Bayesian and Classical Approaches to Inference and Model Averaging
|
|
- Luke Dawson
- 5 years ago
- Views:
Transcription
1 Bayesian and Classical Approaches to Inference and Model Averaging Course Tutors Gernot Doppelhofer NHH Melvyn Weeks University of Cambridge Location Norges Bank Oslo Date 5-8 May 2008
2 The Course The course provides an introduction to Bayesian inference from the perspectives of a classically trained econometrician. Beginning with Bayes Theorem applied to random parameters, the material examines a number of key issues for classical estimation, and where appropriate considers the Bayesian analog. The material moves from the fundamental dichotomy between fixed and random quantities in classical estimation, and considers the role of the principle distinction between the two approaches - namely random versus fixed parameters. We examine how key notions such as convergence, and the use of simulation as an inference tool differs across the two approaches. The translation of fixed versus random effects panel data models into a Bayesian framework provides a convenient introduction to the use of hierarchical and non-hierarchical priors. The curse of dimensionality which plagues inference in a broad class of latent variable model is used to motivate both the use and development of simulation methods in econometrics. Given fundamental differences in the treatment of missing data between Classical and Bayesian approaches, we consider how the use of Data Augmentation presents a powerful tool to circumvent dimensionality problems in a class of Bayesian models. A number of applications are considered. These are Bayesian model averaging applied to the problem of conducting inference on the nature of financial crises. We also introduce new work on identifying complementarities between policy instruments in estimating model of economic growth. Finally, we apply model averaging to problems of economic forecasting. 2
3 Course Outline 1. Introduction to Bayesian Inference 2. Bayesian Model Averaging (BMA) and Frequentist Model Averaging 3. Simulation Methods in Classical and Bayesian Modelling 4. Model Averaging: Applications from to financial crises and economic growth 5. Identifying Jointness in the effects of policy instruments 6. Determinants of Financial Crises 7. Forecasting using Model Averaging 3
4 Bayesian versus Classical Approaches to Inference Agenda Monday 5th May 1 Basics of Bayesian Inference 1. Probability Statements about Unknown Parameters Probability Statements and Interval Estimation 2. Motivation: Multiple Models 3. Bayesian and Classical Objects 4. Binary Uncertainty Bayesian Hypothesis Testing Posterior Odds: A Derivation 5. Bayes Theorem for Events, Random Variables and Parameters 6. Aspects of Bayesian Inference 7. de Finetti s Representation Theorem 8. Pragmatic Bayesians I: Constructing the Posterior II: Specifications of Unobserved Heterogeneity 9. Simultaneous Bayesian and Classical Inference 10. Pointwise Convergence versus Convergence to a Distribution 11. Prior Uncertainty Noninformative and Improper Priors Prior Structures and BMA Natural Conjugate Priors 12. Hierarchical Priors 4
5 Hierarchical Priors I: Panel Data Hierarchical Priors II: Model Averaging Hierarchical Priors III: Unobserved Heterogeneity in the Returns to Schooling Hierarchical Priors IV: Stochastic Frontier Panel Data Models 5
6 Tuesday 6th May 2 Model Averaging in the Linear Regression Model 1. Motivation 2. Statistical Framework Decision Theory Unconditional Distribution Bayesian Hypothesis Testing 3. Linear Regression Model Normal Linear Model Likelihood Function Prior Distributions Posterior Analysis Model Space 4. Conclusion 5. Further Readings 6
7 Wednesday 7th May. 3 Simulation-Based Estimation and Inference 1. Overview 2. Combining Prior and Sample Information 3. Simulation Methods: A Classical Reference Point 4. Simulation Estimation and Discrete Choice Random Coefficient Mixed Logit 5. Simulated Maximum Likelihood estimation The Attraction of the Bayesian Paradigm 6. Bayesian inference in the Binomial Probit Model 7. Data Augmentation with Missing Data Bayesian Analysis of Binary Choice with Data Augmentation Data Augmentation: A General Framework 8. The Integral Transform Theorem 9. Bayesian inference in the Mixed Logit Model 10. Posterior Sampling: Taxonomy 11. Posterior Simulation using MCMC 12. MCMC Methods Gibbs Sampling: Some Specifics 13. Ergodicity 14. The Metropolis Method The Metropolis-Hastings Method 15. Exploring the Model Space The MC3 Algorithm 7
8 Thursday 8th May. 4 Model Averaging and Applications 1. Introduction 2. Determinants of Economic Growth Model Space Posterior Distribution Jointness Nonlinearities and Thresholds Robustness 3. Epilogue: Robust Policy 8
9 Principle Texts [1] Albert, J. and S. Chib (1993) Bayesian Analysis of Binary and Polychotomous Response Data. Journal of the American Statistical Association, 88, [2] Chib, S.and E. Greenberg (1996) Bayesian Analysis of Multivariate Probit Models, Research Paper, John M. Olin School of Business, Washington University. [3] Koop, G. (2003). Bayesian Econometrics. Wiley. [4] Koop, G. and Poirier, D. J. and Tobias, L. (2007) Bayesian Econometric Methods (Econometric Exercises), Cambridge University Press. [5] Lancaster, T. (2004). An Introduction to Modern Bayesian Econometrics. Blackwell. [6] Train, K. E. (2005) Discrete Choice Methods with Simulation. Cambridge University Press. [7] Mariano, B. M. Weeks, and T. Schuermann (eds) (2000). Simulation Based Inference: Theory and Applications, Cambridge University Press. [8] Mariano, B., Schuermann, T. and M. Weeks. (2002). Simulation-Based Inference in Econometrics: Theory and applications. Cambridge University Press. [9] Van Dijk, H.K., A. Monfort and B.W. Brown (eds) (1995). Econometric Inference Using Simulation Techniques, John Wiley and Sons, Chichester, West Sussex, England. Basics of Bayesian Inference [1] Akaike H Information Theory and an Extension of the Maximum Likelihood Principle. In Second International Symposium on Information Theory, Petrov B, Csake F. (eds). Akademiai Kiado: Budapest. [2] Geweke J Bayesian Treatment of the Independent Student-t Linear Model. Journal of Applied Econometrics 8: S19-S40. [3] Kass R, Raftery A Bayes Factors. Journal of the American Statistical Association 90(430): [4] Leamer E.E Multicollinearity: A Bayesian Interpretation. Review of Economics and Statistics 55(3):
10 Model Averaging in the Linear Regression Model [1] Brock W.A, Durlauf, S.N, West K.D Policy Evaluation in Uncertain Economic Environments (with Comments and Discussion). Brookings Papers of Economic Acitivity 1: [2] Doppelhofer G Model Averaging. Palgrave Dictionary of Economics. 2nd edition. [3] Fernandez C, Ley, E., Steel, M. 2001b. Benchmark Priors for Bayesian Model Averaging. Journal of Econometrics 100(2): [4] Hansen B Least Squares Model Averaging. Econometrica 75(4): [5] Hjort N., Claeskens G Frequentist Model Average Estimators. Journal of the American Statistical Association 98(464): [6] Hoeting J., Madigan D, Raftery A., Volinsky C.T Bayesian Model Averaging: A Tutorial. Statistical Science 14(4): [7] Koop G Bayesian Econometrics. Wiley: Chichester. [8] Leamer E.E Specification Searches: Ad Hoc Inference with Nonexperimental Data. Wiley: New York. [9] Poirier D.J Intermediate Statistics and Econometrics. MIT: Mass. [10] Wasserman L Bayesian Model Selection and Model Averaging. Journal of Mathematical Psychology 44(1): Simulation-Based Estimation and Inference [1] Albert and Chib (1993) - Probit [2] McCullogh and Rossi (1994) - Multinomial Probit [3] Koop and Poirier (1996) - Nested Logit [4] Mariano, Schuermann and Weeks (2000) - Bayesian Simulation Methods [5] Allenby (1997) - Mixed Logit [6] Chib and Greenberg (1996) - Interrelated Discrete Response [7] Train (2000) - Classical versus Bayesian simulation 10
11 [8] Koop (2003) - Bayesian Econometrics [9] Lancaster (2005) - An Introduction to Modern Bayesian Econometrics Model Averaging and Applications [1] Brock W.A, Durlauf, S.N, West, K.D Policy Evaluation in Uncertain Economic Environments (with Comments and Discussion). Brookings Papers of Economic Acitivity 1: [2] Crespo-Cuaresma, J., Doppelhofer G Nonlinearities in Cross-Country Growth Regressions: A Bayesian Averaging of Thresholds (BAT) Approach. J. Macroeconomics 29: [3] Doppelhofer, G., Weeks, M. (Forthcoming). Jointness of Growth Determinants. Journal of Applied Econometrics. [4] Fernandez C, Ley, E., Steel, M.F.J. 2001a. Model Uncertainty in Cross-Country Regression. J. Applied Econometrics 16: [5] Sala-i-Martin, X, Doppelhofer, G., Miller, R.I Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach. American Economic Review 94(4): Prior Uncertainty [1] Eicher, T.S., C. Papageorgiou, and A.E. Raftery (2007) Determining Growth Determinants: Default Priors and Predictive Performance in Bayesian Model Averaging. Paper, Department of Economics University of Washington. [2] Fernandez C, Ley E, Steel MFJ. 2001b. Benchmark Priors for Bayesian Model Averaging. Journal of Econometrics 100(2): [3] Ley, E. and M.F.J. Steel (2008) On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression MPRA Paper No
In addition, a working knowledge of Matlab programming language is required to perform well in the course.
Topics in Bayesian Econometrics Fall 2011 Fabio Canova Outline The course present a self-contained exposition of Bayesian methods applied to reduced form models, to structural VARs, to a class of state
More informationAdvanced Bayesian Models for the Social Sciences
Advanced Bayesian Models for the Social Sciences Jeff Harden Department of Political Science, University of Colorado Boulder jeffrey.harden@colorado.edu Daniel Stegmueller Department of Government, University
More informationAdvanced Bayesian Models for the Social Sciences. TA: Elizabeth Menninga (University of North Carolina, Chapel Hill)
Advanced Bayesian Models for the Social Sciences Instructors: Week 1&2: Skyler J. Cranmer Department of Political Science University of North Carolina, Chapel Hill skyler@unc.edu Week 3&4: Daniel Stegmueller
More informationOrdinal Data Modeling
Valen E. Johnson James H. Albert Ordinal Data Modeling With 73 illustrations I ". Springer Contents Preface v 1 Review of Classical and Bayesian Inference 1 1.1 Learning about a binomial proportion 1 1.1.1
More informationGeorgetown University ECON-616, Fall Macroeconometrics. URL: Office Hours: by appointment
Georgetown University ECON-616, Fall 2016 Macroeconometrics Instructor: Ed Herbst E-mail: ed.herbst@gmail.com URL: http://edherbst.net/ Office Hours: by appointment Scheduled Class Time and Organization:
More informationA Brief Introduction to Bayesian Statistics
A Brief Introduction to Statistics David Kaplan Department of Educational Psychology Methods for Social Policy Research and, Washington, DC 2017 1 / 37 The Reverend Thomas Bayes, 1701 1761 2 / 37 Pierre-Simon
More informationEconometrics II - Time Series Analysis
University of Pennsylvania Economics 706, Spring 2008 Econometrics II - Time Series Analysis Instructor: Frank Schorfheide; Room 525, McNeil Building E-mail: schorf@ssc.upenn.edu URL: http://www.econ.upenn.edu/
More informationFor general queries, contact
Much of the work in Bayesian econometrics has focused on showing the value of Bayesian methods for parametric models (see, for example, Geweke (2005), Koop (2003), Li and Tobias (2011), and Rossi, Allenby,
More informationBayesian Inference Bayes Laplace
Bayesian Inference Bayes Laplace Course objective The aim of this course is to introduce the modern approach to Bayesian statistics, emphasizing the computational aspects and the differences between the
More informationAn Exercise in Bayesian Econometric Analysis Probit and Linear Probability Models
Utah State University DigitalCommons@USU All Graduate Plan B and other Reports Graduate Studies 5-1-2014 An Exercise in Bayesian Econometric Analysis Probit and Linear Probability Models Brooke Jeneane
More informationBayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm
Journal of Social and Development Sciences Vol. 4, No. 4, pp. 93-97, Apr 203 (ISSN 222-52) Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm Henry De-Graft Acquah University
More informationData Analysis Using Regression and Multilevel/Hierarchical Models
Data Analysis Using Regression and Multilevel/Hierarchical Models ANDREW GELMAN Columbia University JENNIFER HILL Columbia University CAMBRIDGE UNIVERSITY PRESS Contents List of examples V a 9 e xv " Preface
More informationBayesian Variable Selection Tutorial
Tutorial on Bayesian Variable Selection 1 An informal introductory tutorial Assistant Professor Department of Statistics Athens University of Economics and Business The following presentation is based
More informationIntroduction to Bayesian Analysis 1
Biostats VHM 801/802 Courses Fall 2005, Atlantic Veterinary College, PEI Henrik Stryhn Introduction to Bayesian Analysis 1 Little known outside the statistical science, there exist two different approaches
More informationMacroeconometric Analysis. Chapter 1. Introduction
Macroeconometric Analysis Chapter 1. Introduction Chetan Dave David N. DeJong 1 Background The seminal contribution of Kydland and Prescott (1982) marked the crest of a sea change in the way macroeconomists
More informationBayesian Variable Selection Tutorial
Tutorial on Bayesian Variable Selection 1 ISA SHORT COURSES MCMC, WinBUGS and Bayesian Model Selection 5 6 December 2011 Associate Professor Department of Statistics Athens University of Economics and
More informationStatistics for Social and Behavioral Sciences
Statistics for Social and Behavioral Sciences Advisors: S.E. Fienberg W.J. van der Linden For other titles published in this series, go to http://www.springer.com/series/3463 Jean-Paul Fox Bayesian Item
More informationResponse to Comment on Cognitive Science in the field: Does exercising core mathematical concepts improve school readiness?
Response to Comment on Cognitive Science in the field: Does exercising core mathematical concepts improve school readiness? Authors: Moira R. Dillon 1 *, Rachael Meager 2, Joshua T. Dean 3, Harini Kannan
More informationInstructors: Patrick Brandt Skyler Cranmer and Jong Hee Park
Advanced Bayesian Models for the Social Sciences ICPSR Second Session, 2010. Instructors: Patrick Brandt (pbrandt@utdallas.edu), Skyler Cranmer (skyler@unc.edu), and Jong Hee Park (jhp@uchicago.edu). TA:
More informationHistorical Developments in Bayesian Econometrics after Cowles Foundation Monographs 10, 14
TI 2013-191/III Tinbergen Institute Discussion Paper Historical Developments in Bayesian Econometrics after Cowles Foundation Monographs 10, 14 Nalan Basturk 1 Cem Cakmakli 2 S. Pinar Ceyhan 1 Herman K.
More informationCombining Risks from Several Tumors Using Markov Chain Monte Carlo
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln U.S. Environmental Protection Agency Papers U.S. Environmental Protection Agency 2009 Combining Risks from Several Tumors
More information16:35 17:20 Alexander Luedtke (Fred Hutchinson Cancer Research Center)
Conference on Causal Inference in Longitudinal Studies September 21-23, 2017 Columbia University Thursday, September 21, 2017: tutorial 14:15 15:00 Miguel Hernan (Harvard University) 15:00 15:45 Miguel
More informationMS&E 226: Small Data
MS&E 226: Small Data Lecture 10: Introduction to inference (v2) Ramesh Johari ramesh.johari@stanford.edu 1 / 17 What is inference? 2 / 17 Where did our data come from? Recall our sample is: Y, the vector
More informationBayesian Model Averaging for Propensity Score Analysis
Multivariate Behavioral Research, 49:505 517, 2014 Copyright C Taylor & Francis Group, LLC ISSN: 0027-3171 print / 1532-7906 online DOI: 10.1080/00273171.2014.928492 Bayesian Model Averaging for Propensity
More informationCitation for published version (APA): Ebbes, P. (2004). Latent instrumental variables: a new approach to solve for endogeneity s.n.
University of Groningen Latent instrumental variables Ebbes, P. IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document
More informationINTRODUCTION TO ECONOMETRICS (EC212)
INTRODUCTION TO ECONOMETRICS (EC212) Course duration: 54 hours lecture and class time (Over three weeks) LSE Teaching Department: Department of Economics Lead Faculty (session two): Dr Taisuke Otsu and
More informationP. RICHARD HAHN. Research areas. Employment. Education. Research papers
P. RICHARD HAHN Arizona State University email: prhahn@asu.edu Research areas Bayesian methods, causal inference, foundations of statistics, nonlinear regression, Monte Carlo methods, applications to social
More informationModern Regression Methods
Modern Regression Methods Second Edition THOMAS P. RYAN Acworth, Georgia WILEY A JOHN WILEY & SONS, INC. PUBLICATION Contents Preface 1. Introduction 1.1 Simple Linear Regression Model, 3 1.2 Uses of Regression
More informationBayesian and Frequentist Approaches
Bayesian and Frequentist Approaches G. Jogesh Babu Penn State University http://sites.stat.psu.edu/ babu http://astrostatistics.psu.edu All models are wrong But some are useful George E. P. Box (son-in-law
More informationBAYESIAN INFERENCE FOR HOSPITAL QUALITY IN A SELECTION MODEL. By John Geweke, Gautam Gowrisankaran, and Robert J. Town 1
Econometrica, Vol. 71, No. 4 (July, 2003), 1215 1238 BAYESIAN INFERENCE FOR HOSPITAL QUALITY IN A SELECTION MODEL By John Geweke, Gautam Gowrisankaran, and Robert J. Town 1 This paper develops new econometric
More informationBAYESIAN HYPOTHESIS TESTING WITH SPSS AMOS
Sara Garofalo Department of Psychiatry, University of Cambridge BAYESIAN HYPOTHESIS TESTING WITH SPSS AMOS Overview Bayesian VS classical (NHST or Frequentist) statistical approaches Theoretical issues
More informationCARISMA-LMS Workshop on Statistics for Risk Analysis
Department of Mathematics CARISMA-LMS Workshop on Statistics for Risk Analysis Thursday 28 th May 2015 Location: Department of Mathematics, John Crank Building, Room JNCK128 (Campus map can be found at
More informationComparing treatments evaluated in studies forming disconnected networks of evidence: A review of methods
Comparing treatments evaluated in studies forming disconnected networks of evidence: A review of methods John W Stevens Reader in Decision Science University of Sheffield EFPSI European Statistical Meeting
More informationMethodology for Bayesian Model Averaging: An Update
Methodology for Bayesian Model Averaging: An Update Jennifer A. Hoeting Colorado State University Abstract The standard practice of selecting a single model from some class of models and then making inferences
More informationExplicit Bayes: Working Concrete Examples to Introduce the Bayesian Perspective.
Explicit Bayes: Working Concrete Examples to Introduce the Bayesian Perspective. As published in Benchmarks RSS Matters, January 2015 http://web3.unt.edu/benchmarks/issues/2015/01/rss-matters Jon Starkweather,
More informationComputer Age Statistical Inference. Algorithms, Evidence, and Data Science. BRADLEY EFRON Stanford University, California
Computer Age Statistical Inference Algorithms, Evidence, and Data Science BRADLEY EFRON Stanford University, California TREVOR HASTIE Stanford University, California ggf CAMBRIDGE UNIVERSITY PRESS Preface
More informationOn the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14
TI 2014-085/III Tinbergen Institute Discussion Paper On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14 Nalan Basturk 1 Cem Cakmakli 2 S. Pinar Ceyhan 1 Herman K. van Dijk 1
More informationThe Century of Bayes
The Century of Bayes Joseph J. Retzer Ph.D., Maritz Research The Bayesian `machine together with MCMC is arguably the most powerful mechanism ever created for processing data and knowledge Berger, 2001
More informationBayesians methods in system identification: equivalences, differences, and misunderstandings
Bayesians methods in system identification: equivalences, differences, and misunderstandings Johan Schoukens and Carl Edward Rasmussen ERNSI 217 Workshop on System Identification Lyon, September 24-27,
More informationRussian Journal of Agricultural and Socio-Economic Sciences, 3(15)
ON THE COMPARISON OF BAYESIAN INFORMATION CRITERION AND DRAPER S INFORMATION CRITERION IN SELECTION OF AN ASYMMETRIC PRICE RELATIONSHIP: BOOTSTRAP SIMULATION RESULTS Henry de-graft Acquah, Senior Lecturer
More informationCommentary: Practical Advantages of Bayesian Analysis of Epidemiologic Data
American Journal of Epidemiology Copyright 2001 by The Johns Hopkins University School of Hygiene and Public Health All rights reserved Vol. 153, No. 12 Printed in U.S.A. Practical Advantages of Bayesian
More informationarxiv: v2 [stat.ap] 7 Dec 2016
A Bayesian Approach to Predicting Disengaged Youth arxiv:62.52v2 [stat.ap] 7 Dec 26 David Kohn New South Wales 26 david.kohn@sydney.edu.au Nick Glozier Brain Mind Centre New South Wales 26 Sally Cripps
More informationBayesian model averaging: A systematic review and conceptual classification
Submitted to Statistical Science arxiv: math.pr/0000000 arxiv:1509.08864v1 [stat.me] 29 Sep 2015 Bayesian model averaging: A systematic review and conceptual classification Tiago M. Fragoso and Francisco
More informationRobert McCulloch, Curriculum Vitae January, 2018
Robert McCulloch, Curriculum Vitae January, 2018 Contact Information: email: Robert.McCulloch at asu.edu web: www.rob-mcculloch.org Mail: Robert McCulloch School of Mathematical and Statistical Sciences
More informationApplications with Bayesian Approach
Applications with Bayesian Approach Feng Li feng.li@cufe.edu.cn School of Statistics and Mathematics Central University of Finance and Economics Outline 1 Missing Data in Longitudinal Studies 2 FMRI Analysis
More informationAB - Bayesian Analysis
Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2018 200 - FME - School of Mathematics and Statistics 715 - EIO - Department of Statistics and Operations Research MASTER'S DEGREE
More informationA Bayesian Approach to Characterizing Heterogeneity of Rank-Dependent Expected Utility Models of Lottery Choices
A Bayesian Approach to Characterizing Heterogeneity of Rank-Dependent Expected Utility Models of Lottery Choices by Dale O. Stahl Malcolm Forsman Centennial Professor Department of Economics University
More informationApplication of Multinomial-Dirichlet Conjugate in MCMC Estimation : A Breast Cancer Study
Int. Journal of Math. Analysis, Vol. 4, 2010, no. 41, 2043-2049 Application of Multinomial-Dirichlet Conjugate in MCMC Estimation : A Breast Cancer Study Geetha Antony Pullen Mary Matha Arts & Science
More informationMaximum Likelihood Estimation and Inference. With Examples in R, SAS and ADMB. Russell B. Millar STATISTICS IN PRACTICE
Maximum Likelihood Estimation and Inference With Examples in R, SAS and ADMB Russell B. Millar STATISTICS IN PRACTICE Maximum Likelihood Estimation and Inference Statistics in Practice Series Advisors
More informationBayesian Joint Modelling of Benefit and Risk in Drug Development
Bayesian Joint Modelling of Benefit and Risk in Drug Development EFSPI/PSDM Safety Statistics Meeting Leiden 2017 Disclosure is an employee and shareholder of GSK Data presented is based on human research
More informationDECISION ANALYSIS WITH BAYESIAN NETWORKS
RISK ASSESSMENT AND DECISION ANALYSIS WITH BAYESIAN NETWORKS NORMAN FENTON MARTIN NEIL CRC Press Taylor & Francis Croup Boca Raton London NewYork CRC Press is an imprint of the Taylor Si Francis an Croup,
More informationBayesian Estimation of a Meta-analysis model using Gibbs sampler
University of Wollongong Research Online Applied Statistics Education and Research Collaboration (ASEARC) - Conference Papers Faculty of Engineering and Information Sciences 2012 Bayesian Estimation of
More informationBayesian Tolerance Intervals for Sparse Data Margin Assessment
Bayesian Tolerance Intervals for Sparse Data Margin Assessment Benjamin Schroeder and Lauren Hund ASME V&V Symposium May 3, 2017 - Las Vegas, NV SAND2017-4590 C - (UUR) Sandia National Laboratories is
More informationMethods and Criteria for Model Selection
Methods and Criteria for Model Selection Summary Model selection is an important part of any statistical analysis, and indeed is central to the pursuit of science in general. Many authors have examined
More informationThe University of North Carolina at Chapel Hill School of Social Work
The University of North Carolina at Chapel Hill School of Social Work SOWO 918: Applied Regression Analysis and Generalized Linear Models Spring Semester, 2014 Instructor Shenyang Guo, Ph.D., Room 524j,
More informationTesting the Predictability of Consumption Growth: Evidence from China
Auburn University Department of Economics Working Paper Series Testing the Predictability of Consumption Growth: Evidence from China Liping Gao and Hyeongwoo Kim Georgia Southern University and Auburn
More informationSchool of Population and Public Health SPPH 503 Epidemiologic methods II January to April 2019
School of Population and Public Health SPPH 503 Epidemiologic methods II January to April 2019 Time: Tuesday, 1330 1630 Location: School of Population and Public Health, UBC Course description Students
More informationKelvin Chan Feb 10, 2015
Underestimation of Variance of Predicted Mean Health Utilities Derived from Multi- Attribute Utility Instruments: The Use of Multiple Imputation as a Potential Solution. Kelvin Chan Feb 10, 2015 Outline
More informationMODELING NONCOMPENSATORY CHOICES WITH A COMPENSATORY MODEL FOR A PRODUCT DESIGN SEARCH
Proceedings of the ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2015 August 2 5, 2015, Boston, Massachusetts, USA DETC2015-47632
More informationST440/550: Applied Bayesian Statistics. (10) Frequentist Properties of Bayesian Methods
(10) Frequentist Properties of Bayesian Methods Calibrated Bayes So far we have discussed Bayesian methods as being separate from the frequentist approach However, in many cases methods with frequentist
More informationEcological Statistics
A Primer of Ecological Statistics Second Edition Nicholas J. Gotelli University of Vermont Aaron M. Ellison Harvard Forest Sinauer Associates, Inc. Publishers Sunderland, Massachusetts U.S.A. Brief Contents
More informationIntroductory Statistical Inference with the Likelihood Function
Introductory Statistical Inference with the Likelihood Function Charles A. Rohde Introductory Statistical Inference with the Likelihood Function 123 Charles A. Rohde Bloomberg School of Health Johns Hopkins
More informationIndex. Springer International Publishing Switzerland 2017 T.J. Cleophas, A.H. Zwinderman, Modern Meta-Analysis, DOI /
Index A Adjusted Heterogeneity without Overdispersion, 63 Agenda-driven bias, 40 Agenda-Driven Meta-Analyses, 306 307 Alternative Methods for diagnostic meta-analyses, 133 Antihypertensive effect of potassium,
More informationSLAUGHTER PIG MARKETING MANAGEMENT: UTILIZATION OF HIGHLY BIASED HERD SPECIFIC DATA. Henrik Kure
SLAUGHTER PIG MARKETING MANAGEMENT: UTILIZATION OF HIGHLY BIASED HERD SPECIFIC DATA Henrik Kure Dina, The Royal Veterinary and Agricuural University Bülowsvej 48 DK 1870 Frederiksberg C. kure@dina.kvl.dk
More informationBiostatistics II
Biostatistics II 514-5509 Course Description: Modern multivariable statistical analysis based on the concept of generalized linear models. Includes linear, logistic, and Poisson regression, survival analysis,
More informationSW 9300 Applied Regression Analysis and Generalized Linear Models 3 Credits. Master Syllabus
SW 9300 Applied Regression Analysis and Generalized Linear Models 3 Credits Master Syllabus I. COURSE DOMAIN AND BOUNDARIES This is the second course in the research methods sequence for WSU doctoral students.
More informationBayesRandomForest: An R
BayesRandomForest: An R implementation of Bayesian Random Forest for Regression Analysis of High-dimensional Data Oyebayo Ridwan Olaniran (rid4stat@yahoo.com) Universiti Tun Hussein Onn Malaysia Mohd Asrul
More informationPractical Bayesian Design and Analysis for Drug and Device Clinical Trials
Practical Bayesian Design and Analysis for Drug and Device Clinical Trials p. 1/2 Practical Bayesian Design and Analysis for Drug and Device Clinical Trials Brian P. Hobbs Plan B Advisor: Bradley P. Carlin
More informationBayesian hierarchical modelling
Bayesian hierarchical modelling Matthew Schofield Department of Mathematics and Statistics, University of Otago Bayesian hierarchical modelling Slide 1 What is a statistical model? A statistical model:
More informationDetection of Unknown Confounders. by Bayesian Confirmatory Factor Analysis
Advanced Studies in Medical Sciences, Vol. 1, 2013, no. 3, 143-156 HIKARI Ltd, www.m-hikari.com Detection of Unknown Confounders by Bayesian Confirmatory Factor Analysis Emil Kupek Department of Public
More informationTHE INDIRECT EFFECT IN MULTIPLE MEDIATORS MODEL BY STRUCTURAL EQUATION MODELING ABSTRACT
European Journal of Business, Economics and Accountancy Vol. 4, No. 3, 016 ISSN 056-6018 THE INDIRECT EFFECT IN MULTIPLE MEDIATORS MODEL BY STRUCTURAL EQUATION MODELING Li-Ju Chen Department of Business
More informationHow do we combine two treatment arm trials with multiple arms trials in IPD metaanalysis? An Illustration with College Drinking Interventions
1/29 How do we combine two treatment arm trials with multiple arms trials in IPD metaanalysis? An Illustration with College Drinking Interventions David Huh, PhD 1, Eun-Young Mun, PhD 2, & David C. Atkins,
More informationSyllabus.
Business 41903 Applied Econometrics - Spring 2018 Instructor: Christian Hansen Office: HPC 329 Phone: 773 834 1702 E-mail: chansen1@chicagobooth.edu TA: Jianfei Cao E-mail: jcao0@chicagobooth.edu Syllabus
More informationAPPENDIX AVAILABLE ON REQUEST. HEI Panel on the Health Effects of Traffic-Related Air Pollution
APPENDIX AVAILABLE ON REQUEST Special Report 17 Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health Effects Chapter 3. Assessment of Exposure to Traffic-Related
More informationMissing data. Patrick Breheny. April 23. Introduction Missing response data Missing covariate data
Missing data Patrick Breheny April 3 Patrick Breheny BST 71: Bayesian Modeling in Biostatistics 1/39 Our final topic for the semester is missing data Missing data is very common in practice, and can occur
More informationLennart Hoogerheide 1,2,4 Joern H. Block 1,3,5 Roy Thurik 1,2,3,6,7
TI 2010-075/3 Tinbergen Institute Discussion Paper Family Background Variables as Instruments for Education in Income Regressions: A Bayesian Analysis Lennart Hoogerheide 1,2,4 Joern H. Block 1,3,5 Roy
More informationBayesian graphical models for combining multiple data sources, with applications in environmental epidemiology
Bayesian graphical models for combining multiple data sources, with applications in environmental epidemiology Sylvia Richardson 1 sylvia.richardson@imperial.co.uk Joint work with: Alexina Mason 1, Lawrence
More informationAn Empirical Assessment of Bivariate Methods for Meta-analysis of Test Accuracy
Number XX An Empirical Assessment of Bivariate Methods for Meta-analysis of Test Accuracy Prepared for: Agency for Healthcare Research and Quality U.S. Department of Health and Human Services 54 Gaither
More informationIS GOD IN THE DETAILS? A REEXAMINATION OF THE ROLE OF RELIGION IN ECONOMIC GROWTH
JOURNAL OF APPLIED ECONOMETRICS Published online 26 April 2011 in Wiley Online Library (wileyonlinelibrary.com).1245 IS GOD IN THE DETAILS? A REEXAMINATION OF THE ROLE OF RELIGION IN ECONOMIC GROWTH STEVEN
More informationTRIPODS Workshop: Models & Machine Learning for Causal I. & Decision Making
TRIPODS Workshop: Models & Machine Learning for Causal Inference & Decision Making in Medical Decision Making : and Predictive Accuracy text Stavroula Chrysanthopoulou, PhD Department of Biostatistics
More informationEMPIRICAL STRATEGIES IN LABOUR ECONOMICS
EMPIRICAL STRATEGIES IN LABOUR ECONOMICS University of Minho J. Angrist NIPE Summer School June 2009 This course covers core econometric ideas and widely used empirical modeling strategies. The main theoretical
More informationBIOSTATISTICAL METHODS AND RESEARCH DESIGNS. Xihong Lin Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
BIOSTATISTICAL METHODS AND RESEARCH DESIGNS Xihong Lin Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA Keywords: Case-control study, Cohort study, Cross-Sectional Study, Generalized
More informationScore Tests of Normality in Bivariate Probit Models
Score Tests of Normality in Bivariate Probit Models Anthony Murphy Nuffield College, Oxford OX1 1NF, UK Abstract: A relatively simple and convenient score test of normality in the bivariate probit model
More informationBayesian Prediction Tree Models
Bayesian Prediction Tree Models Statistical Prediction Tree Modelling for Clinico-Genomics Clinical gene expression data - expression signatures, profiling Tree models for predictive sub-typing Combining
More information[1] provides a philosophical introduction to the subject. Simon [21] discusses numerous topics in economics; see [2] for a broad economic survey.
Draft of an article to appear in The MIT Encyclopedia of the Cognitive Sciences (Rob Wilson and Frank Kiel, editors), Cambridge, Massachusetts: MIT Press, 1997. Copyright c 1997 Jon Doyle. All rights reserved
More informationIntroduction to Survival Analysis Procedures (Chapter)
SAS/STAT 9.3 User s Guide Introduction to Survival Analysis Procedures (Chapter) SAS Documentation This document is an individual chapter from SAS/STAT 9.3 User s Guide. The correct bibliographic citation
More informationIndividual Differences in Attention During Category Learning
Individual Differences in Attention During Category Learning Michael D. Lee (mdlee@uci.edu) Department of Cognitive Sciences, 35 Social Sciences Plaza A University of California, Irvine, CA 92697-5 USA
More informationIntroduction to Computational Neuroscience
Introduction to Computational Neuroscience Lecture 5: Data analysis II Lesson Title 1 Introduction 2 Structure and Function of the NS 3 Windows to the Brain 4 Data analysis 5 Data analysis II 6 Single
More informationYou must answer question 1.
Research Methods and Statistics Specialty Area Exam October 28, 2015 Part I: Statistics Committee: Richard Williams (Chair), Elizabeth McClintock, Sarah Mustillo You must answer question 1. 1. Suppose
More informationStatistical Analysis with Missing Data. Second Edition
Statistical Analysis with Missing Data Second Edition WILEY SERIES IN PROBABILITY AND STATISTICS Established by WALTER A. SHEWHART and SAMUEL S. WILKS Editors: David J. Balding, Peter Bloomfield, Noel
More informationLimited dependent variable regression models
181 11 Limited dependent variable regression models In the logit and probit models we discussed previously the dependent variable assumed values of 0 and 1, 0 representing the absence of an attribute and
More informationEconometric Game 2012: infants birthweight?
Econometric Game 2012: How does maternal smoking during pregnancy affect infants birthweight? Case A April 18, 2012 1 Introduction Low birthweight is associated with adverse health related and economic
More informationNEW METHODS FOR SENSITIVITY TESTS OF EXPLOSIVE DEVICES
NEW METHODS FOR SENSITIVITY TESTS OF EXPLOSIVE DEVICES Amit Teller 1, David M. Steinberg 2, Lina Teper 1, Rotem Rozenblum 2, Liran Mendel 2, and Mordechai Jaeger 2 1 RAFAEL, POB 2250, Haifa, 3102102, Israel
More informationLogistic regression: Why we often can do what we think we can do 1.
Logistic regression: Why we often can do what we think we can do 1. Augst 8 th 2015 Maarten L. Buis, University of Konstanz, Department of History and Sociology maarten.buis@uni.konstanz.de All propositions
More informationMethods Research Report. An Empirical Assessment of Bivariate Methods for Meta-Analysis of Test Accuracy
Methods Research Report An Empirical Assessment of Bivariate Methods for Meta-Analysis of Test Accuracy Methods Research Report An Empirical Assessment of Bivariate Methods for Meta-Analysis of Test Accuracy
More information1%(5:25.,1*3$3(56(5,(6 '(7(50,1$1762)/21*7(50*52:7+$%$<(6,$1 $9(5$*,1*2)&/$66,&$/(67,0$7(6%$&($3352$&+
1%(5:25.,1*3$3(56(5,(6 '(7(50,1$1762)/21*7(50*52:7+$%$
More informationOutline. What s inside this paper? My expectation. Software Defect Prediction. Traditional Method. What s inside this paper?
Outline A Critique of Software Defect Prediction Models Norman E. Fenton Dongfeng Zhu What s inside this paper? What kind of new technique was developed in this paper? Research area of this technique?
More informationVision as Bayesian inference: analysis by synthesis?
Vision as Bayesian inference: analysis by synthesis? Schwarz Andreas, Wiesner Thomas 1 / 70 Outline Introduction Motivation Problem Description Bayesian Formulation Generative Models Letters, Text Faces
More informationComments on Limits of Econometrics by David Freedman. Arnold Zellner. University of Chicago
Comments on Limits of Econometrics by David Freedman Arnold Zellner University of Chicago David Freedman s impressive paper reveals well his deep understanding of not only statistical techniques and their
More informationBayesian and Non-Bayesian Approaches to Scientific Modeling and Inference in Economics and Econometrics by Arnold Zellner* U. of Chicago.
Bayesian and Non-Bayesian Approaches to Scientific Modeling and Inference in Economics and Econometrics by Arnold Zellner* U. of Chicago Abstract After brief remarks on the history of modeling and inference
More information