2004 NESS CONTRIBUTED ABSTRACTS |

Alvarez, Enrique - Estimation in stationary Markov renewal processes, with application to earthquake forecasting in Turkey

Choi, Jai Won - A Bayesian Alternative to the Chi-Squared Test of Association in a Two-Way Categorical Table with Intra-Class Correlation

Das, Sonali - Investigating the Effects of Working Conditions on Health Care Quality via Structural Equation Models

Demidenko, Eugene - Clustered Poisson regression or why I don't like GEE

Durairajan, T.M. - Some Information Bounds and Asymptotic Variances

Erhardt, Erik - Bayesian Simultaneous Intervals for Small Area Estimation: An Application to Mapping Mortality Rates in U.S. Health Service Areas

Haughton, Dominique - How to measure age, race and gender effects in ticketing for speeding in Massachusetts

Huang, Lan - Modeling Repeated Binary Responses and Time-Dependent Missing Covariates with Application to a Tree with a Two-Year Periodicity in Flowering Intensity

Jensen, Shane - Prediction of Co-Regulated Genes using Motif Discovery and Clustering

Lai, Yinglei - Statistical methods for identifying differential gene-gene interaction patterns

Levine, Michael - ESTIMATING VARIANCE-COVARIANCE STRUCTURE OF THE NONPARAMETRIC REGRESSION DATA WITH TIME SERIES ERRORS

Li, Lingling - A COMPARISON OF GOODNESS OF FIT TESTS FOR THE LOGISTIC GEE MODEL

Liu, Junfeng - A Statistical Model for Multiple High-throughput Protein-Protein Interaction Assay Assessments

Liu, Zhaohui - Bayesian inference on stochastic volatility under hidden semi-Markov models

L'moudden, Ahmed - TEST OF INDEPENDNECE BASED ON KENDALL'S PROCESS: TABULATE THE PERCENTILES OF CRAMER-VON MESIS STATISTICS BY THE STURN-LIOUVILLE APPROACH

Smith, Robert - GLOBAL HUMAN DEVELOPMENT: EXPLAINING ITS REGIONAL VARIATIONS*

Song, Chang Hong - Zero-inflated Poisson Regression Models

Song, Seongho - Hierarchical models with migration, mutation, and drift: implications for genetic inference

Subramanian, Sundar - ASYMPTOTICALLY EFFICIENT ESTIMATION OF A SURVIVAL FUNCTION IN THE MISSING CENSORING INDICATOR MODEL

Wang, Steve - Statistical Challenges in the Analysis of Mass Extinctions

Wilbur, Jayson - A Two-Stage Nearest-Neighbor Classifier with Application to Microbial Source Tracking

Yu, Yaming - Imputing Missing Data by Monotone Blocks

Zhao, Yifang - Statistical Methods for Discovering Differentially Expressed Genes in Replicated Microarray Experiments

Zhou, Qing - Equi-Energy Sampler With Applications to Mixture Model Simulation and Density of States Calculation

Department of Statistics

University of Connecticut

ealvarez@merlot.stat.uconn.edu

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Worcester Polytechnic Institute* and National Center for Health Statistics**

balnan@wpi.edu* and jwc7@cdc.gov**

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*Department of Statistics

University of Connecticut

+Assistant Professor of Medicine/Ergonomics Coordinator,

University of Connecticut Health Center

*sonali@stat.uconn.edu

*mhchen@stat.uconn.edu

+warren@nso.uchc.edu

*dey@stat.uconn.edu

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Dartmouth College, NH

eugened@dartmouth.edu

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Department of Statistics

Loyola College

Chennai, India

tmdurairajan2001@yahoo.co.uk

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Graduate Student, WPI

erike@WPI.EDU

We propose two methods to construct simultaneous intervals from the optimal individual highest posterior density (HPD) intervals to ensure joint simultaneous coverage of 100(1-alpha)%.

Both methods exhibit the main feature of multiplying the lower bound and dividing the upper bound of the individual HPD intervals by parameters 0

For illustrative purposes we apply our methods to chronic obstructive pulmonary disease (COPD) mortality rates from 1988--92, subset White Males age group 65 and older, for the continental United States consisting of 798 Health Service Areas (HSA).

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Bentley College

Phong Nguyen

Bentley College and General Statistics Office, Hanoi

dhaughton@bentley.edu

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Department of Statistics* and Ecology and Evolutionary Biology+

University of Connecticut

lan@merlot.stat.uconn.edu

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Harvard University

jensen@stat.harvard.edu

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Center for Statistical Genomics and Proteomics

Department of Epidemiology and Public Health

Yale University School of Medicine

yl335@email.med.yale.edu

For a gene of interest, our method can select other genes that have differential gene-gene interaction patterns with this gene in different cell states. Among 10 most frequently selected genes, there are genes hepsin, GSTP1 and AMACR. These 3 genes were recently proposed to be associated with prostate cancer. But, it is difficult to identify genes GSTP1 and AMACR by finding differentially expressed genes. Using tumor suppressor genes PTEN, RB1 and TP53, we identify 7 genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene-gene interaction patterns in different cell states. Our statistical approach is capable of discovering such patterns.

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Purdue University

dahlc@mgmt.purdue.edu, mlevins@stat.purdue.edu

We introduce a model where the data is heteroscedastic with time series based variance-covariance structure

Here ei = F ei-1 + ?i is a stationary AR(1) time series. The variance function f(x) is defined on [0,1] and satisfies very unobtrusive smoothness requirements. This model can be used to describe an exchange rate (with the extraneous covariate being, for example, the current interest rate) and easily generalized to include the trend function g(x). Our main goal is to construct consistent estimators of f(x) and F that are easy to compute and possess good asymptotic properties. We introduce both estimators and then discuss their asymptotic properties and convergence rates.

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Center for Biostatistics in AIDS Research

Department of Biostatistics

Harvard School of Public Health

lingling@sdac.harvard.edu

Scott Evans. Ph.D.

Center for Biostatistics in AIDS Research

Department of Biostatistics

Harvard School of Public Health

evans@sdac.harvard.edu

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Yale Center for Statistical Genomics and Proteomics

Division of Biostatistics

Department of Epidemiology and Public Health

Yale School of Medicine

junfeng.liu@yale.edu, hongyu.zhao@yale.edu

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Department of Statistics

University of Connecticut

Zhaohui.Liu@UConn.edu

Statistical inference will be carried out via the MCMC technique. In Bayesian inference framework, prediction can be easily computed. In addition, we will also discuss model selection by marginal likelihood method, pseudo-Bayes factor, prediction based L-measure, and DIC.

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United Arab Emirate University

ghoudi@uaeu.ac.ae

L'moudden Ahmed

Université de Sherbrooke

Québec, Canada

lmoudden@dmi.usherb.ca

Jean Vaillancourt

Universite de Quebec en Outaouais

Canada

vaillancourt@uqo.ca

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Social Structural Research Inc.

Cambridge, MA

rsmithphd @ aol.com

*Author's Note: With contributions by Kevin Bales, who provided several of his measures for analysis, and by Irina Koltoniuc, who helped prepare the analytic database. Helen Fein underscored the importance of slavery and Philip Gibbs of the SAS Institute clarified some of the nuances of PROC MIXED. Andy Baker, Stanley Guterman, and Sreemoti Mukerjee-Roy critiqued earlier drafts. The views expressed here are the author's and do not necessarily reflect the opinions or policies of the United Nations Development Program or any other organization.

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Department of Statistics

University of Connecticut

changhon@merlot.stat.uconn.edu

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Department of Statistics,

University of Connecticut

seongho@stat.uconn.edu

Dipak K. Dey

Department of Statistics,

University of Connecticut

dey@stat.uconn.edu

Kent E. Holsinger

Department of Ecology and Evolutionary,

University of Connecticut

kent@darwin.eeb.uconn.edu

Keywords: F-Statistics; Finite-Island Model; Genetic Drift; Hierarchical Population Structure Migration; Mutation

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Department of Mathematics and Statistics

University of Maine

Orono

subraman@germain.umemat.maine.edu

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Department of Mathematics and Statistics

Swarthmore College

scwang@swarthmore.edu

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Department of Mathematics and Statistics

Swarthmore College

scwang@swarthmore.edu

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Department of Mathematical Sciences

Worcester Polytechnic Institute

jwilbur@wpi.edu

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Harvard University

yu@stat.harvard.edu

We propose a new method, {\it imputation by monotone blocks} (IMB), to impute missing data for public-use databases. Here a set of conditional models are specified and the missing data are iteratively imputed and re-imputed based on these conditional models. At each step of the imputation a monotone block of missing data is updated. We investigate the frequency properties of this method (bias, interval length, and coverage probability for complete data statistics, etc.) by simulation and derive guidelines on good update strategies for use in practice.

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University of Connecticut

lynn@stat.uconn.edu

fangyu@stat.uconn.edu

yifang@stat.uconn.edu

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(Joint work with Samuel Kou and Wing H. Wong)

Harvard University

zhou@stat.harvard.edu

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