bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Determination of Axis Lengths ofDunaliellafrom the Image Analysis of TEM Sections through Randomly Orientated Cells
Authors: Roessler Jochen , Jones Tim , Bérubé Kelly Ann 
Published: May 2007

AbstractWe present a method which improves the determination of geometric parameters of unicellular organisms with prolate ellipsoid geometry, such asDunaliella. Approximately determined axis lenghts were obtained from transmission electron micrographs of ultrathin sections of microalgae. The density functions generated by these ‘axis lengths’ have poles which can be used to determine the short half axis length and ratio of axis lengths of the ellipsoid in order to determine the true cell volume.
Sensitivity Profiles of Diversity Indices
Authors: Izsák János 
Published: May 2007

AbstractThe author previously described special sensitivity measures for diversity indices. These measures make possible on analytical treatment of the index sensitivity. In the article a detailed analysis of sensitivity properties of the Hurlbert indices is presented. In the first step the abundances nk were sequenced in an increasing order. Plotting the sensitivity against nk or log nk, essential sensitivity properties can be observed. For example, one can study the sensitivities to changes in the dominant and subdominant abundances, the site of the “nearly neutral” abundance and the site of local sensitivity maxima in the domain of the relatively rare categories. The observations are supported by mathematical reasoning.
On the Properties of GEE Estimators in the Presence of Invariant Covariates
Authors: Spiess Martin , Hamerle Alfred 
Published: May 2007

AbstractSince Liang and Zeger (1986) proposed the ‘generalized estimating equations’ approach for the estimation of regression parameters in models with correlated discrete responses, a lot of work has been devoted to the investigation of the properties of the corresponding GEE estimators. However, the effects of different kinds of covariates have often been overlooked. In this paper it is shown that the use of non‐singular block invariant matrices of covariates, as e.g. a design matrix in an analysis of variance model, leads to GEE estimators which are identical regardless of the ‘working’ correlation matrix used. Moreover, they are efficient (McCullagh, 1983). If on the other hand only covariates are used which are invariant within blocks, the efficiency gain in choosing the ‘correct’ vs. an ‘incorrect’ correlation structure is shown to be negligible. The results of a simple simulation study suggest that although different GEE estimators are not identical and are not as efficient as a ML estimator, the differences are still negligible if both types of invariant covariates are present.
On the Use of the Upper Confidence Limit for the Variance from a Pilot Sample for Sample Size Determination
Authors: Kieser Meinhard , Wassmer Gernot 
Published: May 2007

AbstractIn a recent paper, Browne (1995) investigated the use of a pilot sample for sample size calculation. Monte Carlo simulations indicated that using a 100 · (1 — γ) per cent upper one‐sided confidence limit on the population varíance σ2 leads to a sample size that guarantees the planned power with a probability of at least 1 − γ. The purpose of this paper is to get further insight into the results of Browne by analytical considerations. Furthermore, the expected power is investigated when applying the strategy and recommendations for the choice of the pilot sample size are given.
The Multiple Poisson Distribution, Its Characteristics and a Variety of Forms
Authors: Wimmer Gejza , Altmann Gabriel 
Published: May 2007

AbstractThe multiple Poisson distribution, known under different names, such as generalized Poisson, compound Poisson, composed Poisson, stuttering Poisson, Poisson power series, Poisson‐stopped sum distribution, etc., plays an important role in discrete distribution theory. Here we want to show its basic characteristics, the variety of its forms and specify the generalizing distributions.
Use of Generalised Linear Mixed Models for the Analysis of Clustered Survival Data
Authors: Yau K. K. W. , McGilchrist C. A. 
Published: May 2007

AbstractData from a litter matched tumorigenesis experiment are analysed using a generalised linear mixed model (GLMM) approach to the analysis of clustered survival data in which there is a dependence of failure time observations within the same litter. Maximum likelihood (ML) and residual maximum likelihood (REML) estimates of risk variable parameters, variance component parameters and the prediction of random effects are given. Estimation of treatment effect parameter (carcinogen effect) has good agreement with previous analyses obtained in the literature though the dependence structure within a litter is modelled in different ways. The variance component estimation provides the estimated dispersion of the random effects. The prediction of random effects, is useful, for instance, in identifying high risk litters and individuals. The present analysis illustrates its wider application to detecting increased risk of occurrence of disease in particular families of a study population.
Some Tests on a Single Change in Independent Copies of Short Sequences: A Cancer Diagnostic Study
Authors: Faupel M. , Hsu Y. S. , Jin J. , Long D. 
Published: May 2007

AbstractTesting a single change in mean or variance when the observations arenindependent short sequences has not been fully researched in the literature. Two statistical tests, maxtand modified likelihood radtio, are used in this study. Power comparisons and examples are also given. As expected the modified likelihood ratio test yields the largest power. However, with the assumptions we have in the examples, the maxtmight be the optimal method to use. The maxtmay prove useful in assessing departures from the steady state in which the data are short sequences of diagnostic information.
Parameter Estimation of the Gompertz Population
Authors: Chen Zhenmin 
Published: May 2007

AbstractThe Gompertz distribution has been used to model human mortality and fit actuarial tables. In the recent years, this distribution has been studied by some authors. The MLE estimates for the parameters of the Gompertz distribution were disucssed by GARG, RAO and REDMOND (1970). The purpose of this paper is to develop an exact confidence interval and an exact joint confidence region for the parameters of the Gompertz distribution.
Shrinkage Pre‐Test Estimator of the Intercept Parameter for a Regression Model with Multivariate Student‐t Errors
Authors: Khan Shahjahan , Md. Ehsanes Saleh A. K. 
Published: May 2007

AbstractIn the presence of an uncertain prior information about the value of the slope parameter, the estimation of the intercept parameter of a simple regression model with a multivariate Student‐t error distribution is investigated. The unrestricted, restricted and shrinkage preliminary test maximum likelihood estimators are defined. The expressions for the bias and the mean square error of the three estimators are provided and the relative efficiences are analyzed. A maximin criterion is established, and graphs are constructed for an arbitrary number of degrees of freedom (D.F.) as well as sample sizes. A criterion to select optimal significance level is also discussed.
Stabilized Multivariate Tests ‐ the Inclusion of Missing Values
Authors: Kropf S. , Läuter J. , Glimm E. 
Published: May 2007

AbstractIn two recent papers, LÄUTER (1996) and LÄUTER, GLIMM, and KROPF (1996) have proposed a new class of exact tests for multivariate normal data with an inherent structure. These tests are based on the construction of scores that have a spherical distribution. The principle can be adapted to a variety of different situations, and it supports exact tests with small samples of high‐dimensional data.In this paper, an enhancement of that theory will be given regarding the problem of missing data. It provides a new mathematical understanding of mean imputation techniques, and it is also the basis for a modified principal component missing data algorithm described here.
On Robust Linear Regression of Hepatic Extraction of Insulin
Authors: Aboukalam M. A. F. 
Published: May 2007

AbstractIn classical linear regression model for analysis of medical data concerning hepatic extraction of insulin and c‐peptide the fundamental assumption is that the subjects involved are of a similar nature. In reality, if this assumption is violated then the precision of the results is questionable. This paper suggests a robust alternative to overcome this problem. It is observed that the robustification may be a better option to determine an optimal quantity of insulin which minimizes the risk of damage associated with diabatic treatments.
Tests of Independence and Zero Correlations Among P Random Variables
Authors: Wilcox Rand R. 
Published: May 2007

AbstractSuppose it is desired to determine whether there is an association between any pair of p random variables. A common approach is to test H0 : R = I, where R is the usual population correlation matrix. A closely related approach is to test H0 : Rpb = I, where Rpb is the matrix of percentage bend correlations. In so far as type I errors are a concern, at a minimum any test of H0 should have a type I error probability close to the nominal level when all pairs of random variables are independent. Currently, the Gupta‐Rathie method is relatively successful at controlling the probability of a type I error when testing H0: R = I, but as illustrated in this paper, it can fail when sampling from nonnormal distributions. The main goal in this paper is to describe a new test of H0: Rpb = I that continues to give reasonable control over the probability of a type I error in the situations where the Gupta‐Rathie method fails. Even under normality, the new method has advantages when the sample size is small relative to p. Moreover, when there is dependence, but all correlations are equal to zero, the new method continues to give good control over the probability of a type I error while the Gupta‐Rathie method does not. The paper also reports simulation results on a bootstrap confidence interval for the percentage bend correlation.
Methods for the Analyses of Case‐Cohort Studies
Authors: Volovics Alexander , Van Brandt Piet A. Den 
Published: May 2007

AbstractCase‐cohort and nested case‐control sampling methods have recently been introduced as a means of reducing cost in large cohort studies. The asymptotic distribution theory results for relative rate estimation based on Cox type partial or pseudolikelihoods for case‐cohort and nested case‐control studies have been accounted for. However, many researchers use (stratified) frequency table methods for a first or primary summarization of the most important evidence on exposure‐disease or dose‐response relationships, i.e. the classical Mantel‐Haenszel analyses, trend tests and tests for heterogeneity of relative rates. These can be followed by exponential failure time regression methods on grouped or individual data to model relationships between several factors and response. In this paper we present the adaptations needed to use these methods with case‐cohort designs, illustrating their use with data from a recent case‐cohort study on the relationship between diet, life‐style and cancer. We assume a very general setup allowing piecewise constant failure rates, possible recurrent events per individual, independent censoring and left truncation.
Disentangling Intervention and Temporal Effects in Longitudinal Designs Using Latent Curve Analysis
Authors: Raykov Tenko 
Published: May 2007

AbstractThis article is concerned with an approach to disconfounding effects in repeated measure designs with multiple groups. An extension of latent curve analysis (MEREDITH and TISAK, 1990; RAO, 1958, 1965) is described. The method permits estimation and testing of intervention effects separately from temporal effects. It is based on restricted factor analysis of individual change profiles and utilizes basis curves representing group patterns of change. The approach is illustrated on data from a two‐group intervention study.
The Propensity Score in the Analysis of Therapeutic Studies
Authors: Graf Erika 
Published: May 2007

AbstractA method for reducing bias in observational studies proposed by ROSENBAUM and RUBIN (1983, 1984) is discussed with a view to applications in studies designed to compare two treatments. The data are stratified on a function of covariates, called the propensity score. The propensity score is the conditional probability of receiving a specific treatment given a set of observed covariates. Some insight into how this kind of stratification works in theory is given. Within strata, the treatment groups are comparable with respect to the distribution of covariates incorporated into the score, hence a corresponding stratified analysis can be considered. The method is different from other strategies in that the sub‐classes are not intended to comprise patients with similar prognosis. In practice, estimated grouped scores are used. Problems concerning the interpretation of the proposed stratified approach are illustrated by an application in oncology, and the results are compared to those from an analysis in a standard regression model.
The Analysis of Correlated Binary Outcomes Using Multivariate Logistic Regression
Authors: Gauvreau Kimberlee , Pagano Marcello 
Published: May 2007

AbstractA method for analyzing correlated binary outcomes when the responses are distinct measurements made simultaneously on a single individual is presented. This extension of univariate logistic regression allows us to model the dependence of the responses on a set of covariates while estimating the degree of association among them. For the case of two dichotomous outcomes, a form of the cumulative bivariate logistic distribution proposed by Gumbel is used to characterize their joint probabilities in terms of logistic marginal probabilities and the correlation coefficient of the responses. The model is then extended to accommodate three or more dichotomous outcomes. A two‐step approximation to fitting the multivariate logistic model is also described.
Analysis of Repeated Measurements with Ante‐Dependence Covariance Models
Authors: Macchiavelli Raúl E. , Moser E. Barry 
Published: May 2007

AbstractThis paper considers the use of ante‐dependence models in problems with repeated measures through time. These are conditional regression models which reflect the dependence of a measure on some of the previous observations from the same subject. We present maximum likelihood estimators of the covariance matrix and procedures for selecting the order of ante‐degendence based on penalized like‐lihoods. Extensions to missing data situations are discussed.We propose Wald‐type test statistics and apply them in two situations common in experiments with repeated measures: one with pre‐study observations and another one with small sample size relative to the number of time periods. In these examples, tests assuming ante‐dependence find effects which are not detected using competing procedures.
Selection of Tree‐Biased Classifiers with the Bootstrap 632+ Rule
Authors: Merler Stefano , Furlanello Cesare 
Published: May 2007

AbstractThis paper introduces a novel model selection procedure for tree‐based classifiers. The method is based on the bootstrap 632+ rule recently proposed by Efron and Tibshirani. The rule allows selecting compact, non‐overfitting classification trees by weighting the contributions of the resubstitution and standard bootstrap estimated error. The proposed method is applied in a medical entomology problem for modeling the risk of parasite presence.
A Likelihood Approach to Analysing Longitudinal Bivariate Binary Data
Authors: Chan Jennifer S. K. , Kuk Anthony Y. C. , Bell James 
Published: May 2007

AbstractTo study the effect of methadone treatment in reducing multiple drug use, say heroin and benzodiazepines while controlling for their possible interaction, we analyse the results of urine drug screens from patients in treatment at a Sydney clinic in 1986. Weekly tests are either positive or negative for each type of drug and a bivariate binary model was developed to analyse such repeated bivariate binary outcomes. It models simultaneously the logit of each type of drug use and their log odds ratio linearly in some covariates. The serial correlation within subject is accounted for by including the ‘previous outcome’ of both drugs and their interaction as covariates. Our main conclusion is that drug use is reduced over time and the interaction between dose and time effects is not significant. It also suggests that while methadone maintenance is effective in reducing heroin use (CHAN et al., 1995), it does not suppress non‐opioid drug use. Concerning the association between the two drugs, it is found that the present strength of their association depends on the previous outcomes only through a measure of concordance. The proposed model has a tractable likelihood function and so a full likelihood analysis is possible. It can be easily extended to incorporate mixture effects. The EM algorithm is used for the estimation of parameters in the mixture model and model selection can be based on the Akaike Information Criterion.
Test Statistics for Three‐Factor Interaction in 2 × 2 × 2 Contingency Tables
Authors: Mannan M. , Nassar R. 
Published: May 2007

AbstractA Monte Carlo simulation was conducted in order to determine the size and power of two proposed tests (the covariance and correlation tests) for three‐factor interaction in 2 × 2 × 2 contingency tables. Results were compared to the log‐odds ratio test statistic. Simulation showed the correlation test to be more conservative than the covariance test, but less so than the log‐odds ratio test. However, the correlation test was the most powerful among the three tests.
Bayesian Neural Network Models for Censored Data
Authors: Faraggi David , Simon R. , Yaskil E. , Kramar A. 
Published: May 2007

AbstractNeural networks are considered by many to be very promising tools for classification and prediction. The flexibility of the neural network models often result in over‐fit. Shrinking the parameters using a penalized likelihood is often used in order to overcome such over‐fit. In this paper we extend the approach proposed by FARAGGI and SIMON (1995a) to modeling censored survival data using the input‐output relationship associated with a single hidden layer feed‐forward neural network. Instead of estimating the neural network parameters using the method of maximum likelihood, we place normal prior distributions on the parameters and make inferences based on derived posterior distributions of the parameters. This Bayesian formulation will result in shrinking the parameters of the neural network model and will reduce the over‐fit compared with the maximum likelihood estimators. We illustrate our proposed method on a simulated and a real example.
Using a Normal Approximation to Test for the Binomial Distribution
Authors: Sun Ping , Madden Laurence V. 
Published: May 2007

AbstractThe commonly used method to test for the binomial distribution is thex2‐test. In this paper, we introduce an alternative method to test for the binomial distribution. SupposeNis the number of sample groups withnindividuals each,xijis thejth sample inith group, a Bernoulli variable with parameter and VVI=s2/[m(1 ‐m)/n]. Then it is well know that the asymptotic distribution of the statistic (N‐ 1) VVI isx2(N‐ 1) under the hypothesisp1= p2= … = pN.Here we find that VVI has an asymptotic normal distributionN(1, 2(1 ‐ 1/n)/(N‐ 1)). Unlike thex2‐statistic, the variance of the normal test statistic is a function ofn.This method is convenient in detecting spatial patterns and dispersion in the study of diseased organisms (e.g., plants) in field samples.
Confidence Limits on the Ratio of the Mean Failure Times Based on Paired Exponential Observations
Authors: Lui Kung‐Jong 
Published: May 2007

AbstractThis paper discusses interval estimation for the ratio of the mean failure times on the basis of paired exponential observations. This paper considers five interval estimators: the confidence interval using an idea similar to Fieller's theorem (CIFT), the confidence interval using an exact parametric test (CIEP), the confidence interval using the marginal likelihood ratio test (CILR), the confidence interval assuming no matching effect (CINM), and the confidence interval using a locally most powerful test (CIMP). To evaluate and compare the performance of these five interval estimators, this paper applies Monte Carlo simulation. This paper notes that with respect to the coverage probability, use of the CIFT, CILR, or CIMP, although which are all derived based on large sample theory, can perform well even when the number of pairs n is as small as 10. As compared with use of the CILR, this paper finds that use of the CIEP with equal tail probabilities is likely to lose efficiency. However, this loss can be reduced by using the optimal tail probabilities to minimize the average length when n is small (<20). This paper further notes that use of the CIMP is preferable to the CIEP in a variety of situations considered here. In fact, the average length of the CIMP with use of the optimal tail probabilities can even be shorter than that of the CILR. When the intraclass correlation between failure times within pairs is 0 (i.e., the failure times within the same pair are independent), the CINM, which is derived for two independent samples, is certainly the best one among the five interval estimators considered here. When there is an intraclass correlation but which is small (<0.10), the CIFT is recommended for obtaining a relatively short interval estimate without sacrificing the loss of the coverage probability. When the intraclass correlation is moderate or large, either the CILR or the CIMP with the optimal tail probabilities is preferable to the others. This paper also notes that if the intraclass correlation between failure times within pairs is large, use of the CINM can be misleading, especially when the number of pairs is large.
A Two‐Way Analysis of Covariance Model for Classification of Stability Data
Authors: Ahn Hongshik , Chen James J. , Lin Tsae‐Yun D. 
Published: May 2007

AbstractThis paper proposes a procedure for testing and classifying data with multiple factors. A two‐way analysis of covariance is used to classify the differences among the batches as well as another factor such as package type and/or product strength. In the test procedure, slopes and intercepts of the main effects are tested using a combination of simultaneous and sequential F‐tests. Based on the test procedure results, the data are classified into one of four different groups. For each group, shelf life can be calculated accordingly. We examine if the procedure produces satisfactory control of the probability of a Type I error and the power of detecting the difference of degradation rates and intercepts for different nominal levels. The method is evaluated with a Monte Carlo simulation study. The proposed procedure is compared with the current FDA procedure using real data.
Comparing the Powers of the Wald‐Wolfowitz and Kolmogorov‐Smirnov Tests
Authors: Magel Rhonda C. , Wibowo Sasmito H. 
Published: May 2007

AbstractThe Wald‐Wolfowitz test and the Kolmogorov‐Smirnov test are two well‐known tests which can be used to test for differences between two population distributions, where these distributions could differ in means, variances or shapes. This paper compares the simulated power of the Wald‐Wolfowitz test to that of the Kolmogorov‐Smirnov test in several situations. Differences in location only, differences in variance only, and differences in both location and variance are considered. In most cases, equal sample sizes of 10 and 20 are used.