Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Statistics and Probability

Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 11161 - 11190 of 12858

Full-Text Articles in Entire DC Network

Ip Algorithm Applied To Proteomics Data, Christopher Lee Green Nov 2004

Ip Algorithm Applied To Proteomics Data, Christopher Lee Green

Theses and Dissertations

Mass spectrometry has been used extensively in recent years as a valuable tool in the study of proteomics. However, the data thus produced exhibits hyper-dimensionality. Reducing the dimensionality of the data often requires the imposition of many assumptions which can be harmful to subsequent analysis. The IP algorithm is a dimension reduction algorithm, similar in purpose to latent variable analysis. It is based on the principle of maximum entropy and therefore imposes a minimum number of assumptions on the data. Partial Least Squares (PLS) is an algorithm commonly used with proteomics data from mass spectrometry in order to reduce the …

Go to article

Survival Analysis Using Auxiliary Variables Via Nonparametric Multiple Imputation, Chiu-Hsieh Hsu, Jeremy Taylor, Susan Murray, Daniel Commenges Nov 2004

Survival Analysis Using Auxiliary Variables Via Nonparametric Multiple Imputation, Chiu-Hsieh Hsu, Jeremy Taylor, Susan Murray, Daniel Commenges

The University of Michigan Department of Biostatistics Working Paper Series

We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variable to recover information for censored observations. To conduct the imputation, we use two working survival model to define the nearest neighbor imputing risk set. One model is for the event times and the other for the censoring times. Based on the imputing risk set, two nonparametric multiple imputation methods are considered: risk set imputation, and Kaplan-Meier estimator. For both methods a future event or censoring time is imputed for each censored observation. With a categorical auxiliary variable, we show that …

Go to article

A Bayesian Mixture Model Relating Dose To Critical Organs And Functional Complication In 3d Conformal Radiation Therapy, Tim Johnson, Jeremy Taylor, Randall K. Ten Haken, Avraham Eisbruch Nov 2004

A Bayesian Mixture Model Relating Dose To Critical Organs And Functional Complication In 3d Conformal Radiation Therapy, Tim Johnson, Jeremy Taylor, Randall K. Ten Haken, Avraham Eisbruch

The University of Michigan Department of Biostatistics Working Paper Series

A goal of radiation therapy is to deliver maximum dose to the target tumor while minimizing complications due to irradiation of critical organs. Technological advances in 3D conformal radiation therapy has allowed great strides in realizing this goal, however complications may still arise. Critical organs may be adjacent to tumors or in the path of the radiation beam. Several mathematical models have been proposed that describe a relationship between dose and observed functional complication, however only a few published studies have successfully fit these models to data using modern statistical methods which make efficient use of the data. One complication …

Go to article

Choice Of Monitoring Mechanism For Optimal Nonparametric Functional Estimation For Binary Data, Nicholas P. Jewell, Mark J. Van Der Laan, Stephen Shiboski Nov 2004

Choice Of Monitoring Mechanism For Optimal Nonparametric Functional Estimation For Binary Data, Nicholas P. Jewell, Mark J. Van Der Laan, Stephen Shiboski

U.C. Berkeley Division of Biostatistics Working Paper Series

Optimal designs of dose levels in order to estimate parameters from a model for binary response data have a long and rich history. These designs are based on parametric models. Here we consider fully nonparametric models with interest focused on estimation of smooth functionals using plug-in estimators based on the nonparametric maximum likelihood estimator. An important application of the results is the derivation of the optimal choice of the monitoring time distribution function for current status observation of a survival distribution. The optimal choice depends in a simple way on the dose response function and the form of the functional. …

Go to article

On Marginalized Multilevel Models And Their Computation, Michael E. Griswold, Scott L. Zeger Nov 2004

On Marginalized Multilevel Models And Their Computation, Michael E. Griswold, Scott L. Zeger

Johns Hopkins University, Dept. of Biostatistics Working Papers

Clustered data analysis is characterized by the need to describe both systematic variation in a mean model and cluster-dependent random variation in an association model. Marginalized multilevel models embrace the robustness and interpretations of a marginal mean model, while retaining the likelihood inference capabilities and flexible dependence structures of a conditional association model. Although there has been increasing recognition of the attractiveness of marginalized multilevel models, there has been a gap in their practical application arising from a lack of readily available estimation procedures. We extend the marginalized multilevel model to allow for nonlinear functions in both the mean and …

Go to article

Semiparametric Binary Regression Under Monotonicity Constraints, Moulinath Banerjee, Pinaki Biswas, Debashis Ghosh Nov 2004

Semiparametric Binary Regression Under Monotonicity Constraints, Moulinath Banerjee, Pinaki Biswas, Debashis Ghosh

The University of Michigan Department of Biostatistics Working Paper Series

Summary: We study a binary regression model where the response variable $\Delta$ is the indicator of an event of interest (for example, the incidence of cancer) and the set of covariates can be partitioned as $(X,Z)$ where $Z$ (real valued) is the covariate of primary interest and $X$ (vector valued) denotes a set of control variables. For any fixed $X$, the conditional probability of the event of interest is assumed to be a monotonic function of $Z$. The effect of the control variables is captured by a regression parameter $\beta$. We show that the baseline conditional probability function (corresponding to …

Go to article

A Bayesian Method For Finding Interactions In Genomic Studies, Wei Chen, Debashis Ghosh, Trivellore E. Raghuanthan, Sharon Kardia Nov 2004

A Bayesian Method For Finding Interactions In Genomic Studies, Wei Chen, Debashis Ghosh, Trivellore E. Raghuanthan, Sharon Kardia

The University of Michigan Department of Biostatistics Working Paper Series

An important step in building a multiple regression model is the selection of predictors. In genomic and epidemiologic studies, datasets with a small sample size and a large number of predictors are common. In such settings, most standard methods for identifying a good subset of predictors are unstable. Furthermore, there is an increasing emphasis towards identification of interactions, which has not been studied much in the statistical literature. We propose a method, called BSI (Bayesian Selection of Interactions), for selecting predictors in a regression setting when the number of predictors is considerably larger than the sample size with a focus …

Go to article

Deletion/Substitution/Addition Algorithm For Partitioning The Covariate Space In Prediction, Annette Molinaro, Mark J. Van Der Laan Nov 2004

Deletion/Substitution/Addition Algorithm For Partitioning The Covariate Space In Prediction, Annette Molinaro, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We propose a new method for predicting censored (and non-censored) clinical outcomes from a highly-complex covariate space. Previously we suggested a unified strategy for predictor construction, selection, and performance assessment. Here we introduce a new algorithm which generates a piecewise constant estimation sieve of candidate predictors based on an intensive and comprehensive search over the entire covariate space. This algorithm allows us to elucidate interactions and correlation patterns in addition to main effects.

Go to article

Spatially Adaptive Bayesian P-Splines With Heteroscedastic Errors, Ciprian M. Crainiceanu, David Ruppert, Raymond J. Carroll Nov 2004

Spatially Adaptive Bayesian P-Splines With Heteroscedastic Errors, Ciprian M. Crainiceanu, David Ruppert, Raymond J. Carroll

Johns Hopkins University, Dept. of Biostatistics Working Papers

An increasingly popular tool for nonparametric smoothing are penalized splines (P-splines) which use low-rank spline bases to make computations tractable while maintaining accuracy as good as smoothing splines. This paper extends penalized spline methodology by both modeling the variance function nonparametrically and using a spatially adaptive smoothing parameter. These extensions have been studied before, but never together and never in the multivariate case. This combination is needed for satisfactory inference and can be implemented effectively by Bayesian \mbox{MCMC}. The variance process controlling the spatially-adaptive shrinkage of the mean and the variance of the heteroscedastic error process are modeled as log-penalized …

Go to article

Assessing Treatment Effects In Randomized Longitudinal Two-Group Designs With Missing Observations, James Algina, H. J. Keselman Nov 2004

Assessing Treatment Effects In Randomized Longitudinal Two-Group Designs With Missing Observations, James Algina, H. J. Keselman

Journal of Modern Applied Statistical Methods

SAS’s PROC MIXED can be problematic when analyzing data from randomized longitudinal two-group designs when observations are missing over time. Overall (1996, 1999) and colleagues found a number of procedures that are effective in controlling the number of false positives (Type I errors) and are yet sensitive (powerful) to detect treatment effects. Two favorable methods incorporate time in study and baseline scores to model the missing data mechanism; one method was a single-stage PROC MIXED ANCOVA solution and the other was a two-stage endpoint analysis using the change scores as dependent scores. Because the twostage approach can lack sensitivity to …

Go to article

Type I Error Rates For A One Factor Within-Subjects Design With Missing Values, Miguel A. Padilla, James Algina Nov 2004

Type I Error Rates For A One Factor Within-Subjects Design With Missing Values, Miguel A. Padilla, James Algina

Journal of Modern Applied Statistical Methods

Missing data are a common problem in educational research. A promising technique, that can be implemented in SAS PROC MIXED and is therefore widely available, is to use maximum likelihood to estimate model parameters and base hypothesis tests on these estimates. However, it is not clear which test statistic in PROC MIXED performs better with missing data. The performance of the Hotelling- Lawley-McKeon and Kenward-Roger omnibus test statistics on the means for a single factor withinsubject ANOVA are compared. The results indicate that the Kenward-Roger statistic performed better in terms of keeping the Type I error close to the nominal …

Go to article

Size And Power Of The Reset Test As Applied To Systems Of Equations: A Bootstrap Approach, Ghazi Shukur, Panagiotis Mantalos Nov 2004

Size And Power Of The Reset Test As Applied To Systems Of Equations: A Bootstrap Approach, Ghazi Shukur, Panagiotis Mantalos

Journal of Modern Applied Statistical Methods

The size and power of various generalization of the RESET test for functional misspecification are investigated, using the “Bootsrap critical values”, in systems ranging from one to ten equations. The properties of 8 versions of the test are studied using Monte Carlo methods. The results are then compared with another study of Shukur and Edgerton (2002), in which they used the asymptotic critical values instead and found that in general only one version of the tests works well regarding size properties. In our study, when applying the bootstrap critical values, we find that all the tests exhibits correct size even …

Go to article

Variance Stabilizing Power Transformation For Time Series, Victor M. Guerrero, Rafael Perera Nov 2004

Variance Stabilizing Power Transformation For Time Series, Victor M. Guerrero, Rafael Perera

Journal of Modern Applied Statistical Methods

A confidence interval was derived for the index of a power transformation that stabilizes the variance of a time-series. The process starts from a model-independent procedure that minimizes a coefficient of variation to yield a point estimate of the transformation index. The confidence coefficient of the interval is calibrated through a simulation.

Go to article

Multivariate Contrasts For Repeated Measures Designs Under Assumption Violations, Lisa M. Lix, Aynslie M. Hinds Nov 2004

Multivariate Contrasts For Repeated Measures Designs Under Assumption Violations, Lisa M. Lix, Aynslie M. Hinds

Journal of Modern Applied Statistical Methods

Conventional and approximate degrees of freedom procedures for testing multivariate interaction contrasts in groups by trials repeated measures designs were compared under assumption violation conditions. Procedures were based on either least-squares or robust estimators. Power generally favored test procedures based on robust estimators for non-normal distributions, but was influenced by the degree of departure from non-normality, definition of power, and magnitude of the multivariate effect size.

Go to article

Interval Estimation For The Scale Parameter Of Burr Type X Distribution Based On Grouped Data, Amjad D. Al-Nasser, Ayman Baklizi Nov 2004

Interval Estimation For The Scale Parameter Of Burr Type X Distribution Based On Grouped Data, Amjad D. Al-Nasser, Ayman Baklizi

Journal of Modern Applied Statistical Methods

The application of some bootstrap type intervals for the scale parameter of the Burr type X distribution with grouped data is proposed. The general asymptotic confidence interval procedure (Chen & Mi, 2001) is studied. The performance of these intervals is investigated and compared. Some of the bootstrap intervals give better performance for situations of small sample size and heavy censoring.

Go to article

An Algorithm And Code For Computing Exact Critical Values For The Kruskal-Wallis Nonparametric One-Way Anova, Sikha Bagui, Subhash Bagui Nov 2004

An Algorithm And Code For Computing Exact Critical Values For The Kruskal-Wallis Nonparametric One-Way Anova, Sikha Bagui, Subhash Bagui

Journal of Modern Applied Statistical Methods

In this article, an algorithm and code to compute exact critical values (or percentiles) for Kruskal-Wallis test on k independent treatment populations with equal or unequal sample sizes using Visual Basic (VB.NET) is provided. This program has the ability to calculate critical values for any k , sample sizes (ni ) , and significance level (α ) . An exact critical value table for k = 4 is also developed. The table will be useful to practitioners since it is not available in standard nonparametric statistics texts. The program can also be used to compute any other …

Go to article

Pseudo-Random Number Generation In R For Commonly Used Multivariate Distributions, Hakan Demirtas Nov 2004

Pseudo-Random Number Generation In R For Commonly Used Multivariate Distributions, Hakan Demirtas

Journal of Modern Applied Statistical Methods

An increasing number of practitioners and applied statisticians have started using the R programming system in recent years for their computing and data analysis needs. As far as pseudo-random number generation is concerned, the built-in generator in R does not contain multivariate distributions. In this article, R routines for widely used multivariate distributions are presented.

Go to article

Confidence Intervals On Subsets May Be Misleading, Juliet Popper Shaffer Nov 2004

Confidence Intervals On Subsets May Be Misleading, Juliet Popper Shaffer

Journal of Modern Applied Statistical Methods

A combination of hypothesis testing and confidence interval construction is often used in social and behavioral science studies. Sometimes confidence intervals are computed or reported only if a null hypothesis is rejected, perhaps to see whether the range of values is of practical importance. Sometimes they are constructed or reported only if a null hypothesis is accepted, in order to assess the range of plausible nonnull values due to inadequate power to detect them. Even if always computed, they are interpreted differently, depending on whether the null value is or is not included. Furthermore, many studies in which the null …

Go to article

Mentoring Doctoral Students: A Personal Perspective, Bruce W. Hall Nov 2004

Mentoring Doctoral Students: A Personal Perspective, Bruce W. Hall

Journal of Modern Applied Statistical Methods

In this brief essay, I reflect on the mentoring process based on advising over thirty doctoral students in measurement, evaluation, and research. There is considerable cause for optimism, and it is among the professors’ highest honor to mentor the doctoral student.

Go to article

On A Simple Method For Analyzing Multivariate Survival Data Using Sample Survey Methods, Pingfu Fu, J. Sunil Rao Nov 2004

On A Simple Method For Analyzing Multivariate Survival Data Using Sample Survey Methods, Pingfu Fu, J. Sunil Rao

Journal of Modern Applied Statistical Methods

A simple technique is illustrated for analyzing multivariate survival data. The data situation arises when an individual records multiple survival events, or when individuals recording single survival events are grouped into clusters. Past work has focused on developing new methods to handle such data. Here, we use a connection between Poisson regression and survival modeling and a cluster sampling approach to adjust the variance estimates. The approach requires parametric assumption for the marginal hazard function, but avoids specification of a joint multivariate survival distribution. A simulation study demonstrates the proposed approach is a competing method of recent developed marginal approaches …

Go to article

Monte Carlo Evaluation Of Ordinal D With Improved Confidence Interval, Du Feng, Norman Cliff Nov 2004

Monte Carlo Evaluation Of Ordinal D With Improved Confidence Interval, Du Feng, Norman Cliff

Journal of Modern Applied Statistical Methods

This article reports a Monte Carlo evaluation of ordinal statistic d with modified confidence intervals (CI) for location comparison of two independent groups under various conditions. Type I error rate, power, and coverage of CI of d were compared to those of the Welch's t-test.

Go to article

Confidence Elicitation And Anchoring In The Respondent-Generated Intervals (Rgi) Protocol, Liping Chu, S. James Press, Judith M. Tanur Nov 2004

Confidence Elicitation And Anchoring In The Respondent-Generated Intervals (Rgi) Protocol, Liping Chu, S. James Press, Judith M. Tanur

Journal of Modern Applied Statistical Methods

The Respondent-Generated Intervals protocol (RGI) has been used to have respondents recall the answer to a factual question by giving not only a point estimate but also bounds within which they feel it is almost certain that the true value of the quantity being reported upon falls. The RGI protocol is elaborated in this article with the goal of improving the accuracy of the estimators by introducing cueing mechanisms to direct confident (and thus presumably accurate) respondents to give shorter intervals and less confident (and thus presumably less accurate) respondents to give longer ones.

Go to article

A Note On Extending Scheffé’S Modified Multiple-Comparison Procedure To Other Analysis Situations, Xinyue Zhou, Joel R. Levin Nov 2004

A Note On Extending Scheffé’S Modified Multiple-Comparison Procedure To Other Analysis Situations, Xinyue Zhou, Joel R. Levin

Journal of Modern Applied Statistical Methods

This article extends Scheffé’s modified (sequential) multiple-comparison procedure in one-way analysisof- variance to other analysis situations, including interaction comparisons in factorial ANOVA designs, tests of partial regression coefficients in multiple-regression analysis, and comparisons of means in onefactor multivariate analyses of variance. Researchers who are concerned with maintaining familywise Type I error rates while increasing statistical power relative to the original (simultaneous) Scheffé-based procedures are encouraged to consider these improved multiple-comparison methods.

Go to article

A Modification Of The Em Algorithm To Estimate An Andersen-Gill Gamma Frailty Model For Multivariate Failure Time Data, Maria Antònia Barceló, Marc Saez Nov 2004

A Modification Of The Em Algorithm To Estimate An Andersen-Gill Gamma Frailty Model For Multivariate Failure Time Data, Maria Antònia Barceló, Marc Saez

Journal of Modern Applied Statistical Methods

A modification of the Andersen-Gill gamma shared frailty model is presented. The variance of the frailty is directly modeled by means of a generalized linear model, the EM algorithm is modified in order to simultaneously estimate a semiparametric model for the failure times and a model for the variance of the frailty. A simulation study is conducted to evaluate the performance of the proposed algorithm (EMB algorithm) and compared with other methods, a marginal model, and a conditional model. Multivariate data from a nosocomial infection study is used to illustrate the methods. The EMB fit turned out to be better …

Go to article

Laboratory Routines Cause Animal Stress, Jonathan P. Balcombe, Neal D. Barnard, Chad Sandusky Nov 2004

Laboratory Routines Cause Animal Stress, Jonathan P. Balcombe, Neal D. Barnard, Chad Sandusky

Laboratory Experiments Collection

Eighty published studies were appraised to document the potential stress associated with three routine laboratory procedures commonly performed on animals: handling, blood collection, and orogastric gavage. We defined handling as any non-invasive manipulation occurring as part of routine husbandry, including lifting an animal and cleaning or moving an animal's cage. Significant changes in physiologic parameters correlated with stress (e.g., serum or plasma concentrations of corticosterone, glucose, growth hormone or prolactin, heart rate, blood pressure, and behavior) were associated with all three procedures in multiple species in the studies we examined. The results of these studies demonstrated that animals responded with …

Go to article

A Genetic Algorithm Hybrid For Constructing Optimal Response Surface Designs, David Drain, W. Matthew Carlyle, Douglas C. Montgomery, Connie Borror, Christine Anderson-Cook Nov 2004

A Genetic Algorithm Hybrid For Constructing Optimal Response Surface Designs, David Drain, W. Matthew Carlyle, Douglas C. Montgomery, Connie Borror, Christine Anderson-Cook

Mathematics and Statistics Faculty Research & Creative Works

Hybrid heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective, and simple heuristics like simulated annealing fail to find good solutions. One such heuristic hybrid is GASA (genetic algorithm-simulated annealing), developed to take advantage of the exploratory power of the genetic algorithm, while utilizing the local optimum exploitive properties of simulated annealing. the successful application of this method is demonstrated in a difficult design problem with multiple optimization criteria in an irregularly shaped design region. Copyright © 2004 John Wiley & Sons, Ltd.

Go to article

An Overview Of The Respondent-Generated Intervals (Rgi) Approach To Sample Surveys, S. James Press, Judith M. Tanur Nov 2004

An Overview Of The Respondent-Generated Intervals (Rgi) Approach To Sample Surveys, S. James Press, Judith M. Tanur

Journal of Modern Applied Statistical Methods

This article brings together many years of research on the Respondent-Generated Intervals (RGI) approach to recall in factual sample surveys. Additionally presented is new research on the use of RGI in opinion surveys and the use of RGI with gamma-distributed data. The research combines Bayesian hierarchical modeling with various cognitive aspects of sample surveys.

Go to article

Statistics And Technology: Reflections On 35 Years Of Change, James J. Higgins Nov 2004

Statistics And Technology: Reflections On 35 Years Of Change, James J. Higgins

Journal of Modern Applied Statistical Methods

From the days when statistical calculations were done on mechanical calculators to today, technology has transformed the discipline of statistics. More than just giving statisticians the power to crunch numbers, it has fundamentally changed the way we teach, do research, and consult. In this article, I give some examples of this from my 35 years as an academic statistician.

Go to article

“Teaching” In Honor Of Cliff Blair, Howard Stoker Nov 2004

“Teaching” In Honor Of Cliff Blair, Howard Stoker

Journal of Modern Applied Statistical Methods

In this article, I conceptualize teaching as the profession of facilitating and stimulating learning. As “teachers”, we help students acquire learning skills that they may expand on later in their life. I review fifteen principles that facilitate effective learning.

Go to article

A New Goodness-Of-Fit Test For Item Response Theory, John H. Neel Nov 2004

A New Goodness-Of-Fit Test For Item Response Theory, John H. Neel

Journal of Modern Applied Statistical Methods

Chi-square techniques for testing goodness-of-fit in item response theory are shown to give incorrect results. A new measure, CB, based on cumulants is proposed which avoids the arbitrary nature of interval creation found in chi-square techniques. The distribution of CB is estimated using Monte Carlo techniques and critical values for testing goodness-of-fit are given.

Go to article

First Prev Next Last