bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Alternative Approach to Estimation in Two‐Phase Sampling Using two Auxiliary Variables
Authors: Smoo J. , Smoo L. N. , Mohanty S. 
Published: May 2007

AbstractUsing two‐phase sampling mechanism, two alternative estimators in the presence of the available knowledge on second auxiliary variable z are considered, when the population mean of the main auxiliary variable × is unknown. The suggested estimators are found to be more eficient than the ratio‐type and regression‐type estimators suggested by KIREGYERA (1980, 1984).
Evaluation of the Kullback‐Leibler Discrepancy for Model Selection in Open Population Capture‐Recapture Models
Authors: Burnham Kenneth P. , Anderson David R. , White Gary C. 
Published: May 2007

AbstractThe objective of this paper is to introduce the logical basis of AIC‐based model selection to persons analyzing capture‐recapture data and to explore the key theorettical aspect of AIC based model selection, for open‐model capture‐recapture, needed for AIC to perform well in this context. Almost all previous work on AIC assumes a Gaussian model; that assumption does not hold for capture‐recapture models. Assuming the Cormack‐Jolly‐Seber model as the true model, we used numerical methods to evaluate the expectation of the log‐likelihood relative to Akaike's target predictive log‐likelihood. The use of this particular target criterion was motivated by the idea of using the Kullback‐Leibler discrepancy for model selection, for which Akaike found the bias of the sample log‐likelihood was asymptoticallyK, whereK= the number of estimated (by MLE) parameters. In some sense, then, AIC is a bias‐adjusted log‐likelihood. For a set of 81 plausible cases, we evaluated this bias almost exactly. The ratio of this bias to the first order theory (bias ofK) and to second order theory (K+ a sample size adjustment) is essentially 1 for these 81 cases. Thus, AIC should be a suitable basis for model selection in open model capture‐recapture.
The Influence of Noise and Non‐Normality on Biserial Correlation Coefficients
Authors: Dünki Rudolf M. 
Published: May 2007

AbstractThe estimation of any biserial correlation coefficient is biased if noise‐like processes are overlaid. We've carried out a computer simulation to study this effect. The effect can be described in a universal way. Additionally, we've varied the type of distribution within the simulation and compared this effect with the effect of noise. Based on this comparison, we've been able to draw some conclusions on the use of biserial correlation coefficients.
On Probability of Heritability Estimates from Full‐Sib Analysis Exceeding Unity Based on Underlying Theoretical Distribution
Authors: Prabhakaran V. T. , Sharma B. S. 
Published: May 2007

AbstractA procedure for obtaining the theoretical probability of heritability (h2) estimates from full‐sib analysis exceeding unity is described. Probability densities useful in the evaluation of these probabilities are also derived. These are used for obtaining the probabilities for several combinations of sire/dam numbers and three levels of h2 (0.10, 0.25, 0.50) assuming additive genetic model and two full‐sibs per mating.
A Locally Most Powerful Test for Detecting a Treatment Effect in the Presence of Nonresponders
Authors: Razzaghi Mehdi , Nanthakumar A. 
Published: May 2007

AbstractA problem that frequently occurs in many biological experiments is when some subjects in the treatment group may be unaffected by the treatment. For this reason, the distribution of responses in the treatment group is expressed as a two‐component mixture, where Lehmann alternatives are used to relate the distributions of affected and unaffected patients. A distribution‐free approach is proposed for testing the hypothesis of no treatment effect against the alternative that a subset of the treated subjects respond to the treatment. The test is locally optimal and structured on the basis of the empirical distribution of the untreated subjects. The properties of the test are discussed and the power function is derived. It is shown that the power increases with the proportion of responders in the experiment.
Conjugate and Other Distributional Methods in Configural Frequency Analysis
Authors: Wood Phillip K. , Sher Kenneth J. , Eye Alexander Von 
Published: May 2007

AbstractConfigural Frequency Analysis (CFA) is being increasingly used by psychologists and other researchers to test for the presence of combinations of categorical variables which occur more frequently or less frequently than expected under a particular model of chance. Configurations which occur more frequently than chance are known as “Types”‐Configurations which are conspicuous by their absence or rarity are known as “Antitypes”. Most configural frequency test theory consists of binomial tests applied to the cells of a cross‐tabulation table. The wide variety of statistical tests described in papers and books on CFA are approximations to the binomial test, due to the computational intensity associated with performing binomial tests directly (VON EYE, 1990b). This paper advocates direct computation of binomial probabilities instead of the usual approximations used in CFA. Mathematical relationships of the binomial distribution with the F and incomplete beta distributions are described which enable the researcher to efficiently compute binomial probabilities using functions available in common statistical software. The classical inference approach adopted by traditional CFA makes it difficult to make conclusions regarding the likely prevalence rates of types or antitypes in the reference population. It is also not possible to exploit additional information about the sample which, while not known precisely, is known with a degree of confidence and can aid in the identification of types and antitypes. A Bayesian conjugate distributions approach based on the incomplete beta distribution is proposed. Bayesian extensions of this model to both classical CFA and a sequential CFA analysis advanced by KIESER and VICTOR (1991) are described.
Adaptive Methods of Trend Detection and Their Application in Analysing Biosignals
Authors: Schack B. , Grieszbach G. 
Published: May 2007

AbstractIn the study estimations of the linear trend and its slope of discrete deterministic and stochastic signals are introduced, which are based on adaptive methods of mean estimation. Because of its simple recursive nature both the trend and the slope estimation may be realized in real time and thus they may be used for on‐line procedures e.g. in monitoring systems of intensive care units. The adaptive estimation functions are self‐learning, that means they are able to adapt automatically to the new conditions after structure changes of the time series and thus may be also used for detecting points of structure fracture. The properties of the adaptive trend‐ and slope estimations are demonstrated by some characteristic simulation examples and the possibilities of their applications for analysing biological signals are investigated in three different medical cases.
A Bootstrap Method for Using Imputation Techniques for Data With Missing Values
Authors: Bello A. L. 
Published: May 2007

AbstractBootstrap is a time‐honoured distribution‐free approach for attaching standard error to any statistic of interest, but has not received much attention for data with missing values especially when using imputation techniques to replace missing values. We propose a proportional bootstrap method that allows effective use of imputation techniques for all bootstrap samples. Five detcnninistic imputation techniques are examined and particular emphasis is placed on the estimation of standard error for correlation coefficient. Some real data examples are presented. Other possible applications of the proposed bootstrap method are discussed.
Testing the Independence of Competing Risks: An Application to the Analysis of Breast Cancer Recurrence
Authors: Assel Bernard , Fourquet Alain , Hoang Thu , Myasnikova Catherine , Yaicovlev Andrej 
Published: May 2007

AbstractAs evidenced by the results of data analysis the contralateral breast cancer recurrence is most likely to originate from subclinical tumor foci that preexist at the time of treatment This raises the natural question as to whether the rtcumnas in the contralateral breast evolve independently of those in the ipsilateral breast It is common knowledge in the theory of competing risks that this problem can not be solved within the nonparaoretric framework. To overcome the obstacle om needs to proceed from a biologically‐based parametric model of tumor latency. We explore this possibility with a model proposed earlier (HOANG et al., 1992). When incorporated into a family of joint distributions with given marginal distributions for the tumor latency times, the model allows testing the independencc hypothesis by the likelihood ratio test Our analysis of data on the ipsilateral and contralateral breast recurrence, though being conclusive only under the model assumptions, suggests that the dependence of risks is negligible for the two typcs of local failure.AMS 1980 subject classification
Measures of Imbalance for Higher Order Designs
Authors: Marqués Lydia Lera 
Published: May 2007

AbstractIn this paper we consider a generalization of the measures of imbalance given by AHRENS and PINCUS (1981) considering the cases: m‐fold hierarchical model and m‐way classification model in order to quantify the degree of imbalance in an unbalanced design. These measures of imbalance satisfy the same properties as those for the one‐way classification model.
Zero‐Altered and other Regression Models for Count Data with Added Zeros
Authors: Heilbron David C. 
Published: May 2007

AbstractOn occasion, generalized linear models for counts based on Poisson or overdispersed count distributions may encounter lack of fit due to disproportionately large frequencies of zeros. Three alternative types of regression models that utilize all the information and explicitly account for excess zeros are examined and given general formulations. A simple mechanism for added zeros is assumed that directly motivates one type of model, here called the added‐zero type, particular forms of which have been proposed independently by D. LAMBERT (1992) and in unpublished work by the author. An original regression formulation (the zero‐altered model) is presented as a reduced form of the two‐part model for count data, which is also discussed. It is suggested that two‐part models be used to aid in development of an added‐zero model when the latter is thought to be appropriate.
Lower Confidence Limits for the Positive Linear Combination of two Variances
Authors: Anbupalam T. , Ponnuswamy K. N. , Srinivasan M. R. 
Published: May 2007

AbstractAn alternative method for determining the approximate lower confidence limits for the positive linear combination of two variances based on an approach similar to BULMER (1957) has been proposed. The probability coverage of the proposed alternative limits has been compared with the other existing methods.
Proportionality of Diagonal Blocks with Blockwise Independence
Authors: Gupta A. K. , Chao C. C. 
Published: May 2007

AbstractIn this paper, the tests of similarities among group covariance matrices and the differences among block covariance matrices within a group under repeated measurement model are studied. There are nine hierarchical nested structures of covariance matrices which have been tested. The likelihood ratio tests have been derived for these nine hierarchically structured models. An algorithm for determining the numerical solution of the corresponding maximum likelihood equations is also given.
On Countable Mixtures of Bivariate Binomial Distributions
Authors: Papageorgiou H. , David Katerina M. 
Published: May 2007

AbstractA unified treatment is given for mixtures of bivariate binomial distributions with respect to their index parameter(s). The use of probability generating functions is employed and a number of interesting properties including probabilities, factorial moments, factorial cumulants and conditional distributions are derived. Five classes of such mixtures are examined and several well known bivariate discrete distributions are used as illustrative examples. Biological applications are indicated including the fit of three bivariate distributions to an actual set of human family data.
Testing Multivariate Normality in Series Experiments of Incomplete Blocks by Generalized Shapiro‐Wilk Test
Authors: Wagner Wieslaw , Brzeskwiniewicz Henryk 
Published: May 2007

AbstractIn this paper, we verify our assumption about multivariate normality in series experiments of incomplete blocks by the generalized Shapiro‐Wilk test. This test utilizes transformations of mean yields of the investigated varieties and the generated random variables according to Durbin's randomization principle.
Method of Construction of Partial Tetra‐Allel Crosses and Its Analysis
Authors: Ponnuswamy K. N. , Natarajan S. , Anbupalam T. 
Published: May 2007

AbstractThe present paper deals with a method of construction of Partial Tetra‐allel Crosses (PTAC) through Doubly Balanced Incomplete Block Designs (DBIBD) and its analysis by explicit expressions of symmetric sums approach. We have also established an inequality for the estimability of individual design components of variance in the Hinkelmann's double crosses model. A simulation study has been carried out to compare the precision of the estimates of genetic components of variance based on the two different PTAC with same experimental size. A list of DBIBD's will be useful in the construction of PTAC has given in the appendix.
Computation of Some Exact Properties of Wald's Sprt when Sampling from a Class of Discrete Distributions
Authors: Young L. J. 
Published: May 2007

AbstractAn algorithm is developed for determining exact values of the Operating Characteristic and Average Sample Number functions for Wald's Sequential Probability Ratio Test (SPRT) when observations are drawn from a discrete, Koopman‐Darmois density with positive probability on the nonnegative integers. The sample size distribution and percentiles of sample size are also considered. An example is given for the negative binomial distribution.
Testing Equivalence Simultaneously for Location and Dispersion of two Normally Distributed Populations
Authors: Bauer Peter , Bauer Michael M. 
Published: May 2007

AbstractIn clinical trials with an active control usually therapeutical equivalence of a new treatment is investigated by looking at a location parameter of the distributions of the primary efficacy variable. But even if the location parameters are close to each other existing differences in variability may be connected with different risks for under or over treatment in an individual patient.Assuming normally distributed responses a multiple test procedure applying two shifted one‐sided t‐tests for the mean and accordingly two one‐sided F‐tests for the variances is proposed. Equivalence in location and variability is established if all four tests lead to a rejection at the (one‐sided) level α. A conservative procedure “correcting” the t‐tests for heteroscedasticity is derived. The choice of a design in terms of the global level α, the global power, the relevant deviations in the population means and variances, as well as the sample size is outlined. Numerical calculations of the actual level and power for the proposed designs show, that for balanced sample sizes the classical uncorrected one‐sided t‐tests can be used safely without exaggerating the global type I error probability.Finally an example is given.
The Estimation of Skewness and Kurtosis of Random Effects in the Linear Model
Authors: Teuscher F. , Herrendörfer G. , Guiard V. 
Published: May 2007

AbstractGeneralising the ANOVA method of estimating variance components in mixed linear models a simple procedure is presented to estimate skewness and kurtosis of the distributions of the random effects of the model. For the model II of a one‐way classification this procedure is demonstrated explicitly.
Pivotal Quantities for the Substitution Values of Various Entries, Each in Place of its Best Contender, Given the Standard Analysis of Variance Model
Authors: van Aarde I. M. R. 
Published: May 2007

AbstractConsider a yield trial involving entries such as the apple cultivars named “Cox's Pippin”, “Granny Smith”, “Rome Beauty”, etc., where interest is in mean performance, the higher the better. For any of the entries, say Granny Smith, an unknown “X” names the best of the other entries. The substitution value of Granny Smith in place of its best contender, is given in terms of expectations by E (yield of Granny Smith) — E (yield of X). This parameter takes on any real value. A system of interval estimates of the parameter is shown to arise in the standard analysis of variance case. In fact, there arises such a system for each of the entries involved. These systems supersede procedures recently discussed in this journal by VAN DER LAAN (1992).
A Comparison of Robust Estimators in the Presence of Arbitrarily Right Censored Data
Authors: Khan‐Malek R. C. , Magel Rhonda C. 
Published: May 2007

AbstractRobust estimation of a location parameter is considered when the data from an unknown symmetric population are subject to arbitrary right‐censorship. Comparisons are made between various M‐estimators, several L‐estimators (trimmed means), and the Kaplan‐Meier median. Ten sampling distributions, two uniform censoring distributions, and three sample sizes are examined. A Cauchy censoring distribution is also considered when the sample size is equal to twenty for each of the ten sampling distributions. Performance is based on the estimated mean square error.
On Choosing Among Several Experimental Treatments and a Control
Authors: Chen Pinyuen 
Published: May 2007

AbstractThe two classical selection approaches in comparing experimental treatments with a control are combined to form an integrated approach. In this integrated approach, there is a preference zone (PZ) and an indifference zone (IZ), and the concept of a correct decision (CD) is defined differently in each of these zones. In the PZ, we are required to select the best treatment for a correct decision (CD1) but in the IZ, we define any selected subset to be correct (CD2) if it contains the best treatment among all the experimental treatments and the controlled treatment. We propose a single‐stage procedure R to achieve the selection goals CD1 and CD2 simultaneously with certain probability requirements. It is shown that both the probability of a correct decision under PZ, P(CD1 | PZ), and the probability of a correct decision under IZ, P(CD2 | IZ), satisfy some monotonicity properties and the least favorable configuration in PZ and the worst configuration in IZ are derived by these properties. We also derive formulas for the probabilities of correct decision and provide a brief table to illustrate the magnitude of the procedure parameters and the common sample sizes needed for various probability requirements and configurations.
Relationships Between Kendall's Coefficient of Concordance and a Nonparametric Measure of Phenotypic Stability with Implications for the Consistency in Rankings as Affected by Variance Components
Authors: Hühn M. , Piepho H. P. 
Published: May 2007

AbstractFor a two‐way classification with rows (= genotypes) and columns (= environments) relationships between Kendall's coefficient of concordance W and a nonparametric measure of phenotypic stability S (= variance among the ranks over the environments) are presented.This provides an analogy between Wricke's ecovalence and the sum of squares of genotype‐environment interactions on the parametric side and S and Kendall's W on the nonparametric side: S is a genotype's contribution to the discordance (1 — W) in the data set, while Wricke's ecovalence is a genotype's contribution to the genotype‐environment interaction sum of squares.Genotype x environment interactions may lead, but must not necessarily lead to non‐identical rank orders of the genotypes in different environments (crossover versus noncrossover interactions). When does interaction become rank interaction?The similarity of the rank orders (measured by W) can be approximately expressed by the ratio of the genotypic variance and sum of genotypic variance plus residual variance. For the consistency of rankings this relation leads to an approximate test of significance which is based on variance components.Finally, a numerical example is given using grain yield data of 20 genotypes (varieties) of winter wheat in 10 environments (locations) from German registration trials.
An Exact Unconditional test for the Hardy‐Weinberg Equilibrium
Authors: Haber Michael 
Published: May 2007

AbstractAn exact test based on the unconditional distribution of a test statistic for the Hardy‐Weinberg equilibrium is introduced. This test is usually more powerful and requires less extensive tabulations compared with the (ordinary) exact conditional test. Tables of critical values are given for N = 5(1)100 and α =·10, ·05, ·01. Sample sizes required to attain a given power with the unconditional and conditional exact tests are provided.
Body Proportions of the Infant
Authors: Gerver W. J. M. , de Bruin R. , Blanco C. E. 
Published: May 2007

AbstractGrowth is usually evaluated by single measurements which reflect only that direction of growth. At birth the relationship between measurements is often considered as Rohrer's Ponderal Index which describes weight in relation to length but can be misleading especially when considering premature infants. We propose that it is more appropriate to study the relationship of different anthropometric measurements to define normality and thereby create limits from which abnormalities in growth may be diagnosed.Weight, crown heel length, crown rump length and head circumference were measured in 305 healthy infants, at 26–42 gestational weeks to obtain reference values. Subischial leg length was calculated. A multivariate model was made which could be useful to distinguish the form and shape of an infant and to identify abnormalities in growth.
A Mixed Model for Binary Data: The Modified X2‐test
Authors: Hecker Hartmut , Wiese Birgitt 
Published: May 2007

AbstractIn this paper, a statistical model for clinical trials is presented for the special situation that a varying and unstructered number of binary responses is obtained from each subject. The assumptions of the model are the following: 1.) For each subject there is a (constant) individual Bernoulli parameter determining the distribution of the binary responses of this subject. 2.) The Bernoulli parameters associated with the subjects are realizations of independent random variables with distributions Pg in treatment group g(g = 1, 2, …, G).3.) Given the value of the Bernoulli parameter, the observations are stochastically independent within each subject.Under these assumptions, a test statistic is derived to test the hypothesis H0:E(P1) = E(P2) = … = E(PG). It is proven and demonstrated by simulations, that the test statistic asymptotically (i.e. for a large number of subjects) follows the X2‐distribution.
Unbalanced Repeated Measures with Random Coefficients
Authors: Wu Yuan , McGilchrist Clyde A. 
Published: May 2007

AbstractIn a random coefficient repeated measures model, the regression coefficients relating the observations to some underlying variable, such as time, are themselves taken to be random distributed over experimental units. In this paper, a general approach to repeated measures analysis is extended to this wider model. In the model three specific error structures for the random regression coefficients have been studied,viz, the random coefficients variance matrix is considered to be (i) diagonal, (ii) proportional to the identity matrix and (iii) completely general. An example will be analyzed to illustrate the procedure.