EQUINEIGHBOURED DESIGNS
Authors: Ipinyomi R. A. 
Published: February 2008

summaryThis paper presents further results on a class of designs called equineighboured designs, ED. These designs are intended for field and related experiments, especially whenever there is evidence that observations in the same block are correlated.An ED has the property that every unordered pair of treatments occurs as nearest neighbours equally frequently at each level. Ipinyomi (1986) has defined and shown that ED are balanced designs when neighbouring observations are correlated. He has also presented ED as a continuation of the development of optimal block designs.An ED would often require many times the number of experimental materials needed for the construction of an ordinary balanced incomplete block, BIB, design for the same number of treatments and block sizes. Thus for a relatively large number of treatments and block sizes the required minimum number of blocks may be excessively large for practical use of ED.In this paper we shall define and examine partially equineighboured designs with n concurrences, PED (n), as alternatives where ED are practically unachievable. Particular attention will be given to designs with smaller numbers of blocks and for which only as little balance as possible may be lost.
THE SOJOURN TIME DISTRIBUTION OF AN ALTERNATING RENEWAL PROCESS
Authors: Rossiter M.H. 
Published: February 2008

summaryOrdinary, modified, and equilibrium alternating renewal processes are defined in such a way that the distribution of the sojourn time in one state of a two‐state system may be considered conditional on the starting state. The double Laplace transform of a non‐negative stochastic process is also defined, and used to express a result of Takacs (1957) on sojourn time distributions.
ON SPLINE SMOOTHING WITH AUTOCORRELATED ERRORS
Authors: Diggle Peter J. , Hutchinson Michael F. 
Published: February 2008

summaryThe generalised cross‐validation criterion for choosing the degree of smoothing in spline regression is extended to accommodate an autocorrelated error sequence. It is demonstrated via simulation that the minimum generalised cross‐validation smoothing spline is an inconsistent estimator in the presence of autocorrelated errors and that ignoring even moderate autocorrelation structure can seriously affect the performance of the cross‐validated smoothing spline. The method of penalised maximum likelihood is used to develop an efficient algorithm for the case in which the autocorrelation decays exponentially. An application of the method to a published data‐set is described. The method does not require the data to be equally spaced in time.
A SELECTED BIBLIOGRAPHY ON THE ANALYSIS OF REPEATED MEASUREMENTSandRELATED AREAS
Authors: Diggle Peter J. , Donnelly John B. 
Published: February 2008

summaryDifferent approaches to the analysis of repeated measurements are outlined. For each approach, a few key references are given as a possible basis for further reading. Related methodological areas are mentioned, again accompanied by key references in each case.
LIMIT THEOREMS FOR STANDARDIZED PARTIAL SUMS OF WEAKLY EXCHANGEABLE ARRAYS
Authors: Robinson J. 
Published: February 2008

summaryA symmetric array of random variables is weakly exchangeable if, when the same arbitrary permutation is applied to rows and columns, the joint distribution remains the same. We consider the asymptotic distribution of the standardized sums of the elements of the upper left hand corner of the partitioned array, generalizing results on U‐statistics. In general, the asymptotic distribution is normal, but if the array is first standardized by subtracting row and column means, then it is a linear form in a normal variable and independent squares of normal variables with coefficients depending on the limits of the eigenvalues of the array.
A Discrete Time Parametric Model for the Analysis of Failure Time Data
Authors: Adams Geoff , Watson Ray 
Published: February 2008

SummaryThis paper introduces a parametric discrete failure time model which allows a variety of smooth hazard function shapes, including shapes which are not readily available with continuous failure time models. The model is easy to fit, and statistical inference is simple. Further, it is readily extended to allow for differences between subjects while retaining the ease of fit and simplicity of statistical inference. The performance of the discrete time analysis is demonstrated by application to several data sets.
SMALL SAMPLE VARIANCE ESTIMATORS FOR U‐STATISTICS
Authors: Schucany William R. , Bankson Daniel M. 
Published: February 2008

SummaryNew unbiased estimators are presented for the dominant and lower‐order terms of the variance expansion for U‐statistics. In small samples these provide important corrections to the usual estimate of asymptotic standard error which is based on the leading term in the expansion. The new estimators for the first term cannot be recommended. The ordinary jackknife estimator is found to be more effective than the direct estimates of the separate terms.
QUANTILES OF SUMS AND EXPECTED VALUES OF ORDERED SUMS
Authors: Liu Jingyu , David H.A. 
Published: February 2008

SummaryWatson & Gordon (1986) investigated the relationship between the quantiles of a sum of independent continuous random variables and the sum of the individual quantiles. In this note some further results are obtained. Also corresponding relationships are developed for the expected values of the order statistics of a sum, and for the sum of the expected values of the individual order statistics.
TEST FOR TREATMENT EFFECT BASED ON BINARY DATA WITH RANDOM SAMPLE SIZES
Authors: Shao Jun , Chow Shein‐Chung 
Published: February 2008

SummaryThe problem of testing for treatment effect based on binary response data is considered, assuming that the sample size for each experimental unit and treatment combination is random. It is assumed that the sample size follows a distribution that belongs to a parametric family. The uniformly most powerful unbiased tests, which are equivalent to the likelihood ratio tests, are obtained when the probability of the sample size being zero is positive. For the situation where the sample sizes are always positive, the likelihood ratio tests are derived. These test procedures, which are unconditional on the random sample sizes, are useful even when the random sample sizes are not observed. Some examples are presented as illustration.
THE CONSTRUCTION OF EQUILEVERAGE DESIGNS FOR MULTIPLE LINEAR REGRESSION
Authors: Dollinger Michael B. , Staudte Robert G. 
Published: February 2008

SummaryThe hat matrix is widely used as a diagnostic tool in linear regression because it contains the leverages which the independent variables exert on the fitted values. In some experiments, cases with high leverage may be avoided by judicious choice of design for the independent variables. A variety of methods for constructing equileverage designs for linear regression are discussed. Such designs remove one of the factors, namely large leverage points, which can lead to nonrobust estimators and tests. In addition, a method is given for combining equileverage designs to test for lack of fit of the linear model.
ON THE VARIANCE‐COVARIANCE MATRIX IN MARKOVIAN MANPOWER SYSTEMS IN CONTINUOUS TIME
Authors: Gerontidis Ioannis I. 
Published: February 2008

SummaryThe variability of the grade sizes in Markovian manpower systems in continuous time is considered. The differential equations describing the evolution of the variance‐covariance matrix are derived and the associated generator is determined in a closed form. The limiting variability of an expanding system is studied via two theorems which provide conditions for the limit to exist independently of the initial distribution and for the rate of convergence to be exponentially fast. A numerical illustration of the theory is given.
TESTING THE STABILITY OF REGRESSION COEFFICIENTS USING GENERALIZED RECURSIVE RESIDUALS
Authors: Michels Paul , Trenkler Götz 
Published: February 2008

SummarySimple but flexible methods to detect deviations from the assumption of constant coefficients in linear regression are presented. Based on recursive residuals a runs test is developed as an alternative to CUSUM‐ and MOSUM‐techniques. Finally a simulation study gives insight into the new method.
TABLES OF SMALL OPTIMAL REPEATED MEASUREMENTS DESIGNS
Authors: Street Deborah J. , Eccleston J.A. , Wilson William H. 
Published: February 2008

SummaryThis paper presents tables of the optimal repeated measurements designs for the estimation of direct effects and of residual effects for a model with independent errors, for up to n = 10 experimental units, for t= 2 treatments and p= 2, 3 or 4 periods, and for t= 3 treatments and p= 2 or 3 periods.
VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES
Authors: Jones M.C. 
Published: February 2008

SummaryThe term “variable kernel density estimate” is sometimes used to mean a kernel density estimate employing a different bandwidth for each data point, and sometimes to denote a kernel density estimate with bandwidth a function of estimation location. This expository article stresses the importance of the distinction between these two definitions, both via an introductory description of the ideas involved and in terms of their comparative theoretical performance.
COMPUTER‐AIDED CONSTRUCTION OF SMALL (M, S)‐OPTIMAL INCOMPLETE BLOCK DESIGNS
Authors: Nguyen Nam‐Ky , Dey Aloke 
Published: February 2008

SummaryA new exchange algorithm for the construction of (M, S)‐optimal incomplete block designs (IBDS) is developed. This exchange algorithm is used to construct 973 (M, S)‐optimal IBDs (v, k, b) for v= 4,…,12 (varieties) with arbitrary v, k (block size) and b (number of blocks). The efficiencies of the “best” (M, S)‐optimal IBDs constructed by this algorithm are compared with the efficiencies of the corresponding nearly balanced incomplete block designs (NBIBDs) of Cheng(1979), Cheng & Wu (1981) and Mitchell & John(1976).
RANDOMISATION AND THE 1975 SENATE BALLOT DRAW
Authors: CLARK R.M. , HARCOURT A.G. 
Published: February 2008

Summary “So the last shall be first, and the first last; for many be called, but few chosen.” Matthew 20:16The “random” draw for positions on the Senate ballot papers in the 1975 election resulted in an apparently non‐random ordering, to the possible advantage of one particular party. This paper assesses the statistical significance of the 1975 draw and looks at possible causes of the evident non‐randomness. A simplified yet realistic mathematical model is used to describe conditions under which the so‐called donkey vote can have an effect on the final outcome of the election, thereby confirming the widely‐held belief that the order of parties on the Senate ballot paper is relevant. We examine other Senate elections between 1949 and 1983 for the existence of relevant non‐randomness similar to the 1975 result. Finally, we report briefly on our submission to the 1983 Joint Select Committee on Electoral Reform, which led to an improvement in the randomisation procedure.
EFFICIENT BLOCK DESIGNS FOR CORRELATED OBSERVATIONS
Authors: Martin R.J. , Eccleston J.A. 
Published: February 2008

SummaryCurrent methods for the design of efficient incomplete block experiments when the observations within a block are dependent usually involve computer searches of binary designs. These searches give little insight into the features that lead to efficiency, and can miss more efficient designs. This paper aims to develop some approximations which give some insight into the features of a design that lead to high efficiency under a generalized least‐squares analysis for a known dependence structure, and to show that non‐binary designs can be more efficient for some dependence structures. In particular, we show how neighbour balance and end plot balance are related to the design efficiency for low‐order autoregressions, and that under moderate positive dependence, replication at lag two can sometimes increase efficiency.
DISTRIBUTION FREE MULTIPLE COMPARISONS FOR MONOTONICALLY ORDERED TREATMENT EFFECTS
Authors: Hayter Anthony J. , Stone Glenn 
Published: February 2008

SummaryA method of calculating simultaneous one‐sided confidence intervals for all ordered pairwise differences of the treatment effectsj–i, 1 i < j k, in a one‐way model without any distributional assumptions is discussed. When it is known a priori that the treatment effects satisfy the simple ordering1k, these simultaneous confidence intervals offer the experimenter a simple way of determining which treatment effects may be declared to be unequal, and is more powerful than the usual two‐sided Steel‐Dwass procedure. Some exact critical points required by the confidence intervals are presented for k= 3 and small sample sizes, and other methods of critical point determination such as asymptotic approximation and simulation are discussed.
ALL THE GAUSSIAN WHITE NOISE SERIAL COVARIANCE MOMENTS TO ORDER FOUR
Authors: Anderson O.D. 
Published: February 2008

SummaryUsing a recursive method, we obtain all the cumulants, central moments, and moments about zero, up to order 4, for the mean‐corrected serial covariances from series realisations of length n, given a Gaussian white noise process. Some implicit higher order results are also derived.