Bayesian Hidden Markov Models for Alcoholism Treatment Tria


 Phebe Harris
 3 years ago
 Views:
Transcription
1 Bayesian Hidden Markov Models for Alcoholism Treatment Trial Data May 12, 2008
2 CoAuthors Dylan Small, Statistics Department, UPenn Kevin Lynch, Treatment Research Center, Upenn Steve Maisto, Psychology Department, Syracuse University Dave Oslin, Treatment Research Center, UPenn
3
4 The Problem N subjects measured on T days: daily drink counts. Want to estimate the average treatment effect on outcome. Day Subject
5 Sample Time Series Subject 61 Subject Drinks 2 Drinks Day Day Subject 142 Subject Drinks 2 Drinks Day Day
6 The Goal of Treatment The main goal: Reduce Alcohol Consumption 1. Does the treatment reduce the frequency of all drinking events  or only certain types of drinking events? Is moderate drinking an acceptable outcome? How does the treatment affect different complex drinking patterns and behaviors? 2. Does the treatment reduce the frequency and/or duration of relapses? What is a relapse? Everybody agrees on the notion of a relapse, but there is no concensus for an operational definition of relapse.
7 What is the Outcome? It s complicated. The subjects are recovering alcoholics, whose drinking behaviors are complex processes that evolve and change through time. Simple models lack the structure to adequately describe these processes (Wang, et. al., 2002).
8 Simple Models Time until first drink/relapse (Ignores all behavior after first drink) Percentage of days drinking (Ignores amount of alcohol that is consumed) Multiple failure time models (Requires definition of a relapse) Drinks per Day 3 Y it day
9 HMM Motivation A wellknown theory of relapse, the cognitivebehavioral model of relapse (McKay, et. al. 2006, Marlatt and Gordon, 1985), suggests that the cause of a relapse is twofold: 1. First, the subject must be in a mental and/or physical condition in which he or she is vulnerable to drinking. That is, if presented with an opportunity to drink, the subject would not be able to mount a coping response. 2. Second, the subject must actually encounter such a highrisk drinking situation.
10 HMM structure Y it is the observation for subject i at time t. Y i1 Y i2 Y i, t 1 Y it Y i, t+1 Y i, T 1 Y it H i1 H i2 H i, t 1 H it H i, t+1 H i, T 1 H it H it is the hidden state for subject i at time t.
11 A Simple HMM with no covariates The completedata likelihood for an HMM factors into three parts: p(y, H θ) = N p(h i1 θ) (1) i=1 N i=1 t=2 N i=1 t=1 T p(h it H (i,t 1),θ) (2) T p(y it H it,θ), (3) where Y and H denote observations and hidden states, and parts (1), (2), and (3) refer to the initial state distribution, the hidden state transitions, and the observations, respectively.
12 Simple HMM Fit: S=5 Fit multinomial distributions for hidden state transitions and observations conditional on hidden states. Data is pooled across individuals: ˆπ = (.79,.11,.01,.07,.01) ˆQ = ˆP = where ˆQ and ˆP denote the hidden state transition matrix, and the observation distributions, respectively.
13 Interpretation of hidden states for S=5 1. Large probabilities on the diagonal of ˆQ hidden states are persistent. 2. Observation Distributions are clinically interpretable: Y it = 1 Y it = 2 Y it = A IM ˆP = SM IH C A SH Abstinence Intermittent Moderate Drinking Steady Moderate Drinking Intermittent Heavy Drinking Steady Heavy Drinking Fitting additional latent states (S = 6, 7) yielded no additional interpretable drinking behaviors.
14 Choosing the number of Hidden States 10fold CV to make outofsample predictions; measure deviance N T D = 2 log ˆP(Y it = y it ). i=1 t=11 HMM Markov MTD Deviance Number of Hidden States Order Number of Lags
15 Question 1: Is Moderate Drinking OK? Question: If the hidden states are persistent, can a subject drink moderately, and not resort to heavy drinking soon after? Define states 4 and 5 as Relapse States. Probability of Avoiding Relapse as a Function of Time Initial State = 1 (A) Initial State = 2 (IM) Initial State = 3 (SM) Probability Day
16 Question 2: What is a Relapse? Currently, there is no universally agreed upon operational definition of relapse. Furthermore, different definitions can have an impact on the estimates of treatments (Maisto, et. al, 2003). Any drink of alcohol A day of heavy drinking Four consecutive drinking days (any amount of alcohol) Any drink of alcohol that follows at least 4 days of abstinence The HMM offers a new databased definition: Any time point at which a subject has a high probability of being in hidden state 4 or 5 ( Intermittent Heavy Drinking or Steady Heavy Drinking ). Estimate the most likely hidden state sequence for each subject using the Viterbi algorithm.
17 Most Likely Sequence 1 Subject (SH) 4(IH) Y it 2 3(SM) Latent State 2(IM) 1 1(A) day
18 Most Likely Sequence (2) Subject (SH) 4(IH) Y it 2 3(SM) Latent State 2(IM) 1 1(A) day
19 A More Complex HMM Incorporate Covariates, possibly timevarying Random Effects Missing data, assuming MAR
20 The Model For hidden state transition probabilities, use a multinomial logit model, where P(H it = s H (i,t 1) = r, X it, β) = exp(xq it β rs ) k exp(xq it β rk ). β rs0 N(µ rs, σ rs ). For observation probabilities, use an ordinal probit model, where P(y sj = 1) P(y sj = 2) P(y sj = 3) x P sj β s 2 4 γ s1 γ s2
21 The Hidden State Transition Matrix The hidden state transition matrix parameters are organized as follows (for S = 3 hidden states): {0} (0,0,...,) {β 120i } (β 121,β 122,...) {β 130i } (β 131,β 132,...) 2 {0} (0,0,...,) {β 220i } (β 221,β 222,...) {β 230i } (β 231,β 232,...) 3 {0} (0,0,...,) {β 320i } (β 321,β 322,...) {β 330i } (β 331,β 332,...) where braces {β} denote a set of random effects, and the rest are fixed effects.
22 The Data The outcome (N = 240 subjects and T = 168 days) is distributed as follows: Y % Missing 17 Total 100
23 Covariates This clinical trial, conducted at UPenn s Treatment Research Center, had 6 arms: treatment/control for Naltrexone, and two therapies vs. control. In the hidden state transition matrix, we include: 1. Treatment (Naltrexone) 2. Therapy 1 3. Therapy 2 4. Female 5. Time In the observation model, we include: 1. Weekend indicator 2. Past Drinking Behavior
24 The Gibbs Sampler 1. Initialize the parameters θ = (β, η, γ, π, µ, σ). 2. H Y obs, θ from its full conditional distribution by evaluating the likelihood using the forward recursion, and then using a stochastic backward recursion for all subjects i = 1, 2,..., N (Scott, 2002). 3. β H from its posterior using Scott s DAFE algorithm (2007), which involves augmented variables and a MetropolisHastings step, or using a randomwalk Metropolis step. 4. µ β, σ from their full conditional distributions, assuming flat or weakly informative priors (Gelman, forthcoming). 5. σ β, µ from their full conditional distributions. 6. Y mis H, η, γ assuming it is missing at random (MAR) using the current batch of parameters. 7. γ H, Y obs, Y mis, η using Cowles (1996) randomwalk MetropolisHastings step. 8. η H, Y obs, Y mis, γ in the standard data augmentation way (Albert and Chib 1993). 9. π H from its full conditional Dirichlet distribution. 10. Repeat steps 29 for g = 2,..., G.
25 Characterizing the Fit: S = 3 ˆπ = (.94,.04,.02) ˆQ = ˆP =
26 The Treatment Effect (Treat = Red, Control = Black) Q(1,1) Q(1,2) Q(1,3) Density Density Density Q(2,1) Q(2,2) Q(2,3) Density Density Density Q(3,1) Q(3,2) Q(3,3) Density Density Density
27 Missing Data Subject Drinks Day
28 Hidden States Posterior Distribution Subject Drinks Day
29 Missing Data Posterior Distribution Subject Drinks Day
30 Missing Data Subject 61 3 Drinks Day
31 Hidden States Posterior Distribution Subject Drinks Day
32 Missing Data Posterior Distribution Subject 61 3 Drinks Day
33 An HMM is a model with a rich structure that can capture complex drinking behaviors as they evolve through time. It corresponds to a wellknown theoretical model for relapse, the cognitivebehavioral model of relapse. We can (1) assess the danger of moderate drinking, and (2) define relapse in a databased way. We can measure treatment effects. We can fit the model to subjects with incomplete data, and we can incorporate random effects.
34 Thanks! shirley
HIDDEN MARKOV MODELS FOR ALCOHOLISM TREATMENT TRIAL DATA
HIDDEN MARKOV MODELS FOR ALCOHOLISM TREATMENT TRIAL DATA By Kenneth E. Shirley, Dylan S. Small, Kevin G. Lynch, Stephen A. Maisto, and David W. Oslin Columbia University, University of Pennsylvania and
More informationBayesX  Software for Bayesian Inference in Structured Additive Regression
BayesX  Software for Bayesian Inference in Structured Additive Regression Thomas Kneib Faculty of Mathematics and Economics, University of Ulm Department of Statistics, LudwigMaximiliansUniversity Munich
More informationBayes and Naïve Bayes. cs534machine Learning
Bayes and aïve Bayes cs534machine Learning Bayes Classifier Generative model learns Prediction is made by and where This is often referred to as the Bayes Classifier, because of the use of the Bayes rule
More informationPooling and Metaanalysis. Tony O Hagan
Pooling and Metaanalysis Tony O Hagan Pooling Synthesising prior information from several experts 2 Multiple experts The case of multiple experts is important When elicitation is used to provide expert
More informationGaussian Processes to Speed up Hamiltonian Monte Carlo
Gaussian Processes to Speed up Hamiltonian Monte Carlo Matthieu Lê Murray, Iain http://videolectures.net/mlss09uk_murray_mcmc/ Rasmussen, Carl Edward. "Gaussian processes to speed up hybrid Monte Carlo
More informationChenfeng Xiong (corresponding), University of Maryland, College Park (cxiong@umd.edu)
Paper Author (s) Chenfeng Xiong (corresponding), University of Maryland, College Park (cxiong@umd.edu) Lei Zhang, University of Maryland, College Park (lei@umd.edu) Paper Title & Number Dynamic Travel
More informationHidden Markov Models for biological systems
Hidden Markov Models for biological systems 1 1 1 1 0 2 2 2 2 N N KN N b o 1 o 2 o 3 o T SS 2005 Heermann  Universität Heidelberg Seite 1 We would like to identify stretches of sequences that are actually
More informationAdaptive Approach to Naltrexone Treatment for Alcoholism
Adaptive Approach to Naltrexone Treatment for Alcoholism David W. Oslin, Kevin G. Lynch, Susan Murphy, Helen M. Pettinati, Kyle M. Kampman, William Dundon, Thomas Ten Have, Peter Gariti, James McKay, Charles
More informationEstimation and comparison of multiple changepoint models
Journal of Econometrics 86 (1998) 221 241 Estimation and comparison of multiple changepoint models Siddhartha Chib* John M. Olin School of Business, Washington University, 1 Brookings Drive, Campus Box
More informationMANBITESDOG BUSINESS CYCLES ONLINE APPENDIX
MANBITESDOG BUSINESS CYCLES ONLINE APPENDIX KRISTOFFER P. NIMARK The next section derives the equilibrium expressions for the beauty contest model from Section 3 of the main paper. This is followed by
More informationTutorial on Markov Chain Monte Carlo
Tutorial on Markov Chain Monte Carlo Kenneth M. Hanson Los Alamos National Laboratory Presented at the 29 th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Technology,
More informationHierarchical Bayesian Modeling of the HIV Response to Therapy
Hierarchical Bayesian Modeling of the HIV Response to Therapy Shane T. Jensen Department of Statistics, The Wharton School, University of Pennsylvania March 23, 2010 Joint Work with Alex Braunstein and
More informationBAYESIAN ECONOMETRICS
BAYESIAN ECONOMETRICS VICTOR CHERNOZHUKOV Bayesian econometrics employs Bayesian methods for inference about economic questions using economic data. In the following, we briefly review these methods and
More informationThe Exponential Family
The Exponential Family David M. Blei Columbia University November 3, 2015 Definition A probability density in the exponential family has this form where p.x j / D h.x/ expf > t.x/ a./g; (1) is the natural
More informationLongitudinal random effects models for genetic analysis of binary data with application to mastitis in dairy cattle
Genet. Sel. Evol. 35 (2003) 457 468 457 INRA, EDP Sciences, 2003 DOI: 10.1051/gse:2003034 Original article Longitudinal random effects models for genetic analysis of binary data with application to mastitis
More informationProblem of Missing Data
VASA Mission of VA Statisticians Association (VASA) Promote & disseminate statistical methodological research relevant to VA studies; Facilitate communication & collaboration among VAaffiliated statisticians;
More informationMonte Carlo and Empirical Methods for Stochastic Inference (MASM11/FMS091)
Monte Carlo and Empirical Methods for Stochastic Inference (MASM11/FMS091) Magnus Wiktorsson Centre for Mathematical Sciences Lund University, Sweden Lecture 5 Sequential Monte Carlo methods I February
More informationSTA 4273H: Statistical Machine Learning
STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct
More informationData Modeling & Analysis Techniques. Probability & Statistics. Manfred Huber 2011 1
Data Modeling & Analysis Techniques Probability & Statistics Manfred Huber 2011 1 Probability and Statistics Probability and statistics are often used interchangeably but are different, related fields
More informationOverview. Longitudinal Data Variation and Correlation Different Approaches. Linear Mixed Models Generalized Linear Mixed Models
Overview 1 Introduction Longitudinal Data Variation and Correlation Different Approaches 2 Mixed Models Linear Mixed Models Generalized Linear Mixed Models 3 Marginal Models Linear Models Generalized Linear
More informationA Bayesian hierarchical surrogate outcome model for multiple sclerosis
A Bayesian hierarchical surrogate outcome model for multiple sclerosis 3 rd Annual ASA New Jersey Chapter / Bayer Statistics Workshop David Ohlssen (Novartis), Luca Pozzi and Heinz Schmidli (Novartis)
More informationFRN Research Report August 2011 Patient Outcomes and Relapse Prevention Up to One Year Post Treatment at La Paloma Treatment Center
Page 1 FRN Research Report August 2011 Patient Outcomes and Relapse Prevention Up to One Year Post Treatment at La Paloma Treatment Center Background La Paloma Treatment Center offers stateofthe art
More informationMonte Carlobased statistical methods (MASM11/FMS091)
Monte Carlobased statistical methods (MASM11/FMS091) Jimmy Olsson Centre for Mathematical Sciences Lund University, Sweden Lecture 5 Sequential Monte Carlo methods I February 5, 2013 J. Olsson Monte Carlobased
More informationDURATION ANALYSIS OF FLEET DYNAMICS
DURATION ANALYSIS OF FLEET DYNAMICS Garth Holloway, University of Reading, garth.holloway@reading.ac.uk David Tomberlin, NOAA Fisheries, david.tomberlin@noaa.gov ABSTRACT Though long a standard technique
More informationIntroduction to Algorithmic Trading Strategies Lecture 2
Introduction to Algorithmic Trading Strategies Lecture 2 Hidden Markov Trading Model Haksun Li haksun.li@numericalmethod.com www.numericalmethod.com Outline Carry trade Momentum Valuation CAPM Markov chain
More informationSpecial Populations in Alcoholics Anonymous. J. Scott Tonigan, Ph.D., Gerard J. Connors, Ph.D., and William R. Miller, Ph.D.
Special Populations in Alcoholics Anonymous J. Scott Tonigan, Ph.D., Gerard J. Connors, Ph.D., and William R. Miller, Ph.D. The vast majority of Alcoholics Anonymous (AA) members in the United States are
More informationEffectiveness of Treatment The Evidence
Effectiveness of Treatment The Evidence The treatment programme at Castle Craig is based on the 12 Step abstinence model. This document describes the evidence for residential and 12 Step treatment programmes.
More informationIntroduction to Markov Chain Monte Carlo
Introduction to Markov Chain Monte Carlo Monte Carlo: sample from a distribution to estimate the distribution to compute max, mean Markov Chain Monte Carlo: sampling using local information Generic problem
More informationBasics of Statistical Machine Learning
CS761 Spring 2013 Advanced Machine Learning Basics of Statistical Machine Learning Lecturer: Xiaojin Zhu jerryzhu@cs.wisc.edu Modern machine learning is rooted in statistics. You will find many familiar
More informationCHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS
Examples: Regression And Path Analysis CHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS Regression analysis with univariate or multivariate dependent variables is a standard procedure for modeling relationships
More informationStatistical Analysis with Missing Data
Statistical Analysis with Missing Data Second Edition RODERICK J. A. LITTLE DONALD B. RUBIN WILEY INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION Contents Preface PARTI OVERVIEW AND BASIC APPROACHES
More informationCentre for Central Banking Studies
Centre for Central Banking Studies Technical Handbook No. 4 Applied Bayesian econometrics for central bankers Andrew Blake and Haroon Mumtaz CCBS Technical Handbook No. 4 Applied Bayesian econometrics
More informationINDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition)
INDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition) Abstract Indirect inference is a simulationbased method for estimating the parameters of economic models. Its
More informationSemiparametric Multinomial Logit Models for the Analysis of Brand Choice Behaviour
Semiparametric Multinomial Logit Models for the Analysis of Brand Choice Behaviour Thomas Kneib Department of Statistics LudwigMaximiliansUniversity Munich joint work with Bernhard Baumgartner & Winfried
More informationHidden Markov Models
8.47 Introduction to omputational Molecular Biology Lecture 7: November 4, 2004 Scribe: HanPang hiu Lecturer: Ross Lippert Editor: Russ ox Hidden Markov Models The G island phenomenon The nucleotide frequencies
More informationApplications of R Software in Bayesian Data Analysis
Article International Journal of Information Science and System, 2012, 1(1): 723 International Journal of Information Science and System Journal homepage: www.modernscientificpress.com/journals/ijinfosci.aspx
More informationBayesian Statistics: Indian Buffet Process
Bayesian Statistics: Indian Buffet Process Ilker Yildirim Department of Brain and Cognitive Sciences University of Rochester Rochester, NY 14627 August 2012 Reference: Most of the material in this note
More informationAPPLIED MISSING DATA ANALYSIS
APPLIED MISSING DATA ANALYSIS Craig K. Enders Series Editor's Note by Todd D. little THE GUILFORD PRESS New York London Contents 1 An Introduction to Missing Data 1 1.1 Introduction 1 1.2 Chapter Overview
More informationBayesian Statistics in One Hour. Patrick Lam
Bayesian Statistics in One Hour Patrick Lam Outline Introduction Bayesian Models Applications Missing Data Hierarchical Models Outline Introduction Bayesian Models Applications Missing Data Hierarchical
More informationHidden Markov Models with Applications to DNA Sequence Analysis. Christopher Nemeth, STORi
Hidden Markov Models with Applications to DNA Sequence Analysis Christopher Nemeth, STORi May 4, 2011 Contents 1 Introduction 1 2 Hidden Markov Models 2 2.1 Introduction.......................................
More informationEconometric Analysis of Cross Section and Panel Data Second Edition. Jeffrey M. Wooldridge. The MIT Press Cambridge, Massachusetts London, England
Econometric Analysis of Cross Section and Panel Data Second Edition Jeffrey M. Wooldridge The MIT Press Cambridge, Massachusetts London, England Preface Acknowledgments xxi xxix I INTRODUCTION AND BACKGROUND
More informationCS 688 Pattern Recognition Lecture 4. Linear Models for Classification
CS 688 Pattern Recognition Lecture 4 Linear Models for Classification Probabilistic generative models Probabilistic discriminative models 1 Generative Approach ( x ) p C k p( C k ) Ck p ( ) ( x Ck ) p(
More informationHidden Markov Models. Terminology and Basic Algorithms
Hidden Markov Models Terminology and Basic Algorithms Motivation We make predictions based on models of observed data (machine learning). A simple model is that observations are assumed to be independent
More informationIncorporating prior information to overcome complete separation problems in discrete choice model estimation
Incorporating prior information to overcome complete separation problems in discrete choice model estimation Bart D. Frischknecht Centre for the Study of Choice, University of Technology, Sydney, bart.frischknecht@uts.edu.au,
More informationAn Introduction to Using WinBUGS for CostEffectiveness Analyses in Health Economics
Slide 1 An Introduction to Using WinBUGS for CostEffectiveness Analyses in Health Economics Dr. Christian Asseburg Centre for Health Economics Part 1 Slide 2 Talk overview Foundations of Bayesian statistics
More informationGilbert W. Fellingham, Ph. D. Michelle Miskin, M. S. C. Shane Reese, Ph. D.
Men s Gilbert W. Fellingham, Ph. D. Michelle Miskin, M. S. C. Shane Reese, Ph. D. Dept. of Statistics Brigham Young University gwf@byu.edu September 26, 2009 Table of Contents Men s 1 2 3 4 5 Men s 6 7
More informationEquivalence Concepts for Social Networks
Equivalence Concepts for Social Networks Tom A.B. Snijders University of Oxford March 26, 2009 c Tom A.B. Snijders (University of Oxford) Equivalences in networks March 26, 2009 1 / 40 Outline Structural
More informationLinda K. Muthén Bengt Muthén. Copyright 2008 Muthén & Muthén www.statmodel.com. Table Of Contents
Mplus Short Courses Topic 2 Regression Analysis, Eploratory Factor Analysis, Confirmatory Factor Analysis, And Structural Equation Modeling For Categorical, Censored, And Count Outcomes Linda K. Muthén
More informationMachine Learning and Statistics: What s the Connection?
Machine Learning and Statistics: What s the Connection? Institute for Adaptive and Neural Computation School of Informatics, University of Edinburgh, UK August 2006 Outline The roots of machine learning
More informationSample Script of an Initial Brief Alcohol Counseling Session
Information Sheet for Behavioral Health Providers in Primary Care Sample Script of an Initial Brief Alcohol Counseling Session Introduce the Subject with a Transitional Statement From your answers it appears
More informationAdaptive Design for Intra Patient Dose Escalation in Phase I Trials in Oncology
Adaptive Design for Intra Patient Dose Escalation in Phase I Trials in Oncology Jeremy M.G. Taylor Laura L. Fernandes University of Michigan, Ann Arbor 19th August, 2011 J.M.G. Taylor, L.L. Fernandes Adaptive
More informationAnalysis of Bayesian Dynamic Linear Models
Analysis of Bayesian Dynamic Linear Models Emily M. Casleton December 17, 2010 1 Introduction The main purpose of this project is to explore the Bayesian analysis of Dynamic Linear Models (DLMs). The main
More informationAdvanced Signal Processing and Digital Noise Reduction
Advanced Signal Processing and Digital Noise Reduction Saeed V. Vaseghi Queen's University of Belfast UK WILEY HTEUBNER A Partnership between John Wiley & Sons and B. G. Teubner Publishers Chichester New
More informationMultiply imputing missing values in data sets with. generalised linear models
Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models Min Lee Robin Mitra School of Mathematics University of Southampton, Southampton,
More informationStatistics in Retail Finance. Chapter 6: Behavioural models
Statistics in Retail Finance 1 Overview > So far we have focussed mainly on application scorecards. In this chapter we shall look at behavioural models. We shall cover the following topics: Behavioural
More informationProbabilistic user behavior models in online stores for recommender systems
Probabilistic user behavior models in online stores for recommender systems Tomoharu Iwata Abstract Recommender systems are widely used in online stores because they are expected to improve both user
More informationThe Causal Effect of Mortgage Refinancing on InterestRate Volatility: Empirical Evidence and Theoretical Implications by Jefferson Duarte
The Causal Effect of Mortgage Refinancing on InterestRate Volatility: Empirical Evidence and Theoretical Implications by Jefferson Duarte Discussion Daniel Smith Simon Fraser University May 4, 2005 Very
More informationElectronic Theses and Dissertations UC Riverside
Electronic Theses and Dissertations UC Riverside Peer Reviewed Title: Bayesian and Nonparametric Approaches to Missing Data Analysis Author: Yu, Yao Acceptance Date: 01 Series: UC Riverside Electronic
More informationStatistics Graduate Courses
Statistics Graduate Courses STAT 7002Topics in StatisticsBiological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.
More informationModeling and Analysis of Call Center Arrival Data: A Bayesian Approach
Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach Refik Soyer * Department of Management Science The George Washington University M. Murat Tarimcilar Department of Management Science
More informationTreatment of Alcoholism
Treatment of Alcoholism Why is it important Prevents further to body by getting people off alcohol. Can prevent death. Helps keep health insurance down. Provides assistance so alcoholics don t t have to
More informationPS 271B: Quantitative Methods II. Lecture Notes
PS 271B: Quantitative Methods II Lecture Notes Langche Zeng zeng@ucsd.edu The Empirical Research Process; Fundamental Methodological Issues 2 Theory; Data; Models/model selection; Estimation; Inference.
More informationMissing Data in Longitudinal Studies: To Impute or not to Impute? Robert Platt, PhD McGill University
Missing Data in Longitudinal Studies: To Impute or not to Impute? Robert Platt, PhD McGill University 1 Outline Missing data definitions Longitudinal data specific issues Methods Simple methods Multiple
More informationSystem Identification for Acoustic Comms.:
System Identification for Acoustic Comms.: New Insights and Approaches for Tracking Sparse and Rapidly Fluctuating Channels Weichang Li and James Preisig Woods Hole Oceanographic Institution The demodulation
More informationSAMPLE SELECTION BIAS IN CREDIT SCORING MODELS
SAMPLE SELECTION BIAS IN CREDIT SCORING MODELS John Banasik, Jonathan Crook Credit Research Centre, University of Edinburgh Lyn Thomas University of Southampton ssm0 The Problem We wish to estimate an
More informationStatistical Models in Data Mining
Statistical Models in Data Mining Sargur N. Srihari University at Buffalo The State University of New York Department of Computer Science and Engineering Department of Biostatistics 1 Srihari Flood of
More informationService Performance Analysis and Improvement for a Ticket Queue with Balking Customers. Long Gao. joint work with Jihong Ou and Susan Xu
Service Performance Analysis and Improvement for a Ticket Queue with Balking Customers joint work with Jihong Ou and Susan Xu THE PENNSYLVANIA STATE UNIVERSITY MSOM, Atlanta June 20, 2006 Outine Introduction
More informationFortgeschrittene Computerintensive Methoden: Finite Mixture Models Steffen Unkel Manuel Eugster, Bettina Grün, Friedrich Leisch, Matthias Schmid
Fortgeschrittene Computerintensive Methoden: Finite Mixture Models Steffen Unkel Manuel Eugster, Bettina Grün, Friedrich Leisch, Matthias Schmid Institut für Statistik LMU München Sommersemester 2013 Outline
More informationProbabilistic methods for postgenomic data integration
Probabilistic methods for postgenomic data integration Dirk Husmeier Biomathematics & Statistics Scotland (BioSS) JMB, The King s Buildings, Edinburgh EH9 3JZ United Kingdom http://wwwbiossacuk/ dirk
More informationMissing Data & How to Deal: An overview of missing data. Melissa Humphries Population Research Center
Missing Data & How to Deal: An overview of missing data Melissa Humphries Population Research Center Goals Discuss ways to evaluate and understand missing data Discuss common missing data methods Know
More informationTagging with Hidden Markov Models
Tagging with Hidden Markov Models Michael Collins 1 Tagging Problems In many NLP problems, we would like to model pairs of sequences. Partofspeech (POS) tagging is perhaps the earliest, and most famous,
More informationRELAPSE PREVENTION WORKBOOK
RELAPSE PREVENTION WORKBOOK Bradley A. Hedges, Ph.D., LPCC Psychologist MidOhio Psychological Services, Inc. 624 East Main Street Lancaster, Ohio 43130 (740) 6870042 bradhedges@mopsohio.com 03/30/2012
More information11. Time series and dynamic linear models
11. Time series and dynamic linear models Objective To introduce the Bayesian approach to the modeling and forecasting of time series. Recommended reading West, M. and Harrison, J. (1997). models, (2 nd
More informationFINDING SUBGROUPS OF ENHANCED TREATMENT EFFECT. Jeremy M G Taylor Jared Foster University of Michigan Steve Ruberg Eli Lilly
FINDING SUBGROUPS OF ENHANCED TREATMENT EFFECT Jeremy M G Taylor Jared Foster University of Michigan Steve Ruberg Eli Lilly 1 1. INTRODUCTION and MOTIVATION 2. PROPOSED METHOD Random Forests Classification
More informationValidation of Software for Bayesian Models using Posterior Quantiles. Samantha R. Cook Andrew Gelman Donald B. Rubin DRAFT
Validation of Software for Bayesian Models using Posterior Quantiles Samantha R. Cook Andrew Gelman Donald B. Rubin DRAFT Abstract We present a simulationbased method designed to establish that software
More informationTime series analysis as a framework for the characterization of waterborne disease outbreaks
Interdisciplinary Perspectives on Drinking Water Risk Assessment and Management (Proceedings of the Santiago (Chile) Symposium, September 1998). IAHS Publ. no. 260, 2000. 127 Time series analysis as a
More informationCalifornia Society of Addiction Medicine (CSAM) Consumer Q&As
C o n s u m e r Q & A 1 California Society of Addiction Medicine (CSAM) Consumer Q&As Q: Is addiction a disease? A: Addiction is a chronic disorder, like heart disease or diabetes. A chronic disorder is
More informationAuxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus
Auxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives
More informationSimultaneous modeling of the impact of treatments on alcohol consumption and quality of. life in the COMBINE study: a coupled hidden Markov analysis
1 RUNNING HEAD: SIMULTANEOUS MODELING OF DRINKING AND QOL IN COMBINE Simultaneous modeling of the impact of treatments on alcohol consumption and quality of life in the COMBINE study: a coupled hidden
More informationOverview of Chemical Addictions Treatment. Psychology 470. Background
Overview of Chemical Addictions Treatment Psychology 470 Introduction to Chemical Additions Steven E. Meier, Ph.D. Listen to the audio lecture while viewing these slides 1 Background Treatment approaches
More informationMore details on the inputs, functionality, and output can be found below.
Overview: The SMEEACT (Software for More Efficient, Ethical, and Affordable Clinical Trials) web interface (http://research.mdacc.tmc.edu/smeeactweb) implements a single analysis of a twoarmed trial comparing
More informationData Mining Chapter 6: Models and Patterns Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University
Data Mining Chapter 6: Models and Patterns Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University Models vs. Patterns Models A model is a high level, global description of a
More informationMissing Data Approach to Handle Delayed Outcomes in Early
Missing Data Approach to Handle Delayed Outcomes in Early Phase Clinical Trials Department of Biostatistics The University of Texas, MD Anderson Cancer Center Outline Introduction Method Conclusion Background
More informationAnalysis of Microdata
Rainer Winkelmann Stefan Boes Analysis of Microdata With 38 Figures and 41 Tables 4y Springer Contents 1 Introduction 1 1.1 What Are Microdata? 1 1.2 Types of Microdata 4 1.2.1 Qualitative Data 4 1.2.2
More informationData a systematic approach
Pattern Discovery on Australian Medical Claims Data a systematic approach Ah Chung Tsoi Senior Member, IEEE, Shu Zhang, Markus Hagenbuchner Member, IEEE Abstract The national health insurance system in
More informationCoOccurring Substance Use and Mental Health Disorders. Joy Chudzynski, PsyD UCLA Integrated Substance Abuse Programs
CoOccurring Substance Use and Mental Health Disorders Joy Chudzynski, PsyD UCLA Integrated Substance Abuse Programs Introduction Overview of the evolving field of CoOccurring Disorders Addiction and
More informationLasso on Categorical Data
Lasso on Categorical Data Yunjin Choi, Rina Park, Michael Seo December 14, 2012 1 Introduction In social science studies, the variables of interest are often categorical, such as race, gender, and nationality.
More informationNote on the EM Algorithm in Linear Regression Model
International Mathematical Forum 4 2009 no. 38 18831889 Note on the M Algorithm in Linear Regression Model JiXia Wang and Yu Miao College of Mathematics and Information Science Henan Normal University
More informationBayesian Penalized Methods for High Dimensional Data
Bayesian Penalized Methods for High Dimensional Data Joseph G. Ibrahim Joint with Hongtu Zhu and Zakaria Khondker What is Covered? Motivation GLRR: Bayesian Generalized Low Rank Regression L2R2: Bayesian
More informationHMM : Viterbi algorithm  a toy example
MM : Viterbi algorithm  a toy example.5.3.4.2 et's consider the following simple MM. This model is composed of 2 states, (high C content) and (low C content). We can for example consider that state characterizes
More information15 Ordinal longitudinal data analysis
15 Ordinal longitudinal data analysis Jeroen K. Vermunt and Jacques A. Hagenaars Tilburg University Introduction Growth data and longitudinal data in general are often of an ordinal nature. For example,
More informationValidation of Software for Bayesian Models Using Posterior Quantiles
Validation of Software for Bayesian Models Using Posterior Quantiles Samantha R. COOK, Andrew GELMAN, and Donald B. RUBIN This article presents a simulationbased method designed to establish the computational
More informationComputational Statistics for Big Data
Lancaster University Computational Statistics for Big Data Author: 1 Supervisors: Paul Fearnhead 1 Emily Fox 2 1 Lancaster University 2 The University of Washington September 1, 2015 Abstract The amount
More informationTracking Algorithms. Lecture17: Stochastic Tracking. Joint Probability and Graphical Model. Probabilistic Tracking
Tracking Algorithms (2015S) Lecture17: Stochastic Tracking Bohyung Han CSE, POSTECH bhhan@postech.ac.kr Deterministic methods Given input video and current state, tracking result is always same. Local
More informationD.G. Counseling Inc.
D.G. Counseling Inc. March 2009 Newsletter T H I S M O N T H W E E N J O Y A N A R T I C L E W R I T T E N B Y J U D I T H F A W E L L ATTENTION: Two of the books Donna Gluck coauthored with Dr. Rob Bollendorf
More informationMOBC Research Highlights Reel. Mitch Karno Mechanisms of Behavior Change Conference San Antonio, Texas June 20, 2015
MOBC Research Highlights Reel Mitch Karno Mechanisms of Behavior Change Conference San Antonio, Texas June 20, 2015 Starring Change Talk Attentional Bias SelfEfficacy Social Network Craving Study
More informationJames R. McKay, Ph.D.
Effect of Patient Choice in an Adaptive Sequential Randomization Trial of Treatment for Alcohol and Cocaine Dependence James R. McKay, Ph.D. University of Pennsylvania Philadelphia VAMC Limitations of
More informationLatent Class (Finite Mixture) Segments How to find them and what to do with them
Latent Class (Finite Mixture) Segments How to find them and what to do with them Jay Magidson Statistical Innovations Inc. Belmont, MA USA www.statisticalinnovations.com Sensometrics 2010, Rotterdam Overview
More informationWhat s going on? Discovering SpatioTemporal Dependencies in Dynamic Scenes
What s going on? Discovering SpatioTemporal Dependencies in Dynamic Scenes Daniel Kuettel, Michael D Breitenstein, Luc Van Gool, Vittorio Ferrari Computer Vision Laboratory, ETH Zurich @visioneeethzch
More informationSample Size Designs to Assess Controls
Sample Size Designs to Assess Controls B. Ricky Rambharat, PhD, PStat Lead Statistician Office of the Comptroller of the Currency U.S. Department of the Treasury Washington, DC FCSM Research Conference
More information