DOCSLIB.ORG

Asreml User Guide

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Asreml User Guide ASReml User Guide Release 3.0 2009 A R Gilmour NSW Department of Primary Industries, Orange, Australia B J Gogel University of Adelaide, Adelaide, Australia B R Cullis NSW Department of Primary Industries, Wagga Wagga, Australia R Thompson School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London E1 4NS, and Centre for Mathematical and Computational Biology, and Department of Biomathematics and Bioinformatics, Rothamsted Research, Harpenden AL5 2JQ, United Kingdom ASReml User Guide Release 3.0 ASReml is a statistical package that fits linear mixed models using Residual Maximum Likelihood (REML). It is a joint venture between the Biometrics Pro- gram of NSW Department of Primary Industries and the Biomathematics Unit of Rothamsted Research. Statisticians in Britain and Australia have collaborated in its development. Main authors: A. R. Gilmour, B. J. Gogel, B. R. Cullis and R. Thompson Other contributors: D. Butler, M. Cherry, D. Collins, G. Dutkowski, S. A. Harding, K. Haskard, A. Kelly, S. G. Nielsen, A. Smith, A. P. Verbyla, S. J. Welham and I. M. S. White. Author email addresses [email protected] [email protected] [email protected] [email protected] Copyright Notice Copyright °c 2009, NSW Department of Industry and Investment. All rights reserved. Except as permitted under the Copyright Act 1968 (Commonwealth of Aus- tralia), no part of the publication may be reproduced by any process, electronic or otherwise, without specific written permission of the copyright owner. Nei- ther may information be stored electronically in any form whatever without such permission. Published by: VSN International Ltd, 5 The Waterhouse, Waterhouse Street, Hemel Hempstead, HP1 1ES, UK E-mail: [email protected] Website: http://www.vsni.co.uk/ The correct bibliographical reference for this document is: Gilmour, A.R., Gogel, B.J., Cullis, B.R., and Thompson, R. 2009 ASReml User Guide Release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK www.vsni.co.uk Preface ASReml is a statistical package that fits linear mixed models using Residual Max- imum Likelihood (REML). It has been under development since 1993 and is a joint venture between the Biometrics Program of NSW Department of Primary Industries and the Biomathematics and Bioinformatics Division (previously the Statistics Department) of Rothamsted Research. Release 2 of ASReml was dis- tributed in 2006. This guide relates to Release 3 first distributed in 2008. Changes ASReml3 in this version are indicated by the word ASReml3 in the margin. Features added ASReml2 in Release 2 have ASReml2 in the margin. Other significant changes to the text Revised 08 are indicated by Revised in the margin. A separate document, ASReml 3 Update, is available to highlight the changes from Release 2.00. Linear mixed effects models provide a rich and flexible tool for the analysis of many data sets commonly arising in the agricultural, biological, medical and en- vironmental sciences. Typical applications include the analysis of (un)balanced longitudinal data, repeated measures analysis, the analysis of (un)balanced de- signed experiments, the analysis of multi-environment trials, the analysis of both univariate and multivariate animal breeding and genetics data and the analysis of regular or irregular spatial data. ASReml provides a stable platform for delivering well established procedures while also delivering current research in the application of linear mixed models. The strength of ASReml is the use of the Average Information (AI) algorithm and sparse matrix methods for fitting the linear mixed model. This enables it to analyse large and complex data sets quite efficiently. One of the strengths of ASReml is the wide range of variance models for the ran- dom effects in the linear mixed model that are available. There is a potential cost for this wide choice. Users should be aware of the dangers of either overfitting or attempting to fit inappropriate variance models to small or highly unbalanced data sets. We stress the importance of using data-driven diagnostics and encour- age the user to read the examples chapter, in which we have attempted to not only present the syntax of ASReml in the context of real analyses but also to i Preface ii indicate some of the modelling approaches we have found useful. Revised 08 There are several interfaces to the core functionality of ASReml. The program name ASReml relates to the primary program. ASReml-W refers to the user interface program developed by VSN and distributed with ASReml. ASReml-R refers to the S language interface to a DLL of the core ASReml routines. Genstat uses the same core routines for its REML directive. Both of these have good data manipulation and graphical facilities. The focus in developing ASReml has been on the core engine and it is freely acknowledged that its user interface is not to the level of these other packages. Nevertheless, as the developers interface, it is functional, it gives access to every- thing that the core can do and is especially suited to batch processing and running of large models without the overheads of other systems. Feedback from users is welcome and attempts will be made to rectify identified problems in ASReml. The guide has 15 chapters. Chapter 1 introduces ASReml and describes the con- ventions used in this guide. Chapter 2 outlines some basic theory while Chapter 3 presents an overview of the syntax of ASReml through a simple example. Data file preparation is described in Chapter 4 and Chapter 5 describes how to input data into ASReml. Chapters 6 and 7 are key chapters which present the syntax for specifying the linear model and the variance models for the random effects in the linear mixed model. Chapters 8 and 9 describe special commands for multivari- ate and genetic analyses respectively. Chapter 10 deals with prediction of linear functions of fixed and random effects in the linear mixed model and Chapter 13 presents the syntax for forming functions of variance components. Chapter 11 demonstrates running an ASReml job features available and Chapter 14 gives a detailed explanation of the output files. Chapter 15 gives an overview of the error messages generated in ASReml and some guidance as to their probable cause. The guide concludes with the most extensive chapter which presents the examples. Briefly, the improvements in Release 2 include more robust variance parameter updating so that ’Convergence Failure’ is less likely, extensions to the syntax, inclusion of the Mat´erncorrelation model, ability to plot predicted values, im- provements for testing fixed effects, improvements to the handling of pedigrees and some increases in computational speed. ASReml3 Release 3 contains some extensions to data handling (merging files), pedigree pro- cessing, model specification, theshold models, prediction and examining residuals. The data sets and ASReml input files used in this guide are available from Preface iii http://www.vsni.co.uk/products/asreml as well as in the examples direc- tory of the distribution CD-ROM. They remain the property of the authors or of the original source but may be freely distributed provided the source is acknowl- edged. The authors would appreciate feedback and suggestions for improvements to the program and this guide. Proceeds from the licensing of ASReml are used to support continued develop- ment to implement new developments in the application of linear mixed models. The developmental version is available to supported licensees via a website upon request to VSN. Most users will not need to access the developmental version unless they are actively involved in testing a new development. Acknowledgements We gratefully acknowledge the Grains Research and Development Corporation of Australia for their financial support for our research since 1988. Brian Cullis and Arthur Gilmour wish to thank the NSW Department of Primary Industries, for providing a stimulating and exciting environment for applied biometrical re- search and consulting. Rothamsted Research receives grant-aided support from the Biotechnology and Biological Sciences Research Council of the United King- dom. We sincerely thank Ari Verbyla, Sue Welham, Dave Butler and Alison Smith, the other members of the ASReml ‘team’. Ari contributed the cubic smoothing splines technology, information for the Marker map imputation, on-going test- ing of the software and numerous helpful discussions and insight. Sue Welham has overseen the incorporation of the core into Genstat and contributed to the predict functionality. Dave Butler has developed the ASReml-R class of func- tions. Alison contributed to the development of many of the approaches for the analysis of multi-section trials. We also thank Ian White for his contribution to the spline methodology, and Simon Harding for the licensing and installation software and for his development of the WinASReml environment for running ASReml. The Mat´ernfunction material was developed with Kathy Haskard, a PhD student with Brian Cullis, and the denominator degrees of freedom mate- rial was developed with Sharon Nielsen, a Masters student with Brian Cullis. Damian Collins contributed the PREDICT !PLOT material. Greg Dutkowski has contributed to the extended pedigree options. The asremload.dll functionality is provided under license to VSN. Alison Kelly has helped with the review of the XFA models. Finally, we especially thank our close associates who continually test the enhancements. Preface iv I, Arthur Gilmour, thank Jesus Christ for His forgiveness and personal support over many years. As He has said Behold I stand at the door and knock. If any man hear my voice and open the door I will come in, and sup with him and he with me. (Revelation 3:20). I thank the Lord for the privilege of collaborating with several very gifted people including those involved in the ASReml project, acknowledging their acceptance, generosity, patience and perseverence toward a boy from Boree Creek.
Load more

Recommended publications
  • The Top Resources for Learning 13 Statistical Topics
    The Analysis Factor Ebook: The Top Resources for Learning 13 Statistical Topics The Top Resources for Learning 11131333 Statistical Topics By Karen Grace-Martin Founder & President © Copyright 2008 Karen Grace-Martin, All rights reserved www.analysisfactor.com 1 The Analysis Factor Ebook: The Top Resources for Learning 13 Statistical Topics About the Author Karen Grace-Martin is the founder and president of The Analysis Factor . She is a professional statistical consultant with Masters degrees in both applied statistics and social psychology. Her career started in psychology research, where her frustration in applying statistics to her data led her to take more and more statistics classes. She soon realized that her favorite part of research was data analysis, leading to a career change. Her background in experimental research and working with real data has been invaluable in understanding the challenges that researchers face in using statistics and has spurred her passion for deciphering statistics for academic researchers. Karen was a professional statistical consultant at Cornell University for seven years before founding The Analysis Factor. Karen has worked with clients from undergraduate honors students on their first research project to tenured Ivy League professors, as well as non-profits and businesses. Her ability to understand what researchers need and to explain technical information at the level of the researcher’s understanding has been one of her strongest assets as a consultant. She treats all clients with respect, and derives genuine satisfaction from the relief she hears in their voices when they realize that someone can help them. Before consulting, Karen taught statistics courses for economics, psychology, and sociology majors at the University of California, Santa Barbara and Santa Barbara City College.
    [Show full text]
  • Best-Practice Recommendations for Defining, Identifying, and Handling
    Article Organizational Research Methods 16(2) 270-301 ª The Author(s) 2013 Best-Practice Reprints and permission: sagepub.com/journalsPermissions.nav Recommendations for DOI: 10.1177/1094428112470848 orm.sagepub.com Defining, Identifying, and Handling Outliers Herman Aguinis1, Ryan K. Gottfredson1, and Harry Joo1 Abstract The presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. We provide evidence that different ways of defining, identifying, and handling outliers alter substantive research conclusions. Then, we report results of a literature review of 46 methodological sources (i.e., journal articles, book chapters, and books) addressing the topic of outliers, as well as 232 organizational science journal articles mentioning issues about outliers. Our literature review uncovered (a) 14 unique and mutually exclusive outlier defi- nitions, 39 outlier identification techniques, and 20 different ways of handling outliers; (b) inconsistencies in how outliers are defined, identified, and handled in various methodological sources; and (c) confusion and lack of transparency in how outliers are addressed by substantive researchers. We offer guidelines, including decision-making trees, that researchers can follow to define, identify, and handle error, inter- esting, and influential (i.e., model fit and prediction) outliers. Although our emphasis is on regression, structural equation modeling, and multilevel modeling, our general framework forms the basis for a research agenda regarding outliers in the context of other data-analytic approaches. Our recommenda- tions can be used by authors as well as journal editors and reviewers to improve the consistency and transparency of practices regarding the treatment of outliers in organizational science research.
    [Show full text]
  • Society for the Provision of Education in Rural Australia National Rural
    Society for the Provision of Education in Rural Australia National Rural Education Conference July 2006 Hobart, Tasmania Conference Proceedings COMMUNITY, DIVERSITY and INNOVATION in RURAL and REMOTE EDUCATION and TRAINING Edited by Colin Boylan COMMUNITY, DIVERSITY and INNOVATION in RURAL and REMOTE EDUCATION and TRAINING Proceedings of National Rural Education Conference Society for the Provision of Education in Rural Australia Edited by Colin Boylan Society for the Provision of Education in Rural Australia Inc. PO Box 1766 Osborne Park Western Australia 6916 Copyright © Society for the Provision of Education in Rural Australia Inc. 2006 ISBN 0 9775493 0 5 Society for the Provision of Education in Rural Australia, 22nd Annual Conference ii Hobart, Tasmania- July 2006 SPERA Mission and Goals Mission The Society for the Provision of Education in Rural Australia Incorporated (SPERA) links people with a diverse range of interests in education and training to promote the development of rural Australia by: • promoting a positive view of education in rural areas and encouraging innovation and initiative in the provision of rural education services; and • providing a framework for the sharing of concerns, issues and experiences relating to education and training in rural areas. Goals SPERA advances the education and training opportunities for all people in rural Australia by: • promoting State and regional delivery systems which bring about efficient and effective education for people in rural areas; • encouraging both the collection and sharing of relevant information on the provision of education in rural areas; • conducting an annual National Conference to exchange ideas and information about education and training in rural education; and • serving as a national advocate representing rural education and training.
    [Show full text]
  • Bayesian Estimation and Inferenceq
    Greene-50558 book June 21, 2007 15:50 18 BAYESIAN ESTIMATION AND INFERENCEQ 18.1 INTRODUCTION The preceding chapters (and those that follow this one) are focused primarily on para- metric specifications and classical estimation methods. These elements of the economet- ric method present a bit of a methodological dilemma for the researcher. They appear to straightjacket the analyst into a fixed and immutable specification of the model. But in any analysis, there is uncertainty as to the magnitudes, sometimes the signs and, at the extreme, even the meaning of parameters. It is rare that the presentation of a set of empirical results has not been preceded by at least some exploratory analysis. Pro- ponents of the Bayesian methodology argue that the process of “estimation” is not one of deducing the values of fixed parameters, but rather, in accordance with the scientific method, one of continually updating and sharpening our subjective beliefs about the state of the world. Of course, this adherence to a subjective approach to model building is not necessarily a virtue. If one holds that “models” and “parameters” represent objec- tive truths that the analyst seeks to discover, then the subjectivity of Bayesian methods may be less than perfectly comfortable. Contemporary applications of Bayesian methods typically advance little of this the- ological debate. The modern practice of Bayesian econometrics is much more pragmatic. As we will see below in several examples, Bayesian methods have produced some re- markably efficient solutions to difficult estimation problems. Researchers often choose the techniques on practical grounds, rather than in adherence to their philosophical basis; indeed, for some, the Bayesian estimator is merely an algorithm.1 Bayesian methods have have been employed by econometricians since well be- fore Zellner’s classic (1971) presentation of the methodology to economists, but until fairly recently, were more or less at the margin of the field.
    [Show full text]
  • Inequities in Access to Five Types of Healthcare Services
    UNIVERSITY OF CALIFORNIA Los Angeles Rurality and Race: Inequities in Access to Five Types of Healthcare Services A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Public Health by Julia Thornton Caldwell 2015 © Copyright by Julia Thornton Caldwell 2015 ABSTRACT OF THE DISSERTATION Rurality and Race: Inequities in Access to Five Types of Healthcare Services by Julia Thornton Caldwell Doctor of Philosophy in Public Health University of California, Los Angeles, 2015 Professor Chandra L. Ford, Chair Background. Rurality may influence racial/ethnic disparities in access to healthcare. This study sought to: (1) compare and contrast measures used to assess rurality and urbanicity; (2) determine if racial/ethnic disparities in access to healthcare differ for rural vs. urban areas; and, (3) determine if residential segregation and access to healthcare differ for rural vs. urban areas. Sample. The sample was adult respondents to the 2005-2010 Medical Expenditure Panel Survey (MEPS), a nationally representative sample of U.S. households. Each aim had five samples; sample size ranged from 49,839 to 112,125. Measures. Aim 1 involved five measures used to identify rurality relative to urbanicity. Aim 2 and aim 3 involved five self-reported MEPS outcomes indicating whether respondents had (1) a usual source of healthcare, (2) unmet need for healthcare, (3) cholesterol screenings, (4) cervical cancer screenings, or (5) dental visits. The main explanatory factors, which were based on respondents’ residential location, were rurality and residential segregation, which were examined separately for blacks and Hispanics using the isolation index. Respondents’ residential areas were characterized by using geographic identifiers to link the MEPS data with ii census tract and county information available via the American Community Survey (2005-2010), the Area Health Resource File (2010), and five publically available rurality indicators.
    [Show full text]
  • Quantitative Methods for Science, Social Science and Medicine
    Lancaster University Faculty of Science and Technology Department of Mathematics and Statistics QUANTITATIVE METHODS FOR SCIENCE, SOCIAL SCIENCE AND MEDICINE POSTGRADUATE DEGREE COURSE PROGRAMME 2016-2017 This booklet is issued on the condition that it does not form part of any contract between the University and any student. The information given has been made as accurate as possible at the time of going to press, but the University reserves the right to modify or alter without prior notice any of the contents advertised; it may not be possible to offer all courses or components of a course as described in the booklet in each academic session. October 2016. 1 CONTENTS 1 Introduction ............................................................................................. 4 1.1 The Department ................................................................................ 4 1.2 Admission requirements ................................................................... 4 1.3 Course Office .................................................................................... 4 1.4 Staff .................................................................................................. 5 1.5 General Information for Current Students ......................................... 5 2 MSc in Quantitative Methods ................................................................. 6 2.1 Aims and Objectives ......................................................................... 6 2.2 Structure of MSc in Quantitative Methods .......................................
    [Show full text]
  • Antony Fielding B. Sc. (Econ.), M. Sc., FSS, C.Stat. MILT
    Antony Fielding B. Sc. (Econ.), M. Sc., FSS, C.Stat. MILT CURRICULUM VITAE 30th October 2005 Date of Birth: 12.5.44 Married with three daughters Next of Kin: Alison Ann Fielding. Education, Qualifications and Recognitions: 1955-1962 Chadderton Grammar School: GCE "O" Level (1959) 9 subjects. GCE "A" Level (1961) History (with distinction), Economics, Pure Mathematics. GCE "A" Level (1992) Pure Mathematics (with distinction), History (with distinction), Economics. GCE "S" Level (1962) History, Pure Mathematics. 1962 Awarded State Scholarship. 1962-1966 London School of Economics (University of London): B.Sc. (Econ.), Specialism: Statistics. M.Sc., Statistics and Operational Research. 1966 Elected FSS, Fellow of the Royal Statistical Society. 1993 Awarded Professional Status of C.Stat. (Chartered Statistician). 2002 Member of the Institute of Learning and Teaching, now Higher Education Academy. Career Posts to Date: 2003- to date Reader in Social and Educational Statistics, University of Birmingham. 1976 -2003 Lecturer then Senior Lecturer in Social Statistics, University of Birmingham. 1972 - 1976 Lecturer in Statistics, London School of Economics, University of London. 1966 - 1972 Assistant Lecturer and then Lecturer, Department of Statistics, University of Edinburgh. 1 1965 -1966 Research Assistant, Unit for Economic and Statistical Studies in Higher Education, London School of Economics. Other Relevant Honours: 2005 Appointed by Royal Statistical Society as Editor of its Journal (Journal of the Royal Statistical Society, Series A, Statistics in Society). 2005 Birmingham LEA: Governors Recognition Certificate for Services to Birmingham’s Children. 2004 Prize Awarded for a Best Paper in Invited Presentations to 2nd Workshop on Correlated Data Modelling, Scientific Meeting in Honour of Diego de Castro: Common Ideas in Biometrics and Econometrics, University of Turin (with Spencer, N.).
    [Show full text]
  • A User's Guide to Mlwin
    A User's Guide to MLwiN Version 2.10 by Jon Rasbash, Fiona Steele, William J. Browne & Harvey Goldstein Centre for Multilevel Modelling, University of Bristol ii A User's Guide to MLwiN Copyright © 2009 Jon Rasbash, Fiona Steele, William J. Browne and Harvey Goldstein. All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, for any purpose other than the owner's personal use, without the prior written permission of one of the copyright holders. ISBN: 978-0-903024-97-6 Printed in the United Kingdom First Printing November 2004. Updated for University of Bristol, October 2005 and February 2009. iii This manual is dedicated to the memory of Ian Lang- ford, a greatly missed friend and colleague. iv Note When working through this manual, you may notice some minor discrepan- cies between the results you get and the screenshots presented here. This is due to two changes that have been made to the software between the Beta version used to create the screenshots in the manual and the current version. The first affects the −2 × log(likelihood) values. The version used to create the screenshots gives a slightly different value to the current version and to MLwiN version 2.02 in some cases. The second is that an improvement to the estimation procedure in the current version may lead to small improvements in some parameter estimates when using (R)IGLS. The version used to create the screenshots gives identical parameter estimates to MLwiN version 2.02.
    [Show full text]
  • Disease Mapping with Winbugs and Mlwin
    Disease Mapping with WinBUGS and MLwiN Andrew B. Lawson Department of Epidemiology and Biostatistics University of South Carolina, USA William J. Browne School of Mathematical Sciences University of Nottingham, UK Carmen L. Vidal Rodeiro Department of Epidemiology and Biostatistics University of South Carolina, USA Disease Mapping with WinBUGS and MLwiN STATISTICS IN PRACTICE Advisory Editor Stephen Senn University College London, UK Founding Editor Vic Barnett Nottingham Trent University, UK Statistics in Practice is an important international series of texts which provide detailed coverage of statistical concepts, methods and worked case studies in specific fields of investigation and study. With sound motivation and many worked practical examples, the books show in down-to-earth terms how to select and use an appropriate range of statistical techniques in a particular practical field within each title’s special topic area. The books provide statistical support for professionals and research workers across a range of employment fields and research environments. Subject areas covered include medicine and pharmaceutics; industry, finance and commerce; public services; the earth and environmental sciences, and so on. The books also provide support to students studying statistical courses applied to the above areas. The demand for graduates to be equipped for the work environment has led to such courses becoming increasingly prevalent at uni- versities and colleges. It is our aim to present judiciously chosen and well-written workbooks to meet everyday practical needs. Feedback of views from readers will be most valuable to monitor the success of this aim. A complete list of titles in this series appears at the end of the volume.
    [Show full text]
  • Simulation Study to Compare the Random Data Generation from Bernoulli Distribution in Popular Statistical Packages
    Simulation Study to Compare the Random Data Generation from Bernoulli Distribution in Popular Statistical Packages Manash Pratim Kashyap Department of Business Administration Assam University, Silchar, India [email protected] Nadeem Shafique Butt PIQC Institute of Quality Lahore, Pakistan [email protected] Dibyojyoti Bhattacharjee Department of Business Administration Assam University, Silchar, India [email protected] Abstract In study of the statistical packages, simulation from probability distributions is one of the important aspects. This paper is based on simulation study from Bernoulli distribution conducted by various popular statistical packages like R, SAS, Minitab, MS Excel and PASW. The accuracy of generated random data is tested through Chi-Square goodness of fit test. This simulation study based on 8685000 random numbers and 27000 tests of significance shows that ability to simulate random data from Bernoulli distribution is best in SAS and is closely followed by R Language, while Minitab showed the worst performance among compared packages. Keywords: Bernoulli distribution, Goodness of Fit, Minitab, MS Excel, PASW, SAS, Simulation, R language 1. Introduction Use of Statistical software is increasing day by day in scientific research, market surveys and educational research. It is therefore necessary to compare the ability and accuracy of different statistical softwares. This study focuses a comparison of random data generation from Bernoulli distribution among five softwares R, SAS, Minitab, MS Excel and PASW (Formerly SPSS). The statistical packages are selected for comparison on the basis of their popularity and wide usage. Simulation is the statistical method to recreate situation, often repeatedly, so that likelihood of various outcomes can be more accurately estimated.
    [Show full text]
  • 113 Sprunt Street 3106E Mcgavran-Greenberg Hall, CB #7420
    CURRICULUM VITAE TODD A. SCHWARTZ HOME : OFFICE (B IOSTATISTICS ): 113 Sprunt Street 3106E McGavran-Greenberg Hall, CB #7420 Chapel Hill, NC 27517 University of North Carolina at Chapel Hill Chapel Hill, NC 27599-7420 PHONE : (919)933-4016 PHONE : (919)966-7280 FAX : (919)966-7285 EMAIL : [email protected] OFFICE (N URSING ): 2013 Carrington Hall, CB #7460 University of North Carolina at Chapel Hill Chapel Hill, NC 27599-7460 PHONE : (919)966-6306 EDUCATION Doctor of Public Health , Biostatistics, 2004 School of Public Health The University of North Carolina at Chapel Hill Doctoral Dissertation: “A Study of Sample Size Re-Calculation with Particular Focus on Active- and Placebo-Controlled Non-Inferiority Trials,” under the direction of Jonathan S. Denne Master of Science , Biostatistics, 1998 School of Public Health The University of North Carolina at Chapel Hill Master’s Paper: “Interim Analyses in Randomized Clinical Trials,” under the direction of Gary G. Koch Bachelor of Arts (Summa Cum Laude), Mathematics (Statistics minor), 1995 Messiah College, Grantham, Pennsylvania PROFESSIONAL EXPERIENCE 2005 to Present Research Assistant Professor Department of Biostatistics Gillings School of Global Public Health The University of North Carolina at Chapel Hill 2005 to Present Research Assistant Professor School of Nursing (Joint Appointment) The University of North Carolina at Chapel Hill 2001 to 2005 Research Instructor Department of Biostatistics School of Public Health The University of North Carolina at Chapel Hill 2001 to 2005 Statistician Research Support Center School of Nursing The University of North Carolina at Chapel Hill 1996 to 2001 Graduate Research Assistant Biometric Consulting Laboratory Department of Biostatistics School of Public Health The University of North Carolina at Chapel Hill 1994 to 1996 Statistical Analyst and Statistical Analyst Intern Center for Biostatistics and Epidemiology Milton S.
    [Show full text]
  • Journal of Transportation and Statistics
    JOURNAL OF TRANSPORTATION AND STATISTICS Volume 6 Number 1, 2003 ISSN 1094-8848 BUREAU OF TRANSPORTATION STATISTICS UNITED STATES DEPARTMENT OF TRANSPORTATION Vo JOURNAL OF TRANSPORTATION lume 6 Number 1, 2003 AND STATISTICS Volume 6 Number 1, 2003 ISSN 1094-8848 CONTENTS DONALD C SHOUP [ with discussion by Carl H Buttke and Eugene D Arnold, Jr ] Truth in Transportation Planning HARIKESH S NAIR + CHANDRA R BHAT Modeling Trip Duration for Mobile Source Emissions Forecasting SHAW-PIN MIAOU, JOON JIN SONG + BANI K MALLICK Roadway Traffic Crash Mapping: A Space-Time Modeling Approach BUREAU OF TRANSPORTATION STATISTICS UNITED STATES DEPARTMENT OF TRANSPORTATION SARAH T BOWLING + LISA AULTMAN-HALL Development of a Random Sampling Procedure for Local Road Traffic Count Locations JOURNAL OF TRANSPORTATION PIYUSH TIWARI, HIDEKAZU ITOH + MASAYUKI DOI Containerized AND STATISTICS Cargo Shipper’s Behavior in China: A Discrete Choice Analysis JOURNAL OF TRANSPORTATION AND STATISTICS JOHN V. WELLS Editor-in-Chief PEG YOUNG Associate Editor MARSHA FENN Managing Editor JENNIFER BRADY Assistant to the Editor-in-Chief DORINDA EDMONDSON Desktop Publisher ALPHA GLASS Editorial Assistant LORISA SMITH Desktop Publisher MARTHA COURTNEY Editor DARCY HERMAN Editor EDITORIAL BOARD KENNETH BUTTON George Mason University TIMOTHY COBURN Abilene Christian University STEPHEN FIENBERG Carnegie Mellon University GENEVIEVE GIULIANO University of Southern California JOSE GOMEZ-IBANEZ Harvard University DAVID GREENE Oak Ridge National Laboratory KINGSLEY HAYNES George Mason University DAVID HENSHER University of Sydney PATRICIA HU Oak Ridge National Laboratory RICHARD JOHN Volpe National Transportation Systems Center, USDOT T.R. LAKSHMANAN Boston University TIMOTHY LOMAX Texas Transportation Institute PETER NIJKAMP Free University KEITH ORD Georgetown University ALAN PISARSKI Consultant ROBERT RAESIDE Napier University JEROME SACKS National Institute of Statistical Sciences TERRY SHELTON U.S.
    [Show full text]