bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mixed Moment Estimation in Hermite Distribution and its Comparison with Other Estimation Procedures from Sample Size Considerations
Authors: Patel Y. C. 
Published: May 2007

AbstractIn this paper (1) expressions (correct to n−2 terms) for biases, variances, and covariances of the estimators a and b of Hermite distribution with probability generating function Exp[a(t–1) + b(t–1)] are obtained for two mixed moment estimates; (2) for the biases and variance‐covariances, approximate regions of the parameter space (a>0, b>0) have been outlined where a sample of size 100 can be considered as “safe” in the sense that contribution of second order terms in them is 5% of that from the first order term; (3) comparison of the biases and variance‐covariances of these two sets of estimators are made with those for the moment estimators, maximum likelihood estimates and the even point estimators for a sample of size 100 using the terms up to order n−2; (4) the comparisons based on n−2 terms in (3) have not only provided information on the estimation procedures included in the Hermite distribution, but also demonstrated the importance of higher order terms in the sampling properties of the various alternative techniques for the Hermite distribution.
Analysis of Counts involving Random Effects with Applications in Experimental Biology
Authors: Jansen J. 
Published: May 2007

AbstractThis paper is concerned with the analysis of count data with special reference to experimental biology and agricultural research. The model considered in this paper is obtained by extending a generalized linear model by introducing random effects with associated variance components on the scale of the linear predictor. Maximum likelihood estimation is discussed and compared with a method which uses a simplified version of the likelihood equations. Two practical applications are used to illustrate the methods.
A Stratified Product Limit Estimator for the Follow‐up Study in Cardio‐Thoracic Surgery
Authors: Chen C. L. , Laub Glenn W. , McGrath Lynn B. 
Published: May 2007

AbstractWe propose a stratified product limit estimator and compare the asymptotic results with those of the unstratified version. When the censoring mechanisms are unequal for different strata, the unstratified version may overestimate the total survival rate of a heterogeneous population. A numerical example in cardiac surgery is examined to demonstrate that this situation does occur in real applications. This overestimation also can be elucidated heuristically by some redistribution schemes for the censored data.
Testing in Normal Mixture Models with Some Information on the Parameters
Authors: Mangin Brigitte , Goffinet Bruno , Elsen Jean Michel 
Published: May 2007

AbstractThe aim of this paper is to study the distribution of the likelihood ratio for testing whether or not one is sampling from a mixture of two distributions or from a single distribution. We study the case where some information is available on the variation range of the parameters of populations. First we study the simplest case in which the difference between the mean of the two populations is known. We show certain distortions between theoretical and simulation results. Secondly, we show how this distortion spreads to the situation where this difference belongs to an interval. Finally, we give an example concerning the detection of major genes in animal population.
Estimation of a Conditional Mean in a Linear Regression Model
Authors: Bhoj Dinesh S. , Ahsanullah Mohammad 
Published: May 2007

AbstractConsider the two linear regression models of Yij on Xij, namely Yij = βio + βil Xij + εij,j = 1,2,…,ni, i = 1,2, where εij are assumed to be normally distributed with zero mean and common unknown variance σ2. The estimated value of a mean of Y1 for a given value of X1 is made to depend on a preliminary test of significance of the hypothesis β11 = β21. The bias and the mean square error of the estimator for the conditional mean of Y1 are given. The relative efficiency of the estimator to the usual estimator is computed and is used to determine a proper choice of the significance level of the preliminary test.
Calculating the Sample Size in the Examination of Animals for the Presence of Residues Using Prior Information
Authors: Fuchs K. , Wernecke K. D. , Gölles J. , Köfer J. 
Published: May 2007

AbstractThe council of the European Economic Communities has issued a directive (86/469/EEC) for the examination of animals and fresh meat for the presence of residues of several hormones, drugs and environmental pollutants. The purpose of this paper is to compare the method used by the EEC with a Bayesian approach to calculate necessary sample size using prior information in different situations (assuming Beta‐, Normal‐ and Gamma‐distribution for the prior distribution). It has been shown that the Bayesian approach requires significantly smaller sample sizes for all levels of inspection. Furthermore, an example is given in the examination of pigs for the presence of residues of drugs in pork.
Use of the Maximum Likelihood Method in the Analysis of Phenotypic Stability
Authors: Piepho Hans‐Peter 
Published: May 2007

AbstractThe stability variance is an important estimator of phenotypic stability of genotypes. It may be estimated by method of moments and by maximum likelihood. We demonstrate by Monte Carlo simulation that, given a sufficient number of environments, maximum likelihood estimates (MLE's) are slightly better if ranking of genotypes is the experimenter's major aim. A likelihood ratio test is available for different hypotheses.
On the Estimation of Heritability from Unbalanced Data
Authors: Singh B. 
Published: May 2007

AbstractThe distribution and moments, of ANOVA estimator of heritability are given under unbalanced random model. These expressions are used to investigate the effect of unbalancedness on the bias and variance/MSE of the estimator and also the validity of certain approximations for its variance, numerically. The computed results reveal that the unbalancedness increases both the bias and variance/MSE of the estimator and the Smith‐approximation for the variance of the estimator provides better accuracy.
Application of Discriminant Analysis to the Family Planning Data
Authors: Kumar Kuldeep , Sahai Ajit 
Published: May 2007

AbstractThe main strategy of family planning program in many over populated countries like India, China etc. is to bring down fertility by propagating various family planning devices like Tubectomy, IUD, Vasectomy, conventional contraceptives etc. Data was collected on various socio‐economic and demographic characteristics of the users of these devices. Based on it we have developed a discriminant function for these devices using the methodology given by Kumar (1983).
Estimation of Prevalence and Relative Risk in Repetitive Surveys
Authors: Lamba I. M. S. , Singh P. 
Published: May 2007

AbstractThe Theory of double sampling as proposed by Neyman (1938) and subsequently used for successive sampling by Jesson (1942), Yates (1960), Patterson (1950), Eckler (1955), Kuldroff (1963) and Tikkiwal (1960, 1967) has been explored to develop a general estimator which can be used for estimation of parameters such as mean, ratio or double ratio. A simple case of sampling on two occasions has only been considered but the logic can easily be extended for more than two occasions. The results show that the generalised estimator will be very useful for the applied statisticians.
Statistical Analysis of Nonrectangular Family Data
Authors: Kleffe J. , Province M. A. , Rao D. C. 
Published: May 2007

AbstractMINQUE (Minimum Norm Quadratic Unbiased Estimators) theory is applied to the problem of estimation of variance components in family data (siblings) with variable family size. Using this approach, the traditional iterative maximum likelihood estimators are shown to be asymptotically normal, even though the data come from non‐identical parent distributions. Asymptotic expressions are also obtained for the variance of the MINQUE estimators which hold even if the data are decidedly non‐normal (e.g. a mixture of normals). In the case of normal data, exact small‐sample variance estimates are derived. Simulations demonstrate the fast rate of convergence to asymptotic properties as the number of families increases. These desirable qualities suggest that the easy to compute MINQUE class of estimators may provide a useful alternative method for modelling familial aggregation.
Analysis of Rates Using a Generalized Poisson Regression Model
Authors: Singh Karan P. , Famoye Felix 
Published: May 2007

AbstractIn this paper a generalization of the Poisson regression model indexed by a shape parameter is proposed for the analysis of life table and follow‐up data with concomitant variables. The model is suitable for analysis of extra‐Poisson variation data. The model is used to fit the survival data given in Holford (1980). The model parameters, the hazard and survival functions are estimated by the method of maximum likelihood. The results obtained from this study seem to be comparable to those obtained by Chen (1988). Approximate tests of the dispersion and goodness‐of‐fit of the data to the model are also discussed.
Trend Test for Overdispersed Proportions
Authors: Chen James J. 
Published: May 2007

AbstractThe Cochran‐Armitage test has commonly been used for a trend test in binomial proportions. The quasi‐likelihood method provides a simple approach to model extra‐binomial proportions. Two versions of the score and Wald tests using different parameterizations for the extra‐binomial variance were investigated: one in terms of intercluster correlation, and another in terms of variance. The Monte Carlo simulation was used to evaluate the performance of the each version of the score test and the Wald test, and the Cochran‐Armitage test.The simulation shows that the Cochran‐Armitage test has the proper size only for the binomial sample data, and the test is no longer valid when applied to the extra‐binomial data. The Wald test is more likely to exceed the nominal level than the score test under either intercluster correlation model or variance model. Both score tests performed very well even with the binomial data; the tests control the type I error and in the meantime maintain the power of detecting the dose effects. Based on the design considered in this paper, the two scores test are comparable. The score test based on the intercluster correlations model seems better controlling the Type I error but appears less powerful than that based on the variance model. An example from a developmental toxicity experiment is given.
A Method to Rank Multiple Trait Performance of Biological Entities
Authors: Arunachalam V. 
Published: May 2007

AbstractPerformance of biological entities are defined, in general by k inter‐dependent traits whose error variances are heterogeneous. A method of arriving at “Performance Scores” for the entities along with a multivariate test of significance of k‐trait differences is described. They are used to compute, for each entity, a “Performance Index” on which a ranking of the relative performance is obtained. The improvement in the present method over an earlier one is illustrated with a numerical example.
Multi‐Stage Markov Analysis of Progressive Disease Applied to Melanoma
Authors: Wanek Leslie A. , Goradia Tushar M. , Elashoff Robert M. , Morton Donald L. 
Published: May 2007

AbstractA fundamental research goal in clinical studies of progressive, multi‐stage disease is to understand its natural history and its relationship with prognostic factors. Our current understanding of this topic is based on the use of two‐stage methods for event‐time analysis which neglect intermediate transition information. In contrast, a multi‐stage model utilizes all available data and provides more accurate insight into disease progression. We specify a forward‐flowing multi‐stage Markov model based on the discrete clinical stages of disease. By assuming the process to be Markovian, we avoid unnecessary complications to our numerical estimation procedure. Due to noncontinuous patient monitoring and the chronic nature of progressive disease, heavy right‐ and interval‐censoring exists in the transition data. We develop a modified ECM algorithm to numerically carry out the otherwise complicated parameter estimation for this process. We also identify significant prognostic factors relevant to each transition, along with the relative importance of each prognostic factor. The numerical estimation is stable, and the parameter estimates are maximum likelihood estimates (Meng, 1990). In general our forward‐flowing multi‐stage models provide a flexible framework for the study of the effects of prognostic factors on progression among several stages. We apply our Markov model to a dataset of malignant melanoma patients, and present an inferential discussion. Results from our multi‐stage Markov model provide an improved understanding of melanoma progression.
A Modified Chi‐Square Test for Testing Equality of Two Multinomial Populations Against an Ordering Restricted Alternative
Authors: Oluyede Broderick O. 
Published: May 2007

AbstractA modified chi‐square test for testing the equality of two multinomial populations against an ordering restricted alternative in one sample and two sample cases is constructed. The relation between a concept of dependence called dependence by chi‐square and stochastic ordering is established. A tabulation of the asymptotic distribution of the test statistic under the null hypothesis is given. Simulations are used to compare the power of this test with the power of the likelihood ratio test of stochastic ordering of the two multinomial populations.
Multiple Testing for Detecting Efficient Dose Steps
Authors: Bauer P. , Budde M. 
Published: May 2007

AbstractIn the comparison of various dose levels it can often be assumed that the parameters to be tested follow an order restriction. Two closed multiple test procedures for detecting the highest dose level still providing a shift in the response distribution as compared to the adjacent lower dose level is proposed. One is based on one sided comparisons between neighbouring doses, the other uses Helmert‐type contrast statistics. If a sequence of testing is fixed in advance the multiple test can be suitably modified. The power of the procedures is simulated under the assumption of normally distributed responses for various constellations of the dose means. It is compared with the power of a general Holm‐type procedure discussed in BUDDE & BAUER (1989).
Tests for Equality of Several Binomial Populations Against an Order Restricted Alternative and Model Selection for One‐Dimensional Multinomials
Authors: Oluyede Broderick O. 
Published: May 2007

AbstractThe problem of testing the equality of several binomial population against an order restricted alternative and model selection for one‐dimensional multinomials is studied. Test procedures are proposed. The asymptotic distribution of the test statistics are obtained. Comparisons are made with other test statistics including the likelihood ratio test for stochastic ordering. Also alternatives which does not depend on the distribution of test statistic is proposed.
Tests of Occupancy Rates for Clustered Populations: Case Where the Cluster Sizes are Known
Authors: Vaillant J. 
Published: May 2007

AbstractA modified chi‐squared statistic Z is proposed for testing hypotheses about category occupancy rates for individuals distributed by clusters, when the cluster sizes are observed. This statistic is the Pearson chi‐square statistic based on the individuals' counts divided by 1 +M* whereM* is the mean number of other individuals per cluster per individual. The kind of alternative hypothesis for which the Z‐based test compares favourably in power with the Pearson chi‐square test based on the cluster frequencies is given. However, we prove that this latter test is more powerful than the former one as long as the equidistribution of the random choice vectors is assumed.
Error Normality Testing in a Model of Two‐Way Nested Classification
Authors: Brzeskwiniewicz Henryk , Wagner Wiesław 
Published: May 2007

AbstractThe paper deals with the construction of a normality test for the verification of the assumption about normal distribution of random errors in a fixed model of two‐way nested classification. For this purpose adequate linear transformations and orthogonal observable random vectors have been presented. To the adjusted residual vector obtained after these transformations, the D'Agostino normality test has been applied.
Robustness of Shrunken Predictors in Stratified Populations
Authors: Bouza C. N. 
Published: May 2007

AbstractThe effect of assuming an inadequate superpopulation model in stratified sampling is studied when a shrinkage technique is used. The robustness is close related with the balance of the strata samples. This theoretical problem is present in studies of ecology. The results obtained enable to estimate the abundance of plankton when salinity is the auxiliary variable and expert's advise, in coast experimental stations networks, is used for fixing the superpopulation model.
Tests of Significance for Combined Measures of Plant Stability and Performance
Authors: Nassar R. , Léon J. , Hühn M. 
Published: May 2007

AbstractOf significance in plant breeding is to develop varieties that are stable and have optimum performance, such as high yield, in different target environments. As such, selection should be on a measure that combines stability and performance. In this study, we examine the distributions for three nonparametric and three parametric measures for combined stability and performance and show that the F statistic from an analysis of variance may be used to test for differences among genotypes with regard to these measures. Recommendations are made based on the size and power of the F statistic for each of the measures. Application of the theoretical results is demonstrated using data on 14 varieties of oats in 18 locations.
Assessing the Diagnostic Utility of a Test
Authors: Viana M. A. G. , Farewell V. 
Published: May 2007

AbstractGiven the sensitivity and specificity of a binary test, Lindley and Novick's criterion of diagnostic utility is characterized by the range R of prevalence values in which opposing test results are in correct correspondence to opposing posterior odds on the disease. If the utility range of a new test includes the utility range R of an alternative test, then the new test is uniformly at least as useful as the standard, for all prevalence levels in R. A descriptive interpretation of the criterion is suggested and its statistical analysis is discussed.
Estimation of a Conditional Mean in a Linear Regression Model after a Preliminary Test on Regression Coefficient
Authors: Bhoj Dinesh S. , Ahsanullah Mohammad 
Published: May 2007

AbstractConsider the two linear regression models of Yij on Xij, namely Yij = βio + βij, Xij + Eij = 1, 2,…, ni, i = 1, 2, where Eij are assumed to be normally distributed with zero mean and common unknown variance σ2. The problem of estimating the conditional mean of Y1 for a given value of X1 is considered when it is a priori suspected that β10 = β20 and β11 = β21. The preliminary test estimator is proposed. The exact expressions for the bias and the mean square error of the estimator are derived. The relative efficiency of the new estimator to the usual least square estimator based on the first regression alone is computed and is used to determine the appropriate value of the significance level of the preliminary test β10 = β20 and β11 = β21.
Subset Selection for Normal Means in a Two‐Way Design
Authors: Wu K. H. , Cheung S. H. 
Published: May 2007

AbstractSubset selection procedures for normal means are extended to a two‐way layout. Suppose the same c treatments exist in each of r 2 groups. We first examine the case where a standard treatment is present and introduce a method of selecting, in each of the r groups, a subset which contains all treatments with means at least as large as the mean of the standard treatment. The same subset need not be selected in each group. We then consider the case without a standard treatment and develop procedures for selecting, in each of the r groups, a non‐empty subset which includes the treatment with the largest mean. In each case, the probability of simultaneous correct selection in all r groups is constrained to be at least as large as a specified constant P*. It is demonstrated that the percentage points needed for the implementation of these new procedures can be obtained from already existing tables or algorithms. The use of the procedures is illustrated with the reanalysis of two real data sets (JUGDUTT, 1988; SINGH et al., 1991).
Unbiasedness of the Estimator of the Function of Expected Value in the Mixed Linear Model
Authors: Klaczyński K. , Molińska A. , Moliński K. 
Published: May 2007

AbstractThe traditional method for estimating the linear function of fixed parameters in mixed linear model is a two‐stage procedure. In the first stage of this procedure the variance components estimators are calculated and next in the second stage these estimators are taken as true values of variance components to estimating the linear function of fixed parameters according to generalized least squares method.In this paper the general mixed linear model is considered in which a matrix related to fixed parameters and or/a dispersion matrix of observation vector may be deficient in rank. It is shown that the estimators of a set of functions of fixed parameters obtained in second stage are unbiased if only the observation vector is symmetrically distributed about its expected value and the estimators of variance components from first stage are translation‐invariant and are even functions of the observation vector.
The Tomato Salad Problem Reconsidered
Authors: Fleischer Karlheinz 
Published: May 2007

AbstractOne problem in microscopy is the estimation of the distribution of sizes of cellular particles (assumed to be spheres) from the distribution of their profiles in a thin tissue sample (tomato salad problem). Because the exact formula of the solution derived by BACH (1959) is difficult to evaluate, SCHUBERT/VOSS (1974, 1975) provided an iterative procedure and a random search procedure to approximate the solution. This paper shows that neither of both procedures must be carried out, because there is a simple iterative procedure which gives precise results.
The S‐Distribution: Approximation of Discrete Distributions
Authors: Voit Eberhard O. , Yu Shuiyang 
Published: May 2007

AbstractThe S‐distribution is defined by the ordinary differential equation dF/dX = α(Fg — Fh Fo = F(Xo), where F is the cumulative distribution of the random variable X, and α, g, h, and Fo are parameters. The S‐distribution was recently described in this journal as a tool for the approximation and classification of univariate, unimodal continuous probability distributions. This article shows that the S‐distribution rather accurately models the commonly used univariate discrete distributions.
Weighted Multivariate Regression Estimates Solved by Random Effects Approach
Authors: Volaufová Júlia , Komornik Jozef 
Published: May 2007

AbstractThe paper deals with the random effects model, where the expectation vector and the covariance matrix of the effect influencing the population are to be estimated. The iterated estimator of expectation vector is derived, based on the invariant estimator of the combined covariance matrix, and some of its statistical properties are shown.
Non‐Iterative Least Squares Estimation of Missing Values in Cross‐Over Designs Without Residual Effect
Authors: Subramani J. 
Published: May 2007

AbstractIn the present study an attempt has been made to esttimate several missing values in cross‐over designs. When the observations are missing in a particular pattern, explicit expressions are given for the estimators of the missing values. This procedure is illustrated with the help of a numerical example.