bluuebunny/crossref_metadata_2025_split huggingface dataset:
ESTIMATION OF THE PARAMETER OF A POISSON DISTRIBUTION USING A LINEX LOSS FUNCTION
Authors: Sadooghi‐Alvandi S.M. 
Published: February 2008

SummaryThis paper considers estimation of the parameter of a Poisson distribution using Varian's (1975) asymmetric LINEX loss function L (δ) = b{exp(aδ) ‐ aδ ‐ 1}, where δ is the estimation error and b > 0, a 0. It is shown that for a < 0, the sample mean X¯ is admissible whereas for a > 0, X¯ is dominated by c*X¯, where c*= (n/a)log(1+a/n). Practical implications of this result are indicated. More general results, concerning the admissibility of estimators of the form cX¯+ d are also presented.
ESTIMATING BIOMASS IN A VEGETATION MOSAIC USING DOUBLE SAMPLING WITH REGRESSION
Authors: Catchpole W.R. , Catchpole E.A. 
Published: February 2008

SummaryIn some vegetation types, total fuel loading (phytomass) can be predicted by easily‐measured variables such as vegetation type and height. A double sampling scheme is proposed in which fuel loading is estimated on a particular site by using quadrat sampling within patches of similar vegetation to develop a general prediction equation, and then line intercept sampling is used to estimate the mean of the easily‐measured variables on the site. This method is applied to estimate the total fine fuel loading on a heathland site.
BAHADUR EFFICIENCY OF LIKELIHOOD RATIO AND RELATED TESTS IN NONREGULAR MODELS
Authors: Kourouklis Stavros 
Published: February 2008

SummaryConsider the model of k populations whose densities are nonreg‐ular in the sense that they involve one or two unknown truncation parameters. In this paper a unified treatment of the problem of Bahadur efficiency of the likelihood ratio test for such a model is presented. The Bahadur efficiency of a certain test based on the union‐intersection principle is also studied. Some of these results are then extended to a larger class of nonregular densities.
SELECTION PROCEDURES FOR SCALE PARAMETERS USING TWO‐SAMPLE U‐STATISTICS
Authors: Gill A.N. , Mehta G.P. 
Published: February 2008

SummaryLet be k independent populations having the same known quantile of order p (0 p 1) and let F(x)=F(x/i) be the absolutely continuous cumulative distribution function of the ith population indexed by the scale parameter 1, i = 1,…, k. We propose subset selection procedures based on two‐sample U‐statistics for selecting a subset of k populations containing the one associated with the smallest scale parameter. These procedures are compared with the subset selection procedures based on two‐sample linear rank statistics given by Gill & Mehta (1989) in the sense of Pitman asymptotic relative efficiency, with interesting results.
A NEW APPROACH TO MAXIMUM LIKELIHOOD ESTIMATION OF THE THREE‐PARAMETER GAMMA AND WEIBULL DISTRIBUTIONS
Authors: Bai Jun , Jakeman Anthony J. , McAleer Michael 
Published: February 2008

SummaryA new approach, is proposed for maximum likelihood (ML) estimation in continuous univariate distributions. The procedure is used primarily to complement the ML method which can fail in situations such as the gamma and Weibull distributions when the shape parameter is, at most, unity. The new approach provides consistent and efficient estimates for all possible values of the shape parameter. Its performance is examined via simulations. Two other, improved, general methods of ML are reported for comparative purposes. The methods are used to estimate the gamma and Weibull distributions using air pollution data from Melbourne. The new ML method is accurate when the shape parameter is less than unity and is also superior to the maximum product of spacings estimation method for the Weibull distribution.
NEAREST NEIGHBOUR ANALYSIS OF UNEQUALLY REPLICATED TRIALS
Authors: Pithuncharurnlap M. , Basford K.E. , Cullis B.R. 
Published: February 2008

SummaryThere has been much work on the use of neighbouring plots to control environmental variation in the analysis of agricultural field experiments. In particular, the Residual Maximum Likelihood Neighbour (REMLN) analysis of Gleeson&Cullis (1987) appears very promising. The application of the REMLN analysis to an unequally replicated field trial augmented with an additional variety planted every six plots in a grid system is here compared with a covariance (COV) analysis using the neighbouring grid or check plot values as the covariate. The results indicate that the REMLN analysis gives more accurate estimates of treatment contrasts than the COV analyses, but that the estimate of treatment means can be biased. The bias depends on the mean of the check plot. This bias can be removed by adjusting the estimates of the treatment means such that their average equals the average of the raw means rather than that of the raw data.
ESTIMATING THE COMMON MEAN OF A BIVARIATE NORMAL POPULATION
Authors: Kumar Somesh , Sharma Divakar 
Published: February 2008

SummaryFor estimating the common mean of a bivariate normal distribution, Krishnamoorthy & Rohatgi (1989) proposed some estimators which dominate the maximum likelihood estimator in a large region of the parameter space. We consider some modifications of these estimators and study their risk performance.
COMPARISONS AND SELECTION OF TWO‐PARAMETER EXPONENTIAL POPULATIONS
Authors: Bofinger Eve 
Published: February 2008

SummaryFor two‐parameter exponential populations with the same scale parameter (known or unknown) comparisons are made between the location parameters. This is done by constructing confidence intervals, which can then be used for selection procedures. Comparisons are made with a control, and with the (unknown) “best” or “worst” population. Emphasis is laid on finding approximations to the confidence so that calculations are simple and tables are not necessary. (Since we consider unequal sample sizes, tables for exact values would need to be extensive.)
RENEWAL COUNTING PROCESS INDUCED BY A DISCRETE MARKOV CHAIN
Authors: Adke S.R. , Balakrishna N. 
Published: February 2008

SummarySome renewal theoretic properties of a renewal counting process induced by a Markov chain on the set of non‐negative integers are established, namely, analogues of the classical elementary, Blackwell, and Breiman theorems and the key renewal theorem. These results generalize those of Vere‐Jones (1975) who considered a Markov chain on the set of positive integers.
ALLOCATION OF REMOTELY SENSED DATA USING MARKOV MODELS FOR IMAGE DATA AND PIXEL LABELS
Authors: Kiiveri H.T. , Campbell N.A. 
Published: February 2008

SummaryFor remotely sensed data, this paper reviews the Bayesian approach to the allocation of picture elements (pixels) to groups. Group labels are assumed a priori to be spatially correlated and, conditional on the labels, the image data are also assumed to be spatially correlated. The models considered have the property that the posterior distribution of the pixel labels given the image data inherits conditional independence constraints. Two allocation algorithms which exploit this fad are discussed. These algorithms are based on maximising the posterior distribution, and involve the use of neighbouring image and label data to update the label of any given pixel. The effect of spatial correlation in the image data on allocation performance is examined.
QUALITY CONTROL WITH MULTIVARIATE DATA
Authors: Sparks Ross S. 
Published: February 2008

SummaryThe paper examines statistical process control of bivariate and multivariate data, using in particular the multivariate equivalents of the univariate Shewhart chart, CUSUM chart and the Exponentially Weighted Moving Average chart. This illustrates the usefulness of Principal Component methods in statistical process control with multivariate data.
A NEW APPROACH TO DISCRIMINATION AND CLASSIFICATION USING A HAUSDORFF TYPE DISTANCE
Authors: Chatterjee Sangit , Narayanan A. 
Published: February 2008

SummaryA new method of discrimination and classification based on a Hausdorff type distance is proposed. In two groups, the Hausdorff distance is defined as the sum of the furthest distance of the nearest elements of one set to another. This distance has some useful properties and is exploited in developing a discriminant criterion between individual objects belonging to two groups based on a finite number of classification variables. The discrimination criterion is generalized to more than two groups in a couple of ways. Several data sets are analysed and their classification accuracy is compared to that obtained from linear discriminant function and the results are encouraging. The method in simple, lends itself to parallel computation and imposes less stringent conditions on the data.
SEMIPARAMETRIC COMPARISON OF REGRESSION CURVES VIA NORMAL LIKELIHOODS
Authors: Carroll R.J. , Hall Peter 
Published: February 2008

SummaryHärdle & Marron (1990) treated the problem of semiparametric comparison of nonparametric regression curves by proposing a kernel‐based estimator derived by minimizing a version of weighted integrated squared error. The resulting estimators of unknown transformation parameters are n‐consistent, which prompts a consideration of issues. of optimality. We show that when the unknown mean function is periodic, an optimal nonparametric estimator may be motivated by an elegantly simple argument based on maximum likelihood estimation in a parametric model with normal errors. Strikingly, the asymptotic variance of an optimal estimator of θ does not depend at all on the manner of estimating error variances, provided they are estimated n‐consistently. The optimal kernel‐based estimator derived via these considerations is asymptotically equivalent to a periodic version of that suggested by Härdle & Marron, and so the latter technique is in fact optimal in this sense. We discuss the implications of these conclusions for the aperiodic case.
THE BOOTSTRAP RISK OF LINEAR EMPIRICAL BAYES ESTIMATES
Authors: Mousa M. A. M. Ali 
Published: February 2008

SummaryThis paper discusses the bootstrap risk of the linear empirical Bayes estimate of the form θ=A&#x030c;+B&#x030c;x, where x is the current observation, and A&#x030c; and B&#x030c; are generally functions of the estimates of the prior parameters. The standard error of this risk is developed and ‘computations’ of both the bootstrap risk and its standard error are made.
ROBUST METHODS FOR DEPENDENT CELL PEDIGREE DATA
Authors: Marschner Lan C. 
Published: February 2008

SummaryThis paper studies a robust approach to the analysis of cell pedigree data, building on the work of Huggins & Marschner (1991) which discussed M‐estimation for the so‐called bifurcating autoregressive process. The study allows for incomplete observation of the pedigree, and incorporates the possibility of additive effects outliers, as discussed in the time series literature. Some properties of the proposed estimation procedure are studied, including a Monte Carlo investigation of robustness in the presence of contamination.
ON QUASI‐LIKELIHOOD METHODS AND ESTIMATION FOR BUNCHING PROCESSES AND HETEROSCEDASTIC REGRESSION MODELS
Authors: Heyde C.C. , Lin Y.‐X. 
Published: February 2008

SummaryThis paper is concerned with quasi‐likelihood estimation methods which provide benchmarks in efficiency against which other methods may be as sessed. We discuss general principles and illustrate them by the estimation of parameters for branching processes with immigration and heteroscedastic regression models.
FITTING FINITE MIXTURE MODELS IN A REGRESSION CONTEXT
Authors: Jones P.N. , McLachlan G. J. 
Published: February 2008

SummarySuppose data are collected in a three‐mode fashion (individuals x items X attributes), and it is sought to cluster the individuals into groups on the basis of lineat relations between scores on the attributes for each item and auxiliary measurements made on the same items. A mixture model is pro posed and the EM algorithm is used to fit it to the data by simultaneously estimating the group parameters and allocating individuals to groups. The method is illustrated by a simulation study and a real example in which consumers are clustered on the basis of product scores that are related to a sensory laboratory measurement.
MULTIVARLYTE ANALYSIS OF CLUSTERED SURVEY DATA: TESTING EQUALITY OF COVARLANCE MATRICES
Authors: Pezvaiz Muhammad Khalid , Skinner C.J. 
Published: February 2008

SummaryThe problem of testing the hypothesis of equality of covariance matrices in the presence of two‐stage sampling is considered. Asymptotic test procedures based on linearization, grouping and jackknifing with or without transformation are proposed. The finite sample properties of these procedures are investigated in sampling experiments both from simulated known distributions and from a natural population.
LENGTH‐BIASING, CHARACTERIZATIONS OF LAWS AND THE MOMENT PROBLEM
Authors: Pakes Anthony G. , Khattree Ravindra 
Published: February 2008

SummaryA positive random variableXwith a finite mean has an induced length‐biased law represented byY, andYis stochastically larger thanX. An independent uniform random contraction ofY, UY, has the same law asXif and only if the latter is exponential. This property is extended to non‐uniform contractions and a more general notion of length‐biasing. The distributional equality ofXandWleads to a functional equation for the moment function ofX, which has either Infinitely many solutions or none. WhenUis constant,Xcan have a log‐normal law, but it can also have laws with the same moment sequence as this log‐nod law. The case whereUhas a certain beta, or generalized beta, law give t3 characterizations of generalized gamma laws, or to products of independent copies of them. This occurs even when these laws are not determined by their moment sequences.
FACTORIAL ANALYSIS OF RECIDIVIST DATA
Authors: Maller R.A. 
Published: February 2008

SummaryThis paper presents a method of fitting factorial models to recidivism data consisting of the (possibly censored) time to ‘fail’ of individuals, in order to test for differences between groups. Here ‘failure’ means rearrest, reconviction or reincarceration, etc. A proportion P of the sample is assumed to be ‘susceptible’ to failure, i.e. to fail eventually, while the remaining 1‐P are ‘immune’, and never fail. Thus failure may be described in two ways: by the probability P that an individual ever fails again (‘probability of recidivism’), and by the rate of failure Λ for the susceptibles. Related analyses have been proposed previously: this paper argues that a factorial approach, as opposed to regression approaches advocated previously, offers simplified analysis and interpretation of these kinds of data. The methods proposed, which are also applicable in medical statistics and reliability analyses, are demonstrated on data sets in which the factors are Parole Type (released to freedom or on parole), Age group (≤ 20 years, 20–40 years, > 40 years), and Marital Status. The outcome (failure) is a return to prison following first or second release.
A COMPARISON OF DESIGN‐BASED AND MODEL‐BASED ESTIMATORS OF THE FINITE POPULATION DISTRIBUTION FUNCTION
Authors: Dorfman Alan H. 
Published: February 2008

SummaryRival estimators of the distribution function of a finite population, derived from the model‐based and design‐based approaches to sampling inference, are compared. A design‐based estimator due to Rao, Kovar & Mantel (1990) has the desirable property from a model‐based viewpoint, of being model‐unbiased under misspecification of model, when the sample meets certain conditions. A modified version of this estimator is suggested; it relies less on design‐based ingredients, and in particular avoids second‐order inclusion probabilities. It is the preferred estimator when, as is often the case in sampling practice, the model is adopted without thorough consideration of goodness of fit. However, if standard regression procedures of model construction and criticism are employed, then a strictly model‐based estimator does better.
SADDLEPOINT METHODS FOR BOOTSTRAP CONFIDENCE BANDS IN NONPARAMETRIC REGRESSION
Authors: Wang Suojin 
Published: February 2008

SummaryBootstrap techniques have been used to construct confidence bands in nonparametric regression problems (Härdle & Bowman, 1988). Yet the required simulation is generally computationally intensive and therefore makes it difficult to conduct further investigations. In this paper, two saddlepoint methods are considered as alternatives to the naive simulation procedure. Some improvements to Härdle & Bowman's bootstrap method are suggested. The improvements are numerically verified using these efficient and accurate analytic methods.
PREDICTION OF THE FINITE POPULATION DISTRIBUTION FUNCTION UNDER GAUSSIAN SUPERPOPULATION MODELS
Authors: Bolfarine Heleno , Sandoval Mönica C. 
Published: February 2008

SummaryThis article considers optimal prediction of the finite population distribution function under Gaussian superpopulation models, which allows auxiliary prior information to be incorporated into the estimation process. Large sample approximations for the variance of the optimal predictors are derived in some special important cases. A small scale Monte Carlo study illustrates comparisons between the optimal predictor and some others which are proposed in the literature. The conclusion is that the optimal predictor can be considerably more efficient in situations where the normal superpopulation model is adequate.
SOME PROPERTIES OF PROFILE BOOTSTRAP CONFIDENCE INTERVALS
Authors: Kabaila Paul 
Published: February 2008

SummaryWe consider the problem of finding an equi‐tailed confidence interval, with coverage probability (1‐α), for a scalar parameter θ0 in the presence of a (possibly infinite dimensional) nuisance parameter ψ0. It is supposed that the value taken by θ0 does not restrict the value that ψ0 may take and vice‐versa. Given a sensible estimate ψn of ψ0, profile bootstrap confidence interval for θ0 is defined to be the exact equi‐tailed confidence interval with coverage probability (1‐α) assuming that ψ0=ψn. We compare the properties of the profile bootstrap confidence interval and the ordinary bootstrap confidence interval when they are based on studentised and unstudentised quantities. Under mild regularity conditions the profile bootstrap confidence interval is always a subset of the set of allowable values of θ0 and is transformation‐respecting when based on either an unstudentised quantity or a studentised quantity satisfying certain restrictions. As a confidence interval for the autoregressive parameter of an AR(1) process, the profile bootstrap confidence interval has important advantages over the ordinary bootstrap confidence interval based on a studentised quantity.
ON STUDENTIZING AND BLOCKING METHODS FOR IMPLEMENTING THE BOOTSTRAP WITH DEPENDENT DATA
Authors: Davison A.C. , Hall Peter 
Published: February 2008

SummaryGōtze & Kūnsch (1990) announced that a certain version of the bootstrap percentile‐t method, and the blocking method, can be used to improve on the normal approximation to the distribution of a Studentized statistic computed from dependent data. This paper shows that this result depends fundamentally on the method of Studentization. Indeed, if the percentile‐t method is implemented naively, for dependent data, then it does not improve by an order of magnitude on the much simpler normal approximation despite all the computational effort that is required to implement it. On the other hand, if the variance estimator used for the percentile‐t bootstrap is adjusted appropriately, then percentile‐t can improve substantially on the normal approximation.
A CHARACTERIZATION OF GEOMETRIC DISTRIBUTIONS THROUGH CONDITIONAL INDEPENDENCE
Authors: Liang TaChen , Balakrishnan N. 
Published: February 2008

SummaryLetX1,…,Xnbe mutually independent non‐negative integer‐valued random variables with probability mass functionsfi(x) > 0 forz= 0,1,…. LetEdenote the event that {X1≥X2≥…≥Xn}. This note shows that, conditional on the eventE, Xi‐Xi+ 1 andXi+ 1 are independent for all t = 1,…,kif and only ifXi(i= 1,…,k) are geometric random variables, where 1 ≤k≤n‐1. Thekgeometric distributions can have different parameters θi,i= 1,…,k.
EXPECTATIONS OF RATIOS OF QUADRATIC FORMS IN NORMAL VARIABLES: EVALUATING SOME TOP‐ORDER INVARIANT POLYNOMIALS
Authors: Smith Murray D. 
Published: February 2008

SummaryChikuse's (1987) algorithm constructs top‐order invariant polynomials with multiple matrix arguments. Underlying it is a set of simultaneous equations for which all integer solutions must be found. Each solution represents a component of the sum of terms which comprise the polynomial. The system of equations has a specialised structure which may be exploited to obtain a polynomial with r matrix arguments in terms of a polynomial with r‐1 matrix arguments. This is demonstrated for two particular polynomials that have two matrix arguments. These results are applied to problems involving expectations of ratios of quadratic forme in normal variables; analytic as well as computable formulae are derived.
THE REDUCED‐RANK GROWTH CURVE MODEL FOR DISCRIMINANT ANALYSIS OF LONGITUDINAL DATA
Authors: Albert Jeffrey M. , Kshirsagar Anant M. 
Published: February 2008

SummaryThis paper presents a method of discriminant analysis especially suited to longitudinal data. The approach is in the spirit of canonical variate analysis (CVA) and is similarly intended to reduce the dimensionality of multivariate data while retaining information about group differences. A drawback of CVA is that it does not take advantage of special structures that may be anticipated in certain types of data. For longitudinal data, it is often appropriate to specify a growth curve structure (as given, for example, in the model of Potthoff & Roy, 1964). The present paper focuses on this growth curve structure, utilizing it in a model‐based approach to discriminant analysis. For this purpose the paper presents an extension of the reduced‐rank regression model, referred to as the reduced‐rank growth curve (RRGC) model. It estimates discriminant functions via maximum likelihood and gives a procedure for determining dimensionality. This methodology is exploratory only, and is illustrated by a well‐known dataset from Grizzle & Allen (1969).
TESTING THE SIGNIFICANCE OF GENOTYPE‐ENVIRONMENT INTERACTION
Authors: Williams E.R. , Wood J.T. 
Published: February 2008

SummaryThe AMMI (additive main effects‐multiplicative interaction) model is often used to investigate interactions in two‐way tables, in particular for genotype‐environment interactions. Both Gollob (1968) and Mandel (1969, 1971) proposed methods for testing the significance of such interactions. These methods are compared using simulated data. Our results support Mandel's conclusions, but his method is conservative and we recommend a test proposed by Johnson & Graybill (1972).
AN ALGORITHM FOR CONSTRUCTING OPTIMAL RESOLVABLE ROW‐COLUMN DESIGNS
Authors: Nguyen Nam‐Ky , Williams E.R. 
Published: February 2008

SummaryThis paper describes an effective algorithm for constructing optimal or near‐optimal resolvable row‐column designs (RCDs) with up to 100 treatments. The performance of this algorithm is assessed against 20 2‐replicate resolvable RCDs of Patterson & Robinson (1989) and 17 resolvable RCDs based on generalized cyclic designs (GCDs) of Ipinyomi & John (1985). The use of the algorithm to construct RCDs with contiguous replicates is discussed.
ESTIMATING A LOGIT MODEL WITH RANDOMIZED DATA: THE CASE OF COCAINE USE
Authors: KERKVLIET JOE 
Published: February 2008

SummaryIn his influential book, Maddala (1983) suggests combining randomized response survey data with other personal information to estimate logit models predicting immoral, unpopular, or unlawful behaviour. This study is one of the first to implement this technique using real data. Models of college students' recent cocaine use are estimated with academic performance and socio‐economic characteristics as determinants. Parameter estimates obtained from randomized response surveys are compared to those obtained using conventional, direct question surveys. The results indicate that randomized response estimates provide useful information on the degree to which inferences regarding the determinants of cocaine use are sensitive to survey type.