bluuebunny/crossref_metadata_2025_split huggingface dataset:
CORRECTING FOR KURTOSIS IN DENSITY ESTIMATION
Authors: Ruppert D. , Wand M.P. 
Published: February 2008

SummaryUsing a global window width kernel estimator to estimate an approximately symmetric probability density with high kurtosis usually leads to poor estimation because good estimation of the peak of the distribution leads to unsatisfactory estimation of the tails and vice versa. The technique proposed corrects for kurtosis via a transformation of the data before using a global window width kernel estimator. The transformation depends on a “generalised smoothing parameter” consisting of two real‐valued parameters and a window width parameter which can be selected either by a simple graphical method or, for a completely data‐driven implementation, by minimising an estimate of mean integrated squared error. Examples of real and simulated data demonstrate the effectiveness of this approach, which appears suitable for a wide range of symmetric, unimodal densities. Its performance is similar to ordinary kernel estimation in situations where the latter is effective, e.g. Gaussian densities. For densities like the Cauchy where ordinary kernel estimation is not satisfactory, our methodology offers a substantial improvement.
ON THE DESIGN OF FIELD EXPERIMENTS UNDER A ONE‐DIMENSIONAL NEIGHBOUR MODEL
Authors: Gleeson Alan C. , Eccleston John A. 
Published: February 2008

SummaryThis paper examines the effect of randomisation restrictions, either to satisfy conditions for a balanced incomplete block design or to attain a higher level of partial neighbour balance, on the average variance of pair‐wise treatment contrasts under a neighbour model discussed by Gleeson & Cullis (1987). Results suggest that smaller average pairwise variances can be obtained by ignoring requirements for incomplete block designs and concentrating on achieving a higher level of partial neighbour balance. Field layout of the design, although often determined by practical constraints, e.g. size, shape of site, minimum plot size and experimental husbandry, may markedly affect average pairwise variance. For the one‐dimensional (row‐wise) neighbour model considered here, investigation of three different layouts suggests that for a rectangular array of plots, smaller average pairwise variances can generally be obtained from layouts with fewer rows and more plots per row.
ROBUST ESTIMATION AND HYPOTHESIS TESTS FOR FIRST‐ORDER THRESHOLD AUTOREGRESSIVE MODELS
Authors: Kulkarni P.M. 
Published: February 2008

SummaryResults of Petrucelli & Woolford (1984) for a first‐order threshold autoregressive model are considered from a robust point of view. Robust estimators of the threshold parameters of the model are obtained and their asymptotic normality is proved. Testing the equality of the threshold parameters is considered using the robust analogues of Wald and score test statistics. Limiting distributions of these statistics are given under both null and alternative hypotheses.
PITMAN NEARNESS COMPARISONS OF ESTIMATES OF TWO ORDERED NORMAL MEANS
Authors: Gupta Rameshwar D. , Singh Harshinder 
Published: February 2008

SummaryMaximum likelihood estimates of ordered means of two normal distributions having common variance have been shown to be better than the usual maximum likelihood estimates (i.e. corresponding sample means) with respect to Pitman Nearness criterion. The maximum likelihood estimate of common variance taking into consideration the order restriction of the means is shown to have smaller mean square error than the unrestricted maximum likelihood estimate of the common variance. These two estimators have also been compared with respect to Pitman Nearness criterion.
ON THE DERIVATION AND COMPUTATION OF THE CORNISH‐FISHER EXPANSION
Authors: Lee Yoong‐Sin , Lee Moy Chee 
Published: February 2008

SummaryThe paper gives a simple derivation based on elementary statistics for the Cornish—Fisher expansion. The method simplifies greatly the algebra in working out the polynomials involved both in the normalization and in the inverse expansions. The derivation also leads to recursive formulae which yield powerful algorithms for numerical computation of the expansion.
FITTING SPATIAL CORRELATION MODELS: APPROXIMATING A LIKELIHOOD APPROXIMATION
Authors: Kiiveri H.T. 
Published: February 2008

SummaryThe paper considers a class of spatial correlation models (stationary Gaussian processes) which includes (spatial) conditional autoregressive, simultaneous autoregressive, moving average and direct covariance models. Given observations on a finite rectangular lattice, a likelihood approximation for estimating the parameters in the spectral density of the model is discussed. The approximation consists of applying the trapezoidal rule, with a her grid of frequencies than the usual Fourier frequencies, to compute the integral in an appraximation due to Whittle (1954) and later modified by Guyon (1984). With this approximation, a Fisher scoring type algorithm has a simple form and in some casea reduces to iteratively reweighted least squares. Methods for computing the unbiased two‐dimensional periodogram required by the method are presented and the accuracy of the approximation is discussed. The asymptotic distribution of the parameter estimates computed from the likelihood approximation is also given.
A NOTE ON EVANESCENT PROCESSES
Authors: Pollett P.K. , Jones D. Vere 
Published: February 2008

SummaryThis note examines the connection between μ‐invariant measures for the transition function of a continuous‐time Markov chain and those of its q‐matrix, Q. The major result establishes a necessary and aufficient condition for a convergent μ‐invariant measure for Q to be μ‐inhant for the minimal transition function, P, under the assumption that P is honest. This corrects Theorem 6 of Vere‐Jones (1969) and the first part of Corollary 1 of Pollett (1986), both of which assert that the above conclusion holds in the absence of this condition. The error was pointed out by E.A. van Doom (1991) and the counterexample which be presented provides the basis for the present arguments. In determining where the error occurred in the original proof, we are able to identify a simple sufficient condition for μ‐invariance.
STATISTICAL CHALLENGES OF AIDS: (The 1990 Knibbs Lecture, Canberra, 27 November 1990)
Authors: Becker Niels G. 
Published: February 2008

SummaryIssues that are central to the understanding and management of the HIV epidemic have generated numerous statistical challenges. This paper considers questions concerning the incubation period, the effects of treatments, pre diction of AIDS cases, the choice of surrogate end points for the assessment of treatments and design of strategies for screening blood samples. These issues give rise to a broad range of intriguing problems for statisticians. We describe some of these problems, how they have been tackled so far and what remains to be done. The discussion touches on topical statistical methods such as smoothing, bootstrapping, interval censoring and the ill‐posed inverse problem, as well as asking fundamental questions for frequentist statistics.
A SEMIPARAMETRIC MIXTURE MODEL FOR THE ANALYSIS OF COMPETING RISKS DATA
Authors: Kuk Anthony Y.C. 
Published: February 2008

SummaryThis paper deals with the regression analysis of failure time data when there are censoring and multiple types of failures. We propose a semiparametric generalization of a parametric mixture model of Larson & Dinse (1985), for which the marginal probabilities of the various failure types are logistic functions of the covariates. Given the type of failure, the conditional distribution of the time to failure follows a proportional hazards model. A marginal like lihood approach to estimating regression parameters is suggested, whereby the baseline hazard functions are eliminated as nuisance parameters. The Monte Carlo method is used to approximate the marginal likelihood; the resulting function is maximized easily using existing software. Some guidelines for choosing the number of Monte Carlo replications are given. Fixing the regression parameters at their estimated values, the full likelihood is maximized via an EM algorithm to estimate the baseline survivor functions. The methods suggested are illustrated using the Stanford heart transplant data.
ESTIMATION OF GUARANTEE TIME AND MEAN LIFE AFTER WARRANTY FOR TWO‐PARAMETER EXPONENTIAL FAILURE MODEL
Authors: Khattree Ravindra 
Published: February 2008

SummarySuppose manufactured items have failure times distributed with the two‐parameter exponential density function à‐le‐(x‐μ)/à (x≥μ≥0). The paper considers estimation of the guarantee time μ and mean life after warranty à. Properties of various estimators are given for both separate and simultaneous estimation of μ and à. Criteria for comparing estimators are entropy loss, LINEX loss, and (generalized) Pitman nearness.
THE OPTIMAL TEST FOR SELECTING THE GREATER OF TWO BINOMIAL PROBABILITIES
Authors: Pollard Graham H. 
Published: February 2008

SummaryThe efficiency of a sequential test is related to the “importance” of the trials within the test. This relationship is used to find the optimal test for selecting the greater of two binomial probabilities,pαandpb, namely, the stopping rule is “gambler's ruin” and the optimal discipline whenpα+pb≤1 (≥ 1) is play‐the‐winner (loser), i.e. an α‐trial which results in a success is followed by an α‐trial (b‐trial) whereas an α‐trial which results in a failure is followed by α b‐trid (α‐trial) and correspondingly for b‐trials.
ON CHARACTERIZATIONS THROUGH MIXED SUMS
Authors: Pakes Anthony G. 
Published: February 2008

SummaryLet X, X‘ and X“ be independent and have the same law, and let U be uniform on [0,1] and independent of X’ and XI”. A problem which has received considerable attention is to identify the laws of X for which X =c U(X‘+X“), the equality being in law. If X is positive it has an exponential law, and mixture solutions exist without the sign restriction. The problem has been extended to allow more summands on either side and to let U have a special beta law. In the positive case X then has a gamma law.This problem is tackled in full generality by considering an integral equation for the characteristic function of X. Uniqueness results and necessary conditions are given. The general case is reduced to that where X is positive, and the solution of the corresponding integral equation is constructed under the necessary conditions. This is shown to characterize all possible laws of X when there is a single summand on the left and U has an arbitrary law on [0,1]. Examples are given and known particular results are sharpened.
CLASSIFICATION AND PROPORTION ESTIMATION
Authors: Bélanger J. , Gagnon D. 
Published: February 2008

SummaryAssume that a number of individuals are to be classified into one of two populations and that, at the same time, the proportion of members of each population needs to be estimated. The allocated proportions given by the Bayes classification rule are not consistent estimates of the true proportions, so a different classification rule is proposed; this rule yields consistent estimates with only a small increase in the probability of misclassification. As an illustration, the case of two normal distributions with equal covariance matrices is dealt with in detail.
MINIMUM QUADRATIC DISTANCE ESTIMATION FOR A PARAMETRIC FAMILY OF DISCRETE DISTRIBUTIONS DEFINED RECURSIVELY
Authors: Luong Andrew , Garrido José 
Published: February 2008

SummaryThe distribution function of a random sum can easily be computed iteratively when the distribution of the number of independent identically distributed elements in the sum is itself defined recursively. Classical estimation procedures for such recursive parametric families often require specific distributional assumptions (e.g. Poisson, Negative Binomial). The minimum distance estimator proposed here is an estimator within a larger parametric family. The estimator is consistent, efficient when the parametric family is truncated, and can be made either robust or asymptotically efficient when the parametric family has infinite range. Its asymptotic distribution is derived. A brief illustration with Automobile Insurance data is included.
TRIMMED LIKELIHOOD ESTIMATION OF LOCATION AND SCALE OF THE NORMAL DISTRIBUTION
Authors: Bednarski T. , Clarke B.R. 
Published: February 2008

SummaryConsideration of multivariate statistical procedures leads to description of robust methods of estimation of location and scale for real random variables. This paper outlines asymptotic theory for estimating location and scale by viewing it as a trimmed likelihood estimator. An appeal is made to results from empirical processes linking the proofs to compact differentiability of estimating functionals. The estimator for location does not depend on the scale estimate and is robust against asymmetric contamination. Coincidentally the location estimator denned at the normal distribution is similar to the least trimmed squares estimator. Simulations corroborate the asymptotic theory and illustrate robustness against asymmetric contamination.
AMEMIYA'S FORM OF THE WEIGHTED LEAST SQUARES ESTIMATOR
Authors: Koenker Roger , Machado José A.F. , Skeels Christopher L. , Welsh A.H. 
Published: February 2008

SummaryAmemiya's estimator is a weighted least squares estimator of the regression coefficients in a linear model with heteroscedastic errors. It is attractive because the heteroscedasticity is not parametrized and the weights (which depend on the error covariance matrix) are estimated nonparametrically. This paper derives an asymptotic expansion for Amemiya's form of the weighted least squares estimator, and uses it to discuss the effects of estimating the weights, of the number of iterations, and of the choice of the initial estimate. The paper also discusses the special case of normally distributed errors and clarifies the particular consequences of assuming normality.
NONPARAMETRIC ESTIMATION FOR COMPETING RISKS
Authors: Froda S. , Luong A. 
Published: February 2008

SummaryThis paper considers the estimation of the ratio of two scale parameters when the data are censored. It emphasises characteristics of the asymptotic variance under censoring from a practical point of view. The estimator proposed by Padgett & Wei (1982) for the two‐sample scale model is extended to the competing risks model. Asymptotic properties of the estimator are studied via its influence function. The use of influence functions permits a unified treatment of both models. Examples show and illustrate that under both models the variance can become infinite under some circumstances.
UPPER BOUNDS FOR LATINIZED DESIGNS
Authors: Williams E.R. , John J.A. 
Published: February 2008

SummaryUpper bounds axe derived for the efficiency factor of a class of resolvable incomplete block designs known as latinized designs. These designs are particularly useful in glasshouse and field trials, and can be readily extended to two‐dimensional blocking structures. Existing bounds for resolvable designs axe also reviewed and a comparison is made between the third moment bounds discussed by Jarrett (1989) and the second moment bounds of Tjur (1990).
NEIGHBOUR ANALYSIS WITH ADJUSTMENT FOR INTERPLOT COMPETITION
Authors: Pithuncharurnlap M. , Basford K.E. , Federer W.T. 
Published: February 2008

SummaryIn field experiments involving a large number of experimental plots, a neighbour analysis can be used to control environmental variation by estimating the trend within blocks. The effect of interplot competition is another important source of variation which has an influence on the estimation of treatment contrasts. To reduce the effect of the variation from these sources and to improve the precision of comparison between treatments, a spatial model is proposed for incorporating both trend effect and interplot competition. It is a modification to the residual maximum likelihood neighbour analysis of Gleeson & Cullis (1987) using the two neighbouring treatment effects to estimate interplot competition. A real example is used to illustrate this methodology. The results indicate that the extended model gives no appreciable difference in standard error of mean differences compared with the model taking into account the trend effect only. However, the rankings of estimated treatment means do differ. More research using both real and simulated data is required before such models that incorporate trend and competition effects can be confidently recommended.
NONPARAMETRIC ESTIMATION OF ANGULAR DISTRIBUTIONS FROM CARTESIAN OBSERVATIONS WITH ERROR
Authors: Horgan G.W. , Haslett J. 
Published: February 2008

SummaryIf angular data are obtained from Cartesian observations, then any measurement error in these observations will produce a particular error structure in the angular data. The paper shows how non‐parametric density estimation by orthogonal series may be performed in this case.
INVERSE SAMPLING FOR DOMAIN ESTIMATION IN A STRATIFIED POPULATION
Authors: Mukerjee Rahul , Basu Sujit K. 
Published: February 2008

SummaryFor a stratified population under inverse sampling, we propose and study an unbiased estimator for the mean of units belonging to a domain with specific features. An alternative, simpler, ratio‐type estimator is also considered. Empirical studies show that strategies based on inverse sampling can be superior to a more traditional strategy based on stratified simple random sampling with a fixed number of draws in each stratum.
ROBUST ESTIMATION UNDER TYPE II CENSORING
Authors: Akritas Michael G. , Basak Indrani , Lee Myung Hwi 
Published: February 2008

SummaryThe properties of robust M‐estimators with type II censored failure time data are considered. The optimal members within two classes of ψ‐functions are characterized. The first optimality result is the censored data analogue of the optimality result described in Hampel et al. (1986); the estimators corresponding to the optimal members within this class are referred to as the optimal robust estimators. The second result pertains to a restricted class of ψ‐functions which is the analogue of the class of ψ‐functions considered in James (1986) for randomly censored data; the estimators corresponding to the optimal members within this restricted class are referred to as the optimal James‐type estimators. We examine the usefulness of the two classes of ψ‐functions and find that the breakdown point and efficiency of the optimal James‐type estimators compare favourably with those of the corresponding optimal robust estimators. From the computational point of view, the optimal James‐type ψ‐functions are readily obtainable from the optimal ψ‐functions in the uncensored case. The ψ‐functions for the optimal robust estimators require a separate algorithm which is provided. A data set illustrates the optimal robust estimators for the parameters of the extreme value distribution.
KERNEL DENSITY ESTIMATION WHEN THE BANDWIDTH IS LARGE
Authors: Jones M.C. 
Published: February 2008

SummaryThe performance of kernel density estimation, in terms of mean integrated squared error, is investigated in the opposite of the usual situation, namely when the bandwidth is large. This affords noteworthy insights including the special role taken by the normal density function as kernel and a tie‐in with ‘semiparametric’ approaches to density estimation.
INTERRELATIONSHIPS BETWEEN lP‐ISOTROPIC DENSITIES AND lP‐ISOTROPIC SURVIVAL FUNCTIONS, AND DE FINETTI REPRESENTATIONS OF SCHUR‐CONCAVE SURVIVAL FUNCTIONS
Authors: Hayakawa Yu 
Published: February 2008

SummaryInterrelationships between lP‐isotropic densities and lP‐isotropic survival functions are studied. We obtain representations for densities whose survival functions are lP‐isotropic and for survival functions whose densities are lP‐isotiopic. The former are mixtures of products of Weibull densities and the latter are mixtures of products of Gamma survival functions. Based on a theorem proved by Cooke, we show that de Finetti representations for Schur‐concave survival functions that have the same level sets as a product measure can be represented as mixtures of products of increasing failure rate survival functions.
ON CROSS‐VALIDATION FOR SMOOTHING SPLINES IN THE CASE OF DEPENDENT OBSERVATIONS
Authors: van der LINDE ANGELIKA 
Published: February 2008

SummaryCross‐validation, as a popular tool for choosing a smoothing parameter, is generalized to the case of dependent observations. A general version of the ‘deletion theorem’ for representation and simplified calculation of cross‐validatory criteria is given. Finally cross‐validation is discussed in terms of penalized likelihoods as a method for model choice analogous to the Akaike information criterion.
RATES OF CONVERGENCE IN SEMI‐PARAMETRIC MODELLING OF LONGITUDINAL DATA
Authors: MOYEED R.A. , DIGGLE P.J. 
Published: February 2008

SummaryWe consider the problem of semi‐parametric regression modelling when the data consist of a collection of short time series for which measurements within series are correlated. The objective is to estimate a regression function of the form E[Y(t) |x] =x'ß+μ(t), where μ(.) is an arbitrary, smooth function of timet, andxis a vector of explanatory variables which may or may not vary witht.For the non‐parametric part of the estimation we use a kernel estimator with fixed bandwidthh.Whenhis chosen without reference to the data we give exact expressions for the bias and variance of the estimators for β and μ(t) and an asymptotic analysis of the case in which the number of series tends to infinity whilst the number of measurements per series is held fixed. We also report the results of a small‐scale simulation study to indicate the extent to which the theoretical results continue to hold whenhis chosen by a data‐based cross‐validation method.
APPLICATIONS OF ROBUST ESTIMATION FOR VARIANCE COMPONENTS MODELS:THE DETECTION OF MAJOR GENE EFFECTS IN FINGER RIDGE COUNTS IN NORMAL AND FRAGILE X FAMILIES
Authors: Huggins Richard M. , Loesch Danuta Z. 
Published: February 2008

SummaryA diagnostic technique is proposed to detect major gene effects and other systematic departures from a model for the trait means in the presence of outliers. The technique is based on the examination of residuals from fitting variance components models to quantitative pedigree data using robust statistical procedures. The approach is demonstrated using the total ridge count and ridge count of the middle finger from 54 extended families affected with the Fragile X syndrome, and a sample of 217 normal pedigrees.
CHARACTERIZATIONS OF SHIFT‐INVARIANT DISTRIBUTIONS BASED ON SUMMATION MODULO ONE
Authors: Wilms Roel J.G. , Thiemann Jan G.F. 
Published: February 2008

SummaryLet X1Y1,…, Yn be independent random variables. We characterize the distributions of X and Yj satisfying the equation {X+Y1++Yn}=dX, where {Z} denotes the fractional part of a random variable Z. In the case of full generality, either X is uniformly distributed on [0,1), or Yj has.a shifted lattice distribution and X is shift‐invariant. We also give a characterization of shift‐invariant distributions. Finally, we consider some special cases of this equation.
BAHADUR SLOPES FOR COMPARISONS OF TESTS BASED ON RESAMPLING
Authors: Jing Bing‐Yi 
Published: February 2008

SummaryLetX1,…,Xnbe random variables symmetric about θ from a common unknown distribution Fθ(x) =F(x–θ). To test the null hypothesisH0:θ= 0 against the alternativeH1:θ > 0, permutation tests can be used at the cost of computational difficulties. This paper investigates alternative tests that are computationally simpler, notably some bootstrap tests which are compared with permutation tests. Of these the symmetrical bootstrap‐f test competes very favourably with the permutation test in terms of Bahadur asymptotic efficiency, so it is a very attractive alternative.