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2005

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Articles 121 - 134 of 134

Full-Text Articles in Statistical Theory

Effect Of Position Of An Outlier On The Influence Curve Of The Measures Of Preferred Direction For Circular Data, B. Sango Otieno, Christine M. Anderson-Cook May 2005

Effect Of Position Of An Outlier On The Influence Curve Of The Measures Of Preferred Direction For Circular Data, B. Sango Otieno, Christine M. Anderson-Cook

Journal of Modern Applied Statistical Methods

Circular or angular data occur in many fields of applied statistics. A common problem of interest in circular data is estimating a preferred direction and its corresponding distribution. It is complicated by the wrap-around effect on the circle, which exists because there is no natural minimum or maximum. The usual statistics employed for linear data are inappropriate for directional data, as they do not account for its circular nature. The robustness of the three common choices for summarizing the preferred direction (the sample circular mean, sample circular median and a circular analog of the Hodges-Lehmann estimator) are evaluated via their …

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An Empirical Evaluation Of The Retrospective Pretest: Are There Advantages To Looking Back?, Paul A. Nakonezny, Joseph Lee Rodgers May 2005

An Empirical Evaluation Of The Retrospective Pretest: Are There Advantages To Looking Back?, Paul A. Nakonezny, Joseph Lee Rodgers

Journal of Modern Applied Statistical Methods

This article builds on research regarding response shift effects and retrospective self-report ratings. Results suggest moderate evidence of a response shift bias in the conventional pretest-posttest treatment design in the treatment group. The use of explicitly worded anchors on response scales, as well as the measurement of knowledge ratings (a cognitive construct) in an evaluation methodology setting, helped to mitigate the magnitude of a response shift bias. The retrospective pretest-posttest design provides a measure of change that is more in accord with the objective measure of change than is the conventional pretest-posttest treatment design with the objective measure of change, …

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Regression By Data Segments Via Discriminant Analysis, Stan Lipovetsky, Michael Conklin May 2005

Regression By Data Segments Via Discriminant Analysis, Stan Lipovetsky, Michael Conklin

Journal of Modern Applied Statistical Methods

It is known that two-group linear discriminant function can be constructed via binary regression. In this article, it is shown that the opposite relation is also relevant – it is possible to present multiple regression as a linear combination of a main part, based on the pooled variance, and Fisher discriminators by data segments. Presenting regression as an aggregate of the discriminators allows one to decompose coefficients of the model into sum of several vectors related to segments. Using this technique provides an understanding of how the total regression model is composed of the regressions by the segments with possible …

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Inferences About Regression Interactions Via A Robust Smoother With An Application To Cannabis Problems, Rand R. Wilcox, Mitchell Earleywine May 2005

Inferences About Regression Interactions Via A Robust Smoother With An Application To Cannabis Problems, Rand R. Wilcox, Mitchell Earleywine

Journal of Modern Applied Statistical Methods

A flexible approach to testing the hypothesis of no regression interaction is to test the hypothesis that a generalized additive model provides a good fit to the data, where the components are some type of robust smoother. A practical concern, however, is that there are no published results on how well this approach controls the probability of a Type I error. Simulation results, reported here, indicate that an appropriate choice for the span of the smoother is required so that the actual probability of a Type I error is reasonably close to the nominal level. The technique is illustrated with …

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Local Power For Combining Independent Tests In The Presence Of Nuisance Parameters For The Logistic Distribution, Walid A. Abu-Dayyeh, Z. R. Al-Rawi, M. M. A. Al-Momani May 2005

Local Power For Combining Independent Tests In The Presence Of Nuisance Parameters For The Logistic Distribution, Walid A. Abu-Dayyeh, Z. R. Al-Rawi, M. M. A. Al-Momani

Journal of Modern Applied Statistical Methods

Four combination methods of independent tests for testing a simple hypothesis versus one-sided alternative are considered viz. Fisher, the logistic, the sum of P-values and the inverse normal method in case of logistic distribution. These methods are compared via local power in the presence of nuisance parameters for some values of α using simple random sample.

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A Comparison Of Parametric And Coarsened Bayesian Interval Estimation In The Presence Of A Known Mean-Variance Relationship, Kent Koprowicz, Scott S. Emerson, Peter Hoff Apr 2005

A Comparison Of Parametric And Coarsened Bayesian Interval Estimation In The Presence Of A Known Mean-Variance Relationship, Kent Koprowicz, Scott S. Emerson, Peter Hoff

UW Biostatistics Working Paper Series

While the use of Bayesian methods of analysis have become increasingly common, classical frequentist hypothesis testing still holds sway in medical research - especially clinical trials. One major difference between a standard frequentist approach and the most common Bayesian approaches is that even when a frequentist hypothesis test is derived from parametric models, the interpretation and operating characteristics of the test may be considered in a distribution-free manner. Bayesian inference, on the other hand, is often conducted in a parametric setting where the interpretation of the results is dependent on the parametric model. Here we consider a Bayesian counterpart to …

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Causal Inference In Longitudinal Studies With History-Restricted Marginal Structural Models, Romain Neugebauer, Mark J. Van Der Laan, Ira B. Tager Apr 2005

Causal Inference In Longitudinal Studies With History-Restricted Marginal Structural Models, Romain Neugebauer, Mark J. Van Der Laan, Ira B. Tager

U.C. Berkeley Division of Biostatistics Working Paper Series

Causal Inference based on Marginal Structural Models (MSMs) is particularly attractive to subject-matter investigators because MSM parameters provide explicit representations of causal effects. We introduce History-Restricted Marginal Structural Models (HRMSMs) for longitudinal data for the purpose of defining causal parameters which may often be better suited for Public Health research. This new class of MSMs allows investigators to analyze the causal effect of a treatment on an outcome based on a fixed, shorter and user-specified history of exposure compared to MSMs. By default, the latter represents the treatment causal effect of interest based on a treatment history defined by the …

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The Bayesian Two-Sample T-Test, Mithat Gonen, Wesley O. Johnson, Yonggang Lu, Peter H. Westfall Apr 2005

The Bayesian Two-Sample T-Test, Mithat Gonen, Wesley O. Johnson, Yonggang Lu, Peter H. Westfall

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

In this article we show how the pooled-variance two-sample t-statistic arises from a Bayesian formulation of the two-sided point null testing problem, with emphasis on teaching. We identify a reasonable and useful prior giving a closed-form Bayes factor that can be written in terms of the distribution of the two-sample t-statistic under the null and alternative hypotheses respectively. This provides a Bayesian motivation for the two-sample t-statistic, which has heretofore been buried as a special case of more complex linear models, or given only roughly via analytic or Monte Carlo approximations. The resulting formulation of the Bayesian test is easy …

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Resampling Based Multiple Testing Procedure Controlling Tail Probability Of The Proportion Of False Positives, Mark J. Van Der Laan, Merrill D. Birkner, Alan E. Hubbard Mar 2005

Resampling Based Multiple Testing Procedure Controlling Tail Probability Of The Proportion Of False Positives, Mark J. Van Der Laan, Merrill D. Birkner, Alan E. Hubbard

U.C. Berkeley Division of Biostatistics Working Paper Series

Simultaneously testing a collection of null hypotheses about a data generating distribution based on a sample of independent and identically distributed observations is a fundamental and important statistical problem involving many applications. In this article we propose a new resampling based multiple testing procedure asymptotically controlling the probability that the proportion of false positives among the set of rejections exceeds q at level alpha, where q and alpha are user supplied numbers. The procedure involves 1) specifying a conditional distribution for a guessed set of true null hypotheses, given the data, which asymptotically is degenerate at the true set of …

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Bayesian Evaluation Of Group Sequential Clinical Trial Designs, Scott S. Emerson, John M. Kittelson, Daniel L. Gillen Mar 2005

Bayesian Evaluation Of Group Sequential Clinical Trial Designs, Scott S. Emerson, John M. Kittelson, Daniel L. Gillen

UW Biostatistics Working Paper Series

Clincal trial designs often incorporate a sequential stopping rule to serve as a guide in the early termination of a study. When choosing a particular stopping rule, it is most common to examine frequentist operating characteristics such as type I error, statistical power, and precision of confi- dence intervals (Emerson, et al. [1]). Increasingly, however, clinical trials are designed and analyzed in the Bayesian paradigm. In this paper we describe how the Bayesian operating characteristics of a particular stopping rule might be evaluated and communicated to the scientific community. In particular, we consider a choice of probability models and a …

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Implementation Of Estimating-Function Based Inference Procedures With Mcmc Sampler, Lu Tian, Jun S. Liu, L. J. Wei Feb 2005

Implementation Of Estimating-Function Based Inference Procedures With Mcmc Sampler, Lu Tian, Jun S. Liu, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

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Fixed-Width Output Analysis For Markov Chain Monte Carlo, Galin L. Jones, Murali Haran, Brian S. Caffo, Ronald Neath Feb 2005

Fixed-Width Output Analysis For Markov Chain Monte Carlo, Galin L. Jones, Murali Haran, Brian S. Caffo, Ronald Neath

Johns Hopkins University, Dept. of Biostatistics Working Papers

Markov chain Monte Carlo is a method of producing a correlated sample in order to estimate features of a complicated target distribution via simple ergodic averages. A fundamental question in MCMC applications is when should the sampling stop? That is, when are the ergodic averages good estimates of the desired quantities? We consider a method that stops the MCMC sampling the first time the width of a confidence interval based on the ergodic averages is less than a user-specified value. Hence calculating Monte Carlo standard errors is a critical step in assessing the output of the simulation. In particular, we …

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Multiple Testing Procedures And Applications To Genomics, Merrill D. Birkner, Katherine S. Pollard, Mark J. Van Der Laan, Sandrine Dudoit Jan 2005

Multiple Testing Procedures And Applications To Genomics, Merrill D. Birkner, Katherine S. Pollard, Mark J. Van Der Laan, Sandrine Dudoit

U.C. Berkeley Division of Biostatistics Working Paper Series

This chapter proposes widely applicable resampling-based single-step and stepwise multiple testing procedures (MTP) for controlling a broad class of Type I error rates, in testing problems involving general data generating distributions (with arbitrary dependence structures among variables), null hypotheses, and test statistics (Dudoit and van der Laan, 2005; Dudoit et al., 2004a,b; van der Laan et al., 2004a,b; Pollard and van der Laan, 2004; Pollard et al., 2005). Procedures are provided to control Type I error rates defined as tail probabilities for arbitrary functions of the numbers of Type I errors, V_n, and rejected hypotheses, R_n. These error rates include: …

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Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models, Larry Leon, Tianxi Cai, L. J. Wei Jan 2005

Robust Inferences For Covariate Effects On Survival Time With Censored Linear Regression Models, Larry Leon, Tianxi Cai, L. J. Wei

Harvard University Biostatistics Working Paper Series

Various inference procedures for linear regression models with censored failure times have been studied extensively. Recent developments on efficient algorithms to implement these procedures enhance the practical usage of such models in survival analysis. In this article, we present robust inferences for certain covariate effects on the failure time in the presence of "nuisance" confounders under a semiparametric, partial linear regression setting. Specifically, the estimation procedures for the regression coefficients of interest are derived from a working linear model and are valid even when the function of the confounders in the model is not correctly specified. The new proposals are …

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