bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Complete Analysis of Variability for Estimates of Absolute Risk from a Population‐Based Case‐Control Study on Breast Cancer
Authors: Benichou Jacques 
Published: May 2007

AbstractThe absolute risk is the probability of developing a given disease over a specified time interval given age and risk factors. GAIL et al. (1989) obtained point estimates for the absolute risk of breast cancer from a population‐based case‐control study, the Breast Cancer Detection and Demonstration Project (BCDDP). They combined relative risk estimates obtained from the case‐control data and based on four risk factors and age, and composite hazard estimates from the cohort data. In this paper, we present a complete analysis of variability for breast cancer absolute risk estimates. Our proposed variance estimator takes into account all components of variability, namely the variances of relative risk estimates and of baseline hazard estimates as well as the covariance between the two, the latter terms being based on implicit delta‐method arguments (BENICHOU and GAIL, 1989). Our variance estimator also takes into account the specifics of the BCDDP study, namely the subsampling of cases and controls. We provide full details of the variance calculations because we anticipate that, in future studies, subsampling will also occur. We also present numerical illustration based on the BCDDP data. These calculations have been used in a recently developed program that computes point estimates and confidence intervals for the absolute risk of breast cancer based on the BCDDP data (BENICHOU, 1993).
Sample Size Calculation for Intraclass Correlation in the Presence of Random Loss of Sampled Patients
Authors: Lui Kung‐Jong 
Published: May 2007

AbstractWhen a trial involves an invasive laboratory test procedure or requires patients to make a commitment to follow a restrictive test schedule, we can often lose a great proportion of our sampled patients due to refusal of participation into our study. Therefore, incorporating the possible loss of patients into sample size calculation is certainly important in the planning stage of a study. In this paper, we have generalized the sample size calculation procedure for intraclass correlation by accounting for the random loss of patients in the beginning of a trial. We have demonstrated that the simple ad hoc procedure, that raises the estimated sample size in the absence of loss of patients by the factor 1/po, where po is the retention probability for a randomly selected patient, is adequate when po is large (=0.80). When po is small (i.e., a high refusal rate), however, use of this simple ad hoc procedure tends to underestimate the required sample size. Furthermore, we have found that if the individual retention probability varied substantially among patients, then the magnitude of the above underestimation could even be critical and therefore, the application of the simple direct adjustment procedure in this situation should be avoided.
A Stochastic Model for Surname Evolution
Authors: Islam M. 
Published: May 2007

AbstractSurnames are inherited in much the same way as biological traits like alleles of one locus. Assuming the heritability of surnames, a simple stochastic model for X, the total number of occurrences of a surname, the Consul distribution defined by the probability mass function:

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for x = 1, 2, 3,… and zero otherwise and where either (i) m is a positive integer when 0 ≤ θ ≤ 1 such that θ ≦ mθ ≦ 1, or (ii) m≤0, θ ≤0 such that mθ 1, can be arrived at by considering the branching process mechanism. Some applications of the model to real data are also considered.
Sensitivity Analysis of Variable‐Size Group Testing and its Related Continuous Models
Authors: Chen C. L. , Swallow W. H. 
Published: May 2007

AbstractParallels between the discrete group‐testing model and some closely‐related continuous models are elucidated. It is shown that in both the discrete and continuous cases, the maximum likelihood estimators may suffer from similar lack of robustness. Isotonic regression and maximum likelihood estimation were therefore compared for a modified group testing model.
Optimal Experimental Design and Accuracy of Parameter Estimation for Nonlinear Regression Models Used in Long‐term Selection
Authors: Rudolph P. E. , Herrendörfer G. 
Published: May 2007

AbstractAn experimental design problem is considered for the analysis of long‐term selection experiments with nonlinear regression models. For a 3‐parametric exponential regression function whose parameters have also a reasonable biological interpretation approximate formulas for the determination of the necessary number of observations at each generation are constructed in such a way that the half expected length of an (1 — α)‐confidence interval for a chosen parameter is not greater than a given value. In this sense the accuracy of the parameter estimators can be described.
A Symmetric Extended Logistic Model with Applications to Experimental Toxicity Data
Authors: El‐Saidi Mohammed A. 
Published: May 2007

AbstractThe fit of the logit and probit models for quantal response data can be improved by embedding these classical models within a richer parametric family indexed by one or two shape parameters. In this paper, a symmetric extended logistic model indexed by a shape parameter λ is discussed with application to dose response curves. The usual maximum likelihood method is employed to estimate the parameters of the model. The need to include the shape parameter λ is illustrated by analyzing a set of real experimental data and comparing the fit of the extended logistic model to those obtained by the standard logit and probit models.
The Polynomial Model in the Multivariate Analysis of Variance
Authors: Baumgartner Cornelia 
Published: May 2007

AbstractAssuming that the independent variables (factors) are quantitative, there exist besides the coding schemes generally used for the multivariate analysis of variance (dummy‐coded or effect‐coded design matrices) the so‐called polynomial models. The advantage of these polynomial models are the full rank design matrices, which allow a more comprehensible analysis, i.e. the unambiguous interpretation of tested hypotheses and simultaneous confidence intervals.
Removing Cancer when it is Correlated with other Causes of Death
Authors: Carrière Jacques F. 
Published: May 2007

AbstractThis paper presents an identifiability theorem in the theory of dependent competing risks and it applies the result by examining the effect of removing cancer from the United States population when cancer is correlated with the other causes of death. The paper shows how dependence can be modeled with copula functions and it shows that calculating the survival probabilities after cancer is removed is equivalent to solving a system of nonlinear differential equations.
Temporal Analogues to Spatial K Functions
Authors: Cannon Ann , Cressie Noel 
Published: May 2007

AbstractIn this article, the spatial statistic known as the K function is adapted for temporal processes and patterns. The (optimal) K‐function estimator is used in a testing procedure to determine whether behavior patterns of exposed rats versus control rats are different. Specifically, the temporal analogue to the K function is given and an approximately optimal estimator is developed. Next, a testing procedure, to determine whether a group of point patterns is generated from complete temporal randomness, is given. Finally, a testing procedure, to compare pairwise two groups of point patterns to each other, is given. The testing procedures are illustrated with rat‐behavior data from both a control‐control experiment as well as an exposed‐control experiment, where in the latter case a difference in behavior is known to exist.
Linear Rank Tests Derived from a Superpopulation Model
Authors: Bouza Carlos 
Published: May 2007

AbstractA superpopulation model generates the probabilities of a Bernouilli random variable. The ranks of the involved variables are considered as survey weights. The distribution f each linear rank statistic is derived under the null hypothesis for the two sample problem and for the case k2 when a simple random sampling or stratified sampling is used.The growth of a population of insects and the behavior of patients with imsomnia are studied using these procedures.
Subset Selection for Exponential Populations: Determination of the Selection Constant
Authors: van der Laan Paul 
Published: May 2007

AbstractGiven are k(≧2) exponential populations differing only in their location parameter. One wishes to choose the best one, that is the population with the largest value of the location parameter. A possible method for solving this problem is to select a subset of the k populations of size at least one which includes the best population with a required confidence P*(k−1 ≤ P* ≤1). In this paper the required selection constant is determined for different values of k and P*. Also an approximation for the selection constant is derived. A comparison with the exact results is made.
Testing Quasi‐Independence in Stratified Triangular Contingency Tables
Authors: May Warren L. , Johnson William D. 
Published: May 2007

AbstractA special class of incomplete contingency tables has structural zeroes in one or more cells. Some of these tables have a triangular appearance in the sense that they are square and their cells are known a priori to contain zeroes above (or below) their main diagonals. We propose methods of analyzing log‐linear models and testing quasi‐independence in these triangular tables. We also offer a method for combining such tables that are stratified by a concomitant categorical variable. This strategy follows the same approach used in a Cochran‐Mantel‐Haenszel test.
Generalized Binomial Regression Model
Authors: Famoye Felix 
Published: May 2007

AbstractThe generalized negative binomial distribution has been found useful in fitting over‐dispersed as well as under‐dispersed count data. We define and study the generalized binomial regression model which is used to predict a count response variable affected by one or more explanatory variables. The methods of maximum likelihood and moments are given for estimating the model parameters. Approximate tests for the adequacy of the model are considered. The generalized binomial regression model has been applied to two observed data sets to which binomial regression model was applied earlier.
Improved Shrinkage Estimation of Relative Potency
Authors: Ahmed S. Ejaz 
Published: May 2007

AbstractThis article considers the asymptotic estimation theory for the log relative potency in a symmetric parallel bioassay when uncertain prior information about the true log relative potency is assumed to be a known quantity. Three classes of point estimation, namely, the unrestricted estimator, the shrinkage restricted estimator and shrinkage preliminary test estimator are proposed. Their asymptotic mean squared errors are derived and compared. The relative dominance picture of the estimators is presented. Interestingly, proposed shrinkage preliminary test estimator dominates the unrestricted estimator in a range that is wider than that of the usual preliminary test estimator. Most importantly, the size of the preliminary test is much appropriate than the usual preliminary test estimator.
Parameters Estimation in a Simple Linear Regression Using Rank Set Sampling
Authors: Muttlak Hassen A. 
Published: May 2007

AbstractRanked set sampling (RSS) as suggested by MCINTYRE (1952) and TAKAHASI and WAKIMOTO (1968) may be used to estimate the parameters of the simple regression line. The objective is to use the RSS method to increase the efficiency of the estimators relative to the simple random sampling (SRS) method. Estimators of the slope and intercept are considered. Computer simulated results are given, and an example using real data presented to illustrate the computations.
Additive and Non‐additive Genetic Correlations. I. Properties of Sampling Distributions
Authors: Hohls Trevor , Clarke G. P. Y. 
Published: May 2007

AbstractThe sampling distributions of additive and non‐additive genetic correlations estimated from a diallel experiment were determined through a computer simulation study. The properties of the sampling distributions were investigated in terms of the effect of varying the magnitude of the relevant variance components and the true correlation coefficient (Q), as well as the size of the diallel experiment. It was found that there was a high incidence of Q falling outside the range −1 ≦Q≦1 when Q was close to 1 or ‐1, and the variance components were small. The degrees of freedom of the correlation components were shown to affect the variability of Q, and it was shown that a diallel experiment, that is to be used for a correlation analysis, should not be smaller than a 12 × 12 half diallel. Use of the Z transformation as a means of testing the significance of Q has been verified in this study and improvements on the degrees of freedom used for the transformation have been discussed.
A Multivariate Goodness‐of‐Fit Test for Stochastically Ordered Distributions
Authors: Alam Khursheed , Williams Calvin L. 
Published: May 2007

AbstractThere are a number of nonparametric procedures known for testing goodness‐of‐fit in the univariate case. Similar procedures can be derived for testing goodness‐of‐fit in the multivariate case through an application of the theory of statistically equivalent blocks (SEB). The SEB transforms the data into coverages which are distributed as spacings from a uniform distribution on [0,1], under the null hypothesis. In this paper, we present a multivariate nonparametric test of goodness‐of‐fit based on the SEB when the multivariate distributions under the null hypothesis and the alternative hypothesis are “weakly” ordered. Empirical results are given on the performance of the proposed test in an application to the problem of assessing the reliability of ap‐component system.
Spatial Mark‐Recapture Method in the Estimation of Crayfish Population Size
Authors: Partanen Päivi , Penttinen Antti 
Published: May 2007

AbstractThe mark‐recapture method is considered for estimation of population size of slowly moving animals like crayfish. The Petersen type estimator for closed population is generalized for situations where recaptures are spatially dependent between the capture sites, and its variance approximation is derived using point processes as models for the population. The method of quadratic forms is suggested to be used as variance estimator. Finally, a trapping design is proposed where one trap at recapture is replaced by four adjacent traps. A simulation experiment is performed to explain the robusticity of the new trapping design against movements of animals.
Structural Changes in the Relationships of Body Dimensions of Four Species of Pulmonate Slugs (Mollusca : Gastropoda : Pulmonata)
Authors: Bhuyan Keshab Chandra , Nair G. Achuthan 
Published: May 2007

AbstractFour species of pulmonate slugsviz. Milax rusticus, Milax sowerbyi, Milax gagatesandLimax tenellusare colonised along the north eastern region of Libya. These slugs are homogeneous in character since they belong to the same class and order. However, their body dimensions were significantly different. Significant positive relationships existed on most of the body dimensions of each species and these relationships were found to be heterogeneous among the four species studied.
On the Power of Fisher's Combination Test for Two Stage Sampling in the Presence of Nuisance Parameters
Authors: Banik N. , Köhne K. , Bauer P. 
Published: May 2007

AbstractCombination tests for p‐values are used in meta‐analysis and in multistage testing with adaptive designs (Bauer & Köhne, 1993). The well known combination test due to Fisher is based on the products of p‐values. The power of this test applied to p‐values found from stochastically independent samples at two stages of an experiment is calculated. The probability models assumed correspond to the classical t‐tests for paired comparisons and parallel groups. The loss of power as compared to the optimal t‐tests in the pooled sample is small even for moderate sample sizes differing over stages, however, with one exception: If in the case of parallel groups the sample sizes are substantially unbalanced over treatments, then the combination of two tests with a nonoptimal sample size allocation exaggerates the loss of power.
Fitting Mixed Poisson Regression Models Using Quasi‐Likelihood Methods
Authors: Chen James J. , Ahn Hongshik 
Published: May 2007

AbstractThis paper is to investigate the use of the quasi‐likelihood, extended quasi‐likelihood, and pseudo‐likelihood approach to estimating and testing the mean parameters with respect to two variance models, M1: φ μθ(1+μphis;) and M2: φ μθ(1+τ). Simulation was conducted to compare the bias and standard deviation, and type I error of the Wald tests, based on the model‐based and robust variance estimates, using the three semi‐parametric approaches under four mixed Poisson models, two variance structures, and two sample sizes. All methods perform reasonably well in terms of bias. Type I error of the Wald test, based on either the model‐based or robust estimate, tends to be larger than the nominal level when over‐dispersion is moderate. The extended quasi‐likelihood method with the variance model M1 performs more consistently in terms of the efficiency and controlling the type I error than with the model M2, and better than the pseudo‐likelihood approach with either the M1 or M2 model. The model‐based estimate seems to perform better than the robust estimate when the sample size is small.
A Note on Response to Selection in Non‐Random Mating Populations
Authors: Nomura T. 
Published: May 2007

AbstractThe selection theory proposed by Muralidharan and Jain (1992a, b; Biom. J. 34, 147–152, 633–637) was re‐examined. Although they concluded that the theory is appropriate for any system of mating, the author showed that it is not applicable to inbreeding populations, which is the most important type of non‐random mating.
Incorporating Surrogate Endpoints into Group Sequential Trials
Authors: Cook Richard J. , Farewell Vern T. 
Published: May 2007

AbstractRecent developments proposed by Lin (1991) which allow for the sequential analysis of multivariate failure time data, are discussed in the context of clinical trials with marker process data. In particular, a marker based response observed as a univariate failure time variable is considered in a univariate manner and in combination with the primary failure time variable through global measures of treatment effect. A weighting scheme is introduced that is designed to make greater use of the marker process responses when they are consistent with the responses for the primary endpoint.Univariate analyses and analyses based on global test statistics are evaluated with respect to duration of study and type I and type II error rates for a variety of relative risk parameter configurations. The simulation study is designed to extend the work of Machado, Gail and Ellenberg (1990) to sequential trials and to assess the performance of the global test statistics in this context. The global measures are found to be preferable over the univariate primary endpoint analyses when the treatment affects all transition intensities in a consistent manner. Further, there are advantages over the univariate marker based analysis when the treatment effects on some transition intensities are in opposite directions. Such a scenario remains problematic however with the relative error rates determined to a large extent by the baseline weighting scheme.
A Model for the Censoring Process in Incomplete Repeated Measurement Experiments
Authors: Ducharme Gilles R. , Lepage Yves 
Published: May 2007

AbstractPremature terminations or dropouts occur often in repeated measurement experiments. A number of methods have been proposed to analyze such data but most of them assume that the censoring mechanism is, within each group, unaffected by the mechanism generating the response variables. In this paper, we propose a model for the censoring mechanism that generates dropouts. We then show how this model can be used to check whether the censoring mechanism is affected by the response variables and other covariates. Finally, the methods of the paper are applied to the “Halothane” data set.
Weighting Class Importance in Agricultural Crop Classification from Remotely Sensed Data with an Artificial Neural Network
Authors: Foody G. M. 
Published: May 2007

AbstractIn some classifications the importance of classes varies and it is desirable to weight allocation to selected classes. This is common in classifications of remotely sensed imagery, especially as class occurrence can vary markedly. If, for instance, there is prior knowledge on the distribution of class occurrence this weighting can be achieved with widely used statistical classifiers by setting appropriate a priori probabilities of class membership. With an arificial neural network the incorporation of prior knowledge is more problematic. An approach to weight class allocation in an artificial neural network classifcation by replicating selected training patterns is discussed. In comparison against a discriminant analysis for the classification of synthetic aperture radar imagery the results showed that training pattern replication could be used to weight class allocation with an effect similar to that of incorporating a priori probabilities of class membership into the discriminant analysis and resulted in a significant increase in classification accuracy.
Notes on Confidence Limits for the Odds Ratio in Case‐Control Studies under Inverse Sampling
Authors: Lui Kung‐Jong 
Published: May 2007

AbstractWhen the disease of interest is rare or chronic, we often apply the case control study design and the odds ratio to determine the possibly risk factors. In this paper, three simple closed‐form interval calculation procedures for the odds ratio under inverse sampling are proposed. Results on the basis of a Monte Carlo simulation to evaluate and compare the performance of these three procedures are presented. A discussion on the limitation of the proposed procedures when the probability of exposure is extremely common in both the case and the control groups and a suggestion to overcome this limitation are included as well.
Confidence Limits for the Intraclass Correlation in Compound‐Poisson Sampling
Authors: Lui Kung‐Jong , Kuo Lynn 
Published: May 2007

AbstractWhen the underlying responses are discrete, the interval estimation of the intraclass correlation derived from the normality assumption is not strictly valid for use. This paper focuses the interval estimation on the intraclass correlation under the negative binomial distribution, that has been commonly applied in epidemiological or consumer purchasing behaviour studies. This paper develops two simple asymptotic interval estimation procedures in closed forms for the intraclass correlation. To evaluate the performance of these procedures, a Monte Carlo simulation is carried out for a variety of situations. An example about consumer purchasing behaviors is also included to illustrate the use of the two proposed interval estimation procedures.
Hypothesis Testing Procedures for a Series of Independent Fourfold Tables under Inverse Sampling
Authors: Lui Kung‐Jong 
Published: May 2007

AbstractThis paper considers four summary test statistics, including the one recently proposed by Bennett (1986, Biometrical Journal 28, 859–862), for hypothesis testing of association in a series of independent fourfold tables under inverse sampling. This paper provides a systematic and quantitative evaluation of the small‐sample performance for these summary test statistics on the basis of a Monte Carlo simulation. This paper notes that the test statistic developed by Bennett (1986) can be conservative and thereby possibly lose the power when the underlying disease is not rare. This paper also finds that for given a fixed total number of cases in each table, the conditional test statistic is the best in controlling type I error among all test statistics considered here.