bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bayesian Reliability Estimation of a Two‐parameter Cauchy Distribution
Authors: Howlader H. A. , Weiss G. 
Published: June 2010

AbstractThis paper gives an approximate Bayes procedure for the estimation of the reliability function of a two‐parameter Cauchy distribution using Jeffreys' non‐informative prior with a squared‐error loss function, and with a log‐odds ratio squared‐error loss function. Based on a Monte Carlo simulation study, two such Bayes estimators of the reliability are compared with the maximum likelihood estimator.
Maximum Likelihood Estimates for Binary Data with Random Effects
Authors: Preisler Haiganoush K. 
Published: June 2010

AbstractThe purpose of this paper is to present a procedure for obtaining approximate maximum likelihood estimates for compound binary response models. The extra binomial variation is incorporated into the model by adding random effects to the fixed effects on the probit (or logit) scale. Numerical integration techniques are used to arrive at a solution of the likelihood equations.The paper also presents an illustrating numerical example based on a large toxicological data set. The computations are carried out within the GLIM statistical package.
Plausibility Inference for Generalized Poisson Distribution
Authors: Famoye F. , Consul P. C. 
Published: June 2010

AbstractThe two parameter generalized Poisson distribution (GPD) has been usefully applied to many biological and genetic problems and important interpretations have been given to both the parameters; however, reliable interval estimates for them are not available. A number of results on the plausibility inference for the GPD have been provided. Likelihood intervals and likelihood regions are also considered for the two parameters of the GPD. Expressions needed to compute the intervals and regions are given. These results are then applied to two biological problems to derive the relevant regions and intervals for the parameters of interest.
Combining Information from Similar Experiments: I. Statistical Issues
Authors: Verducci J. S. 
Published: June 2010

AbstractA general method for combining information from similar experiments is illustrated in the case where two independent experiments are designed to estimate a dose‐response curve. By accounting for variability across experiments, the proposed method avoids inferential pitfalls such as extended forms of Simpson's paradox. The validity of the method is supported by seven fundamental assumptions about data from replicated experiments. In contrast, an example indicates that failing to reject a preliminary test of equal distributions is inadequate justification for pooling data from two experiments. Methods that account for the variability across experiments in terms of known covariates are also discussed.
Estimation of the Number of Classes in a Population
Authors: Arnold Barry C. , Beaver Robert J. 
Published: June 2010

AbstractThe problem of estimating M, the number of classes in a population, is formulated as an occupancy problem in which N items are drawn from M urns. Under the assumption of a uniform distribution for the number of classes in the population, the sufficient statistic for M, which is the number of distinct classes observed, does not depend upon the number of repetitions in the sample. Point and interval estimates of M are developed using maximum likelihood and the method of moments. Both techniques give rise to the same basic equation which requires a simple iterative solution. These same techniques are used in the more general situation in which the classes can be further subdivided according to type.
A Dynamic Logistic Regression Model for Multiple Spell Failure Time Data
Authors: Hamerle A. 
Published: June 2010

AbstractThe paper deals with discrete‐time regression models to analyze multistate‐multiepisode failure time data. The covariate process may include fixed and external as well as internal time dependent covariates. The effects of the covariates may differ among different kinds of failures and among successive episodes. A dynamic form of the logistic regression model is investigated and maximum likelihood estimation of the regression coefficients is discussed. In the last section we give an application of the model to the analysis of survival time after breast cancer operation.
The Power of a Nonparametric Test for Seasonality
Authors: Marrero Osvaldo 
Published: June 2010

AbstractA computer simulation study was carried out to assess the performance of the test for seasonality of Hewitt et al. with monthly incidence rates. This test can be very powerful against an annual sinusoidal pattern. The test's power against a semi‐annual sinusoidal pattern or against a “one‐pulse” pattern is very small for the parameter values considered in this study.
Applications of a Vectorized MANOVA‐Model
Authors: Hecker H. , Wübbelt P. 
Published: June 2010

AbstractIf the observation matrix ofnindependentp‐dimensional normal vectors with common covariance matrix is transformed into annp‐vector, linear models and hypotheses on the expectations can be formulated, that are not attainable in standard MANOVA or GMANOVA models. The distribution of an earlier proposed test procedure for this vectorized model is derived forp= 2, and its application is demonstrated for crossover and repeated measurement designs. Numerical results are compared in situations, in which both the standard and the vectorized model are applicable.
Application of Prediction Analysis to Cross Classifications of Ordinal Data
Authors: von Eye Alexander , Brandtstädter Jochen 
Published: June 2010

AbstractPrediction analysis (PA) of cross classifications is characterized as a method for the analysis of local prediction hypotheses, that is, hypotheses that link particular predictor states to particular states of criteria. To evaluate the success of a prediction, PA compares the observed with an expected frequency distribution. The latter is estimated under the assumption of independence between predictors and criteria. When predictors of criteria have ordinal categories, the success of a prediction hypothesis is overestimated if there is a regression of the cell frequencies on the ranks of the variable categories. Using the method of log‐linear models, it is shown how ordinal categories can be taken into account in PA. Numerical examples are given from the areas of cognitive development and drug research.
Small Sample Properties of the Mantel‐Haenszel Test Statistic for Density Follow‐Up Studies
Authors: Boshuizen H. C. 
Published: June 2010

AbstractFor the analysis of combinations of 2×2 non‐contingency tables as obtained from density follow‐up studies (relating a number of events to a number of person‐years of follow‐up) an analogue of the Mantel‐Haenszel test for 2×2 contingency tables is widely used. In this paper the small sample properties of this test, both with and without continuity correction, are evaluated. Also the improvement of the test‐statistic by using the first four cumulants via the Edgeworth expansion was studied. Results on continuity correction agree with similar studies on the Mantel‐Haenszel statistic for 2×2 contingency tables: Continuity correction gives a p‐value which approximates the exact p‐value better than the p‐value obtained without this correction; both the exact test and its approximations show considerable conservatism in small samples; the uncorrected Mantel‐Haenszel test statistic gives a p‐value that agrees more with the nominal significance level, but can be anti‐conservative. The p‐value based on the first four cumulants gives a better approximation of the exact p‐value than the continuity corrected test, especially when the distribution has marked skewness.
Smoothing for Discrete Kernels in Discrimination
Authors: Tutz Gerhard 
Published: June 2010

AbstractIn multivariate discrimination by the discrete kernel method the allocation rule is Bayes risk consistent if the smoothing parameter is chosen by maximization of the leaving‐one‐out nonerror rate. It is shown that consistency still holds if the leaving‐one‐out nonerror rate is replaced by a smoothed version. Thus a cross‐validatory criterion is given which secures consistency and really can be used in practice.
A Monte Carlo Study to Evaluate the Performance of Selected Tests of Homogeneity in Sparse Contingency Tables
Authors: Kraemer Dale F. , Woolson Robert F. 
Published: June 2010

AbstractWe address the problem of tests of homogeneity in two‐way contingency tables in case‐control studies when the case category is subdivided intoksubcategories. In this situation, we have two cells with large frequencies and2 X kcells with frequencies that become small askincreases. We propose two ad hoc statistics in which a statistic for the sparse cells is combined with a statistic for the cells with large frequencies. We will study these tests along with the Pearson test (using a chi‐square approximation) in a Monte Carlo simulation study. Two sets of null hypothesis models and two sets of alternative hypothesis models are considered. The best test for the models considered is the usual Pearson test (using an approximate chi‐square distribution) although the ad hoc models are more powerful under one alternative model considered.
Small Sample Properties of Rank Tests for Incomplete Unbalanced Designs
Authors: Wittkowski Knut M. 
Published: June 2010

AbstractA rank test is presented for analysis of incomplete unbalanced designs, i.e. for designs that may have been originally planned to be either balanced or unbalanced and where some observations may be missing at random. This test is a modification of the procedure of Benard and van El‐teren (1953) based on a generalization of block weights proposed by Prentice (1979). It is compared with the tests of Haux, Schumacher, and Weckesser (1984) and Rai (1987). For incomplete or unbalanced designs with more than two treatments the quadratic forms proposed by these authors are proven to be invalid for small sample sizes, except for special cases. A necessary condition is given for test statistics to be valid also for small samples.
Polya‐Eggenberger Distribution: Parameter Estimation and Hypothesis Tests
Authors: Qu Yinsheng , Beck G. J. , Williams G. W. 
Published: June 2010

AbstractAlmost all common discrete distributions are related to the Polya‐Eggenberger distribution (PED), either its special cases or its limiting distributions. We demonstrate that the sum ofnbinary random variablesYj(j=1, …,n) taking values of 0 or 1 follows a PED if and only if the conditional expectation ofYkwith respect toY1, …,Ykis a linear function ofY1, …,Yk‐1, the expectationsE Yj(j= 1,…,n) are the same, and for each pairYiandYj, the correlations are the same.The maximum likelihood estimation of the parameters is studied. In most cases, the maximum likelihood equations can be solved by the Newton‐Raphson iterative procedure; in a special case, the maximum likelihood parameter estimates can be expressed as a function of the observed frequencies; and in some cases, the maximum likelihood equations are not soluble. Even when the maximum likelihood equations are soluble, the solutions may not be permissible. We propose a method to handle this problem.For testing the hypothesis that the parameter is zero, the Wald statistic is used; for model selection, the likelihood ratio test is used. The hypothesis tests are described by two data examples and applications of the PED to data analysis are demonstrated.
A Further Application of D. G. Kendall's Procrustes Analysis
Authors: Stoyan D. , Stoyan H. 
Published: June 2010

AbstractThis paper suggests a method for explorative data analysis of samples of figures described byn‐tuples of landmarks. It uses some variants of a form of procrustes analysis introduced by D. G. Kendall. Their results can be analysed by well‐known methods of data analysis, for example by cluster analysis. As an example a sample of hands of students is studied.
Empirical Bayes Methods in Clinical Trials Meta‐Analysis
Authors: Stijnen Th. , Van Houwelingen J. C. 
Published: June 2010

AbstractIn this paper we discuss empirical Bayes methods for combining evidence from a series of clinical experiments comparing two treatments. The approach is based on a random effects model for the treatment effects. We describe two nonparametric empirical Bayes procedures for estimating the effect sizes in the individual trials. The empirical Bayes estimates should have smaller mean squared error than the observed effect sizes. Moreover, the empirical Bayes estimates exhibit less variability and are more comparable to each other than the original estimates.
Stationary Forecasting; Using Holt‐Winter and a Modification of Holt‐Winter
Authors: Kazempour Mohammed K. 
Published: June 2010

AbstractThe mean square error of the best (projection) linear predictor and some approximation methods are compared. Some analytical results concerning the optimal ‘smoothing constant’ for Holt‐Winter in Moving Average (1), model MA(1), and Autoregressive Moving Average (1,1), model ARMA(1,1), have been derived. A modified Holt‐Winter technique has been proposed and it is shown for MA(1) and ARMA(1,1) it coincides with the best linear predictor. Finally, we considered flow prediction for the Niger River and the performance of modified Holt‐Winter graphically.
On a Mixed Linear Model when the Data are Subject to Selection
Authors: Im S. 
Published: June 2010

AbstractMost data available to animal breeders are subject to selection. We consider estimation and prediction in a mixed linear model for two traits when selection acts on the first trait and data for the second trait are available on selected individuals only. Difficulties of applying best linear unbiased predictors accounting for selection (HENDERSON, 1975) are highlighted. Using inferences based on the likelihood, it is shown that selection can be ignored in estimating variance components and fixed effects and in predicting random effects.
Use of Bootstrapping for Estimation of standard Deviation and Confidence Intervals of Genetic Variance‐ and Covariance‐Components
Authors: Eit A. H. Aastv 
Published: June 2010

AbstractThe paper deals with the problem of sampling error of genotypic correlation estimated from a randomised block design. The Bootstrap method is used and two different procedures are studied. Both standard deviations and confidence intervals for the genotypic correlations are calculated in a real experiment. The results show that the estimates might be very unstable. Theoretical considerations indicate that large experiments are needed in order to obtain reliable estimates.
Bootstrap Confidence Intervals for Effective Doses in the Probit Model for Dose‐Response Data
Authors: Müller Hans‐Georg , Wang Jane‐Ling 
Published: June 2010

AbstractParametric bootstrap methods for the construction of confidence intervals for the effective dose at level α(EDα) under the probit model for the dose‐response relationship are investigated. The standard maximum likelihood confidence intervals and percentile, centered percentile, studentized, bias corrected and better bias corrected bootstrap methods are compared in a simulation with 1000 Monte Carlo runs and 1000 bootstrap samples. Among the bootstrap methods, studentized and centered percentile methods are found to behave unfavorably with respect to observed coverage probability, whereas the bias corrected and better bias corrected bootstrap sometimes improve on the maximum likelihood method. The maximum likelihood method yielded very mixed results, but in our simulation none of the currently available bootstrap methods improved uniformly on this standard method. The methods are illustrated by an application to a bioassay.
Tables of Bonferroni‐limits for Simultaneous Chi‐square Tests
Authors: Dunkl E. , Ludwig O. , Lotz R. 
Published: June 2010

AbstractA table of Bonferroni Chi‐square limits is given for R simultaneous tests, where R = 1(1) 30(5) 150 at the level of α = 0.05 for 1 to 100 degrees of freedom. At the level α = 0.01 the limits are given for R = 1(1) 30, correspondingly. Additionally, a table of Bonferroni Chi‐square limits for 1 degree of freedom is given at both levels up to R(5%) = 300 and R(1 %) = 60 allowing for one‐sided as well as two‐sided testing. Reading of both tables is illustrated by biomedical examples. Methodological problems and clinical applications are discussed with special reference to configural frequency analysis (CFA).
A Comparison Between Trimmed Means and the Kaplan‐Meier Median
Authors: Magel Rhonda C. 
Published: June 2010

AbstractLet X1, X2,…, Xn denote a random sample from an unknown symmetric population distribution with mean, θ. The observations are subject to right censorship. The parameter, θ, is estimated using the Kaplan‐Meier median and various trimmed mean estimators (at α = .2, .3, and .4). A comparison is made between the Kaplan‐Meier median and the various trimmed means based on the estimated mean square errors. Ten underlying population distributions and two censoring distributions are considered.
Quantile Tests for Comparing Several Treatments with a Control Under Unequal Right‐Censoring
Authors: Chakraborti S. , Desu M. M. 
Published: June 2010

AbstractTests are proposed for comparing K treatments with a control when the samples are subject to unequal patterns of right censorhip. The tests are useful in the context of life testing experiments and clinical trials for survival analysis, where cost of experimentation is usually high, since the tests can be applied before all the units have failed. A numerical example is given to illustrate the implementation of the tests. The asymptotic distributions of the test statistics are obtained under both the null and the alternative hypotheses. Pitman efficacies of the tests are derived under the usual sequences of translation and proportional hazard alternatives. The size and the power of the tests are examined in a simulation study.
A Monte Carlo Study of Some Censored Data Wilcoxon Rank Tests
Authors: Öhman Marie‐Louise 
Published: June 2010

AbstractThe variance estimators usually applied for the generalized censored data Wilcoxon rank tests by Gehan and Peto & Prentice, are heavily biased in unbalanced problems. This paper reports the results of a Monte Carlo simulation study, where jackknifing is used to construct estimators of variance. Size, power and variance properties are compared for five variance estimators, when using different combinations of group sizes, failure and censoring patterns. The variance estimators are the permutational, the conditional permutational and the jackknife variance estimators for the statistic of Gehan and the asymptotic and the jackknife variance estimators for the statistic of Peto & Prentice. It appears that observed size, power and variance properties may be improved by using the jackknife variance estimator, when comparing to the variance estimators usually applied.
Estimatien of Infectious Potential of Blood Containing Human Immunodeficiency Virus
Authors: Connett J. E. , Rhame F. S. , Thomas J. , Le Ch. T. 
Published: June 2010

AbstractA method is described for estimating the probability that transfusion of a given quantity of blood from an AIDS‐infected donor will result in infection in the recipient. The method is based on maximum likelihood, and requires the assumption that individual viruses act independently in causing infection. Results of simulations indicate that the resulting estimates have low bias. The method is applicable to a wide variety of other problems.
Relative Efficiency of the Case‐Control to the Cohort Design in Epidemiology
Authors: Daniels K. , McHugh R. 
Published: June 2010

AbstractIn deriving the efficiency of the stratified to the simple random sample design in survey research, the critical link between the designs is the population analysis of variance. Analogously, the efficiency of the case‐control to the cohort design in epidemiologic research can be derived using Bayes' theorem as the essential connection between the designs. Prior information on the odds of the disease is also required. A numerical example using data from Fleiss' text is used to illustrate the result.
Testing the Mean Vector and the Correlation Coefficient in Life‐Testing Under Bivariate Normality
Authors: Tiku M. L. , Gill P. S. 
Published: June 2010

AbstractIn life‐testing situations under bivariate normality of (X, U), a few smallest or a few largest Y‐observations may not be available. Tests for μ = 0 (mean vector) and o = 0 (correlation coefficient) are developed from the available Y‐observations and their concomitant X‐observations. The robustness of these tests to departures from normality is investigated.
Quantile Function Models for Quantal Response Analysis
Authors: Mudholkar Govind S. , Phatak Mohan V. 
Published: June 2010

AbstractThe usual analysis of quantal response data occurring in diverse fields such as economics, medicine, psychology and toxicology use probit and logit models or their extensions with generalized least squares or the principle of likelihood as the method of statistical inference. The symmetric alternative models lead to practically comparable results and the choice of model or method is determined by considerations of familiarity and computational convenience. Recent attempts at improvement involve larger parametric families of tolerance distributions and employ the method of maximum likelihood in analysis. In this paper we consider models with the tolerance distributions based upon the Tukey‐lambda distributions which are described in terms of their quantile functions. The likelihood methods for fitting the models and testing their adequacies are developed and illustrated using classical data due to BLISS (1935) and ASHFORD and SMITH (1964).
Two‐Dimensional Mass‐Transfer Model of a Flat‐Plate Dialyzer with Ultrafiltration Operation
Authors: Tu J.‐W. , Ho C.‐D. 
Published: June 2010

AbstractA two‐dimensional theoretical model of a flat‐plate dialyzer with ultrafiltration operation was developed in this study. The two‐dimensional velocity profile of the fluid and the concentration distribution in the membrane of the dialysis system with ultrafiltration operation were also derived. The Crank‐Nicolson method was used to numerically solve the two‐dimensional theoretical model. The influences of the retentate phase flow rate, dialysate phase flow rate, ultrafiltration flow rate and channel thickness ratio on the concentration distribution and the mass transfer rate were illustrated. A considerable mass‐transfer efficiency improvement was obtained by employing the flat‐plate dialyzer with ultrafiltration operation in comparison to the pure dialysis system without ultrafiltration operation. An experiment involving urea separation by the flat‐plate dialyzer with ultrafiltration operation was also performed to confirm the accuracy of the proposed mathematical model.
The Porosity, Permeability and Restructuring of Heterogeneous Filter Cakes
Authors: Chellappah K. , Tarleton E. S. , Wakeman R. J. 
Published: June 2010

AbstractThe constant pressure filtration characteristics of cellulose fibers, titania (rutile) and mixtures of the two were studied using an automated filtration apparatus. With filtrations at 450 kPa, the average permeability (kav) for pure fiber and rutile were approximately 3.2 × 10–17 and 1.3 × 10–16 m2, respectively, with the variation of kav with fiber fraction showing a maximum. Similar trends were observed at filtration pressures of 150 and 600 kPa. The porosities (ϵav) of filter cakes formed from pure fiber and rutile suspensions were approximately 0.75 and 0.6, respectively. Interparticle penetration and additive porosity models were applied to the porosity data, and the additive porosity model appeared to better represent the experimental data. Also, the results obtained suggest that abrupt transitions in cake structure occur part way through some filtrations, resulting in anomalous filtration behavior.