bluuebunny/crossref_metadata_2025_split huggingface dataset:
Precision of Estimation of the Treatment Contrasts and the Intra‐block Matrix of Block Designs
Authors: Gupta S. C. 
Published: June 2010

AbstractIn many practical situations the experiment is conducted using a block design, and it is desired to estimate a given set of contrasts with variances none of which is greater than a corresponding set of specified variances: In the present paper the form of the intra‐block matrix of a design is, therefore, derived for such situations. Usefulness of the results given is illustrated with the help of examples. The construction of two‐plot block designs is shown to be particularly straightforward.
Simultaneous Analysis of Variance Tests Using Friedman's χ Statistics
Authors: Jensen D. R. 
Published: June 2010

AbstractThis study develops simultaneous tests for shifts in marginal distributions pertaining to complete two‐way multivariate data not having Gaussian errors. Gerig's (1969) statistics based on ranks are used, a special case yielding Friedman's (1937) χ Statistics statistics for the one‐dimensional marginal distributions. Exact α‐level procedures are not now available. Instead, approximate but conservative tests are constructed using available tables together with suitable probability inequalities. A variational result permits unlimited tests even when these are suggested by the data. The procedures are illustrated using data from field studies with peanuts; some extensions are noted; and selected percentage points are provided to support some of the tests.
Methods for Fitting the Right‐Truncated Weibull Distribution to Life‐Test and Survival Data
Authors: Wingo Dallas R. 
Published: June 2010

AbstractThis paper develops mathematical and computational methods for fitting, by the method of maximum likelihood (ML), the two‐parameter, right‐truncated Weibull distribution (RTWD) to life‐test or survival data. Some important statistical properties of the RTWD are derived and ML estimating equations for the scale and shape parameters of the RTWD are developed. The ML equations are used to express the scale parameter as an analytic function of the shape parameter and to establish a computationally useful lower bound on the ML estimate of the shape parameter. This bound is a function only of the sample observations and the (known) truncation point T. The ML equations are reducible to a single nonlinear, transcendental equation in the shape parameter, and a computationally efficient algorithm is described for solving this equation. The practical use of the methods is illustrated in two numerical examples.
A Comparison Between a Parametric and a Non‐Parametric Method of Estimating the Percentiles of a Difference
Authors: Taylor Jeremy M. G. 
Published: June 2010

AbstractA parametric and a nonparametric method of estimating the percentiles of a difference are compared. The parametric method assumes an underlying normal distribution and the nonparametric method estimates the distribution from the residuals. Applications to immunology are discussed.
A Comparative Statistical Analysis of Genetic Distances. I ‐ Estimation of Distances and of their Variances: Distribution of the Estimators
Authors: Katz M. 
Published: June 2010

AbstractWe present a Monte‐Carlo simulation analysis of the statistical properties of absolute genetic distance and of Nei's minimum and standard genetic distances. The estimation of distances (bias) and of their variances is analysed as well as the distributions of distance and variance estimators, taking into account both gamete and locus samplings. Both of Nei's statistics are non‐linear when distances are small and consequently the distributions of their estimators are extremely asymmetrical. It is difficult to find theoretical laws that fit such asymmetrical distributions. Absolute genetic distance is linear and its distributions are better fit by a normal distribution. When distances are medium or large, minimum distance and absolute distance distributions are close to a normal distribution, but those of the standard distance can never be considered as normal. For large distances the jack‐knife estimator of the standard distance variance is bad; another standard distance estimator is suggested. Absolute distance, which has the best mathematical properties, is particularly interesting for small distances if the gamete sample size is large, even when the number of loci is small. When both distance and gamete sample size are small, this statistic is biased.
General Analysis of Covariance Models for the Split Block and Related Block Designs
Authors: Monlezun C. J. , Blouin D. C. 
Published: June 2010

AbstractExact test statistics and confidence intervals for a general split block ANOCOVA model are derived. With a single covariate, each statistic for testing main effect A, main effect B, and the AxB interaction has one less numerator degree of freedom than its counterpart in the ordinary ANOVA without a covariate. Sufficient conditions on the model parameters which allow these lost numerator degrees of freedom to be regained are given, as are exact statistics and confidence intervals for the corresponding reduced models. A note of caution is offered when constructing test statistics for reduced versions of the general model using the method of generalized least squares. General analysis of covariance models for two other block designs are presented.
Estimation and Tests of Mendelian Inheritance in Small Samples of Human Families
Authors: Bchini M. , Goux J. M. 
Published: June 2010

AbstractEstimation of parameters in human genetics is usually performed by Maximum Likelihood methods for large samples relying on asymptotic results. With the aim of evaluating the importance of the bias resulting from sample size, two sets of human family data have been analysed. The bias is not very important, but programs have been elaborated, giving better estimations. The tests of hypotheses from these estimations have also been considered.
Small Sample Bias and Variance of Lincoln‐Petersen and Bailey Estimators
Authors: Bonett D. G. 
Published: June 2010

AbstractThe Lincoln‐Petersen and Bailey estimators of an unknown population size were compared in a computer simulation of capture‐recapture sampling with replacement from small populations. The Bailey estimator was negatively biased and had smaller variance than the Petersen estimator. The Petersen estimator tended to be positively biased. The Lincoln‐Petersen variance estimator tended to be positively biased while the Bailey variance estimator was negatively biased.
On the Estimation of the Mean with Random Number Of Observations. (Comments on a Paper by M. Singh and V. K. Gupta)
Authors: Lindqvist B. G. 
Published: June 2010

AbstractIn this paper we study the problem of estimating the mean when the number of observations taken on each unit is a random variable. The estimator proposed by M. Singh and V. K. Gupta (1980) is discussed and modified. It is argued, however, that the arithmetic mean is a more appropriate choice of estimator.
A Procedure for Clustering Means of Unequal‐Sized Samples in ANOVA
Authors: Neumann H. 
Published: June 2010

AbstractThe proposed procedure “SECOLICO” is based on the sequential construction of linear contrasts. After an analysis of variance the procedure is able to classify the treatments represented by mean values of unequal‐sized samples made at random into distinguishable groups. For the purpose of illustrating the procedure “SECOLICO” a simplified example is given.
Age Preference in Sexual Choice and the Basic Reproduction Number of HIV/AIDS
Authors: Knolle Helmut 
Published: June 2010

AbstractIt is shown that the concept of the basic reproduction number R, which has arisen in the theory of acute air‐borne infections, needs essential modifications when it is applied to chronic, sexually transmitted infections. A new definition of R is given, which takes into account the aging of the infected person and its partners during the course of the infection. Thereby the impact of age preference in sexual choice on the spread of HIV/AIDS can be quantified with little computational effort. It turns out that for low‐risk groups, R is by far smaller than conventional calculation would suggest, and that the frequency of partnerships between people with distant ages exerts greater influence on R than the infection probability per contact and the number of successive partnerships within the infectious period. It is suggested that a high incidence of partnerships between older men and very young women is a major reason for the AIDS epidemic in Africa.
Comparison of Two Exponential Distributions
Authors: Bristol David R. , Desu M. M. 
Published: June 2010

AbstractThe problem of comparing two treatments with respect to a continuous response variable, following an exponential distribution, is considered. It is assumed that a relevant comparative clinical trial is planned to either (i) test that the effect of one treatment is larger or not much smaller than that of the other, (ii) test whether the treatments are equivalent, or (iii) select the better treatment when they may not be equivalent and otherwise assert equivalence. Statistical procedures for achieving these goals are proposed and description of the required sample sizes is given with the necessary tables.
Small Sample Exact Power of the Range from a Symmetric Multinomial Distribution
Authors: Bennett B. M. , Holmes E. N. 
Published: June 2010

AbstractThe authors (1968) have previously given tables of the percentage points of the range (= w) of r samples from a multinomial distribution of r cells each with probability r−1, r = 2(1)10. This paper presents the small sample power of w for r = 2(1)6 under alternatives H to the null hypothesis H0 of a symmetric multinomial distribution. The alternatives are of the form H: pi=r−1+c1n1/2 i = 1, …, r, where the c's are such that ∑ci=0, i. e. H consists of a set of multinomial probabilities approaching H0 of 0(n−1/2). Table 1 gives values of the power of the range test, and Table 2 consists of selected comparisons of the power with respect to the X2 index of dispersion.
A Comparative Study on Approximative and Exact Tests in Two‐dimensional Contingency Tables
Authors: Enke H. , Bismarck M. 
Published: June 2010

AbstractOften the statistician is faced with the necessity of analysing a × b‐contingency tables which are partially very sparsely occupied beyond a very‐well occupied “kernel”. The weak part shall not be due to misclassification or miscalculation. By the aid of a fictive example an analysing strategy is discussed combining exploratory and confirmatory elements and comparing the use of Exact Fisher Test and Freeman‐Halton Test with Pearson's X2 and Fisher's Maximum Likelihood Quotient Statistics Y2.
Time‐variant Spectral Analysis of Electroencephalograms
Authors: Volke H.‐J. , Dettmar P. 
Published: June 2010

AbstractMethods usually applied for analysis of biological signals, as a rule, presuppose stationarity of the time series under investigation. Such kind of approach, however, will certainly be connected with an essential loss of information as dynamic properties of the system are excluded from consideration a priori. The method of time‐variant spectral analysis of the EEG suggested in this paper, on the contrary, provides time‐relatedchanges of the spectral coefficients. Additinonally, a decomposition of the EEG into its natural componentsas well as a combined EEG‐/EP‐analysis is enabled.
Logistic Transformation Regression Estimators of Heritability
Authors: Prabhakaran V. T. , Jain J. P. 
Published: June 2010

AbstractThree logistic transformation estimators are proposed to deal with the problem of inadmissible estimates of intra‐sire regression heritability (h2). These are compared with the estimators of earlier workers. The logistic transformation estimator with exponential correction is shown empirically to give more consistent estimates.
Prediction in Random Coefficient Regression Models
Authors: Bondeson Jan 
Published: June 2010

AbstractMuch attention has been given to the problem of predicting future observations for some individual within a random coefficient regression (RCR) model, using the previous observations on that individual as well as the information from the rest of the data material. In this paper, the literature on this subject is critically reviewed and new methods of linear prediction are proposed for the general RCR model. Exact results are derived for the mean squared errors of some predictors in a special case, but this is not possible in the general RCR model when its parameters are not known. In this model, the old and new predictors are compared in a simulation study, and further illustrated by prediction in a medical data material.
Comparison of Decontaminants Over Arbitrary Dose Ranges Using Dose‐Response Survival and Contamination Experiment Data
Authors: Trajstman A. C. 
Published: June 2010

AbstractTRAJSTMAN (1989) defined indices for assessing the performance of decontaminants used in the culturing of a micro‐organism. One index used was the probability that the micro‐organism of interest survives without being contaminated by foreign micro‐organisms. In TRAJSTMAN (1989) attention was given to the maximum of this probability. In the present paper the performance of the decontaminant is assessed by considering, over dose ranges of interest, the mean probability that the organism survives without contamination.
Estimation of Degree of Dominance and Gene Frequency in Random Mating Populations
Authors: Dhillon B. S. 
Published: June 2010

AbstractAn estimate of mean degree of dominance of genes affecting a quantitative trait in random mating populations cannot, in general, be obtained with the available methodology. Three mating designs have been proposed for the estimation of mean degree of dominance and mean gene frequency over loci. These designs are based on related S1, full‐sib and half‐sib families and on the families developed by simultaneous selfing and crossing the parents in either nested or factorial manner.
An Almost Unbiased Correlation Estimator of Heritability
Authors: Verma D. , Jain J. P. 
Published: June 2010

AbstractAssuming normality of observations an almost unbiased estimator of heritability (h2) for half‐sib data design has been obtained. Results of bias in the conventional estimator for several combinations of sire/dam number and h2 are discussed for the balanced situation.
Monte Carlo Confidence Bounds for Inter‐Event and Nearest Neighbour Distribution Functions of Random Points in a Square
Authors: Koen Chris 
Published: June 2010

AbstractRandom distributions of N points in a square were simulated by computer, interpoint and nearest neighbour distances were calculated and corresponding distribution functions found. The results of 1000 repetitions of this process were used to find approximate 90, 95 and 99 % confidence bounds for the distribution functions. Calculations were performed in the range N = 10 to N = 350. Results are given in tabular form.
A Related Problem in Bioavailability/Bioequivalence Studies — Estimation of the Intrasubject Variability With a Common CV
Authors: Chow Shein‐Chung , Tse Siu‐Keung 
Published: June 2010

AbstractThe problem of characterizing the intrasubject variability in bioavailability/bioequivalence studies when the intrasubject variability differs from subject to subject is considered. To account for the heterogeneity of the intrasubject variability, a conditional random effects model that assumes a common coefficient of variation (CV) for each subject is proposed. This common CV is used to assess the intrasubject variability. Several procedures are developed for estimating the common CV. The proposed estimators are compared asymptotically. An extensive simulation study is conducted to study their finite sample performances.
A Different Approach to the Analysis of Person‐Years Data
Authors: Le Chap T. 
Published: June 2010

AbstractThe observed mortality of a group of individuals often needs to be compared with that expected from the death rates of the national population, with allowance made for period, age, sex and race. This is achieved by the person‐years or subject‐years method. A different approach is proposed which leads to new test statistics and confidence intervals which may be used with small samples.
A New Approach to Estimation of Relative Risks Due to Two or More Risk Factors Under Multiple Matching in Case‐Control Studies
Authors: Ejigou Ayenew 
Published: June 2010

AbstractThe analysis of case‐control data using logistic regression involves making some critical model assumptions. In the analysis of matched data for which the values of the matching variable(s) are unavailable, use of logistic regression presupposes that odds ratios are homogeneous over the levels of the matching variable (s); this is done since logistic regression cannot, at present, provide a way of testing for the homogeneity of odds ratios.In our model, it is possible, as in logistic regression, to estimate odds ratios, check the multiplicativity of effects, or test for the significance of association. In addition, and unlike logistic regression, our model enables one to test for the homogeneity of the odds ratios over the levels of the matching variable(s) even when explicit information is not available on the matching variable(s).
Tests of Hypotheses in Density Follow‐Up Studies
Authors: Bennett B. M. 
Published: June 2010

AbstractThis paper discusses statistical methods for situations in which two or more (k + 1) exposure groups, including a reference one, are compared with respect to rate ratios, or incidence density ratios (IDR) (e.g. MIETTINEN, 1976). Data are assumed available on population times (PT) experienced in the exposure groups.The logit transformation or its generalizations (e.g. Cox, 1970 are utilized in order to obtain suitable tests for the hypotheses involved. Three examples illustrate these methods, including approximate and exact confidence limits for the IDR's (k = 1). For the case of (k + 1) groups a linear contrast for the hypothesis of a trend is derived as well as its standard error.
Tables for Studentized Multivariate Maximum Ranges and their Application by Maximum Range Tests
Authors: Royen Th. 
Published: June 2010

AbstractThe maximum range test as a closed multiple test procedure for all pairwise comparisons of several independent samples was described by ROYEN (1988) and (1989). For the number k of samples, the dimension d and some suitable degrees of freedom v between 10 and ∞ the following tables are contained in this paper:bounds for the studentized multivariate maximum range for 2≦k≦12, 2≦d≦6 and the familywise type I error rates α = 0.01 (table 1.1) and α = 0.01 (table 1.2), furthermore bounds for the studentized univariate maximum range for 2≦k≦15 and α = 0.01 (table 2). The corresponding bounds with α = 0.05 for univariate maximum ranges have been tabulated in ROYEN (1989).Some examples for the application of these bounds by maximum range tests are included.
A Decomposition of Linear Diagonals‐Parameter Symmetry Model for Square Contingency Tables with Ordered Categories
Authors: Tomizawa Sadao 
Published: June 2010

AbstractFor square contingency tables with ordered categories, this paper gives a decomposition for AGRESTI'S (1983) linear diagonals‐parameter symmetry (LDPS) model into GOODMAN'S (1979) diagonals‐parameter symmetry model and the linear diagonals‐parameter marginal symmetry model introduced in this paper. It is also pointed out that the likelihood ratio statistic for the LDPS model is equal to the sum of those for the decomposed two models.
URegression and Survival Analysis
Authors: Nagelkerke N. J. D. , Plummer F. A. 
Published: June 2010

AbstractThe problem of survival analysis with covariates when observations are subject to irregular interval censoring is considered. A proportional hazards model is assumed. A pseudo likelihood based on the comparison of all pairs of individuals whose survival times can be ordered, is proposed. Statistical properties of the maximum pseudo likelihood estimator and its score test are explored. Several examples are presented, one of which is an examination of the effect of covariates on HIV seroconversion.
The Model Selection for One‐Dimensional Multinomials
Authors: Jolayemi E. Tejumola 
Published: June 2010

AbstractThe use of the common test statistics in obtaining appropriate null hypothesis or model for one‐dimensional qualitative data is re‐examined. We propose alternatives which do not depend on the distributional approximation of the test statistics. We examine these alternatives using data from a medical set‐up and conclude that these alternatives are adequate for descriptive purposes which is what is usually desirable for such data. An extra caution is required however, if estimates are of importance especially for such data which depends on a predictor variable.
Models Having Structure of Various Symmetry Plus Diagonal Exponential Frequencies Applied to Data from Endometrial Cancer Study
Authors: Tomizawa Sadao 
Published: June 2010

AbstractFor the analysis of square contingency tables with ordered categories, this note proposes some models having the structure of symmetry or asymmetry plus the main‐diagonal exponential frequencies. These models are applied to the data from the Los Angeles endometrial cancer study in BRESLOW and DAY (1980, p. 185).
A Method for the Estimation of Crop Varieties Stability
Authors: Martynov S. P. 
Published: June 2010

AbstractA method for point and interval stability estimation of crop varieties or hybrids is suggested. The method makes it possible to estimate the ability of a genotype to combine high potential yield with minimal yield reduction under unfavourable conditions. The stability measure is a weighted sum of standardized deviations of a trait value from the mean in each trial (location or year) of an ecological experiment. The application of the method is illustrated by a numerical example.
Optimum Biased Spring Balance Weighing Designs Under Equal Correlations of Errors
Authors: Ceranka B. , Katulska K. 
Published: June 2010

AbstractThe paper deals with the problem of estimating the individual weights of objects under a biased spring balance weighing design with equal correlations of errors in the model. A lower bound for the variance of each of the estimated weights resulting from this biased spring balance weighing design is obtained and a necessary and sufficient condition for this lower bound to be attained is given. The incidence matrix of a BIB design has been used to construct optimum biased spring balance weighing designs.
An Alternative Approach for the Assessment of Bioequivalence Between Two Formulations of a Drug
Authors: Chow Shein‐Chung , Shao Jun 
Published: June 2010

AbstractThe problem of the assessment of bioequivalence between a test formulation (T) and a reference formulation (R) of a drug using a two‐way crossover experiment is considered. To claim bioequivalence between two formulations, it is required by the United States Food and Drug Administration (FDA) to demonstrate that the true ratio of means μT/μR of pharmacokinetic parameters of concern falls within some reasonable limits (e.g., (80%, 120%)) with certain assurance. A commonly used approach is to construct an approximate 90% confidence interval for μT/μR and compare it with (80%, 120%). In this paper, an exact approach according to the FDA's criteria is proposed. The proposed procedure is derived by constructing an exact confidence region (an ellipse) for (μR, μT) and comparing it with the region bounded by μT = 0.8 μR and μT = 1.2 μR. Bioequivalence is concluded if the ellipse is within the critical region.