计量经济分析 = Econometric analysis

https://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(13)70145-1.pdf

Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis.

Foreword Stephen P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng Introduction to MCMC, Charles J. Geyer A short history of Markov chain Monte Carlo: Subjective recollections from in-complete data, Christian Robert and George Casella Reversible jump Markov chain Monte Carlo, Yanan Fan and Scott A. Sisson Optimal proposal distributions and adaptive MCMC, Jeffrey S. Rosenthal MCMC using Hamiltonian dynamics, Radford M. Neal Inference and Monitoring Convergence, Andrew Gelman and Kenneth Shirley Implementing MCMC: Estimating with confidence, James M. Flegal and Galin L. Jones Perfection within reach: Exact MCMC sampling, Radu V. Craiu and Xiao-Li Meng Spatial point processes, Mark Huber The data augmentation algorithm: Theory and methodology, James P. Hobert Importance sampling, simulated tempering and umbrella sampling, Charles J.Geyer Likelihood-free Markov chain Monte Carlo, Scott A. Sisson and Yanan Fan MCMC in the analysis of genetic data on related individuals, Elizabeth Thompson A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data, Brian Caffo, DuBois Bowman, Lynn Eberly, and Susan Spear Bassett Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings in high-energy astrophysics, David van Dyk and Taeyoung Park Posterior exploration for computationally intensive forward models, Dave Higdon, C. Shane Reese, J. David Moulton, Jasper A. Vrugt and Colin Fox Statistical ecology, Ruth King Gaussian random field models for spatial data, Murali Haran Modeling preference changes via a hidden Markov item response theory model, Jong Hee Park Parallel Bayesian MCMC imputation for multiple distributed lag models: A case study in environmental epidemiology, Brian Caffo, Roger Peng, Francesca Dominici, Thomas A. Louis, and Scott Zeger MCMC for state space models, Paul Fearnhead MCMC in educational research, Roy Levy, Robert J. Mislevy, and John T. Behrens Applications of MCMC in fisheries science, Russell B. Millar Model comparison and simulation for hierarchical models: analyzing rural-urban migration in Thailand, Filiz Garip and Bruce Western

Handbook of Markov Chain Monte Carlo

BUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instrumental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20-year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives. Copyright © 2009 John Wiley & Sons, Ltd.

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The BUGS project: Evolution, critique and future directions

Monetary policy and the private sector behavior of the US economy are modeled as a time varying structural vector autoregression, where the sources of time variation are both the coefficients and the variance covariance matrix of the innovations. The paper develops a new, simple modeling strategy for the law of motion of the variance covariance matrix and proposes an efficient Markov chain Monte Carlo algorithm for the model likelihood/posterior numerical evaluation. The main empirical conclusions are: 1) both systematic and non-systematic monetary policy have changed during the last forty years. In particular, systematic responses of the interest rate to inflation and unemployment exhibit a trend toward a more aggressive behavior, despite remarkable oscillations; 2) this has had a negligible effect on the rest of the economy. The role played by exogenous non-policy shocks seems more important than interest rate policy in explaining the high inflation and unemployment episodes in recent US economic history.

/pdf/time-varying-structural-vector-autoregressions-and-monetary-3ch9uubw9g.pdf

Time Varying Structural Vector Autoregressions and Monetary Policy

Hardcover: 520 pages Publisher: John Wiley, first edition (February 2009) Language: English ISBN-10: 047014114X Yes, yet another Bayesian textbook. Ioannis Ntzoufras' Bayesian Modeling Using WinBUG...

/pdf/bayesian-modeling-using-winbugs-4llywzxlzm.pdf

Bayesian Modeling Using WinBUGS

Several MCMC methods have been proposed for estimating probabilities of models and associated ‘model-averaged’ posterior distributions in the presence of model uncertainty. We discuss, compare, develop and illustrate several of these methods, focussing on connections between them.

https://stat.duke.edu/~scs/Courses/Stat376/Papers/TransdimMCMC/Dellaportas2002.pdf

On Bayesian model and variable selection using MCMC

Summary. Models based on the bivariate Poisson distribution are used for modelling sports data. Independent Poisson distributions are usually adopted to model the number of goals of two competing teams. We replace the independence assumption by considering a bivariate Poisson model and its extensions. The models proposed allow for correlation between the two scores, which is a plausible assumption in sports with two opposing teams competing against each other. The effect of introducing even slight correlation is discussed. Using just a bivariate Poisson distribution can improve model fit and prediction of the number of draws in football games. The model is extended by considering an inflation factor for diagonal terms in the bivariate joint distribution. This inflation improves in precision the estimation of draws and, at the same time, allows for overdispersed, relative to the simple Poisson distribution, marginal distributions. The properties of the models proposed as well as interpretation and estimation procedures are provided. An illustration of the models is presented by using data sets from football and water-polo.

https://www.jstor.org/stable/pdfplus/4128211.pdf

Analysis of sports data by using bivariate Poisson models

The aim of this study within the Athens Study of Psychosis Proneness and Incidence of Schizophrenia (ASPIS) was the examination of the latent structure of schizotypal dimensions among a large population of young male conscripts in the Greek Air Force during their first week of military training. Confirmatory factor analysis (CFA) was conducted on 1,355 reliable responders to the self-rated Schizotypal Personality Questionnaire (SPQ), which covers all nine aspects of DSM-III-R schizotypal personality disorder (SPD). A four-factor model (cognitive/perceptual, paranoid, negative, and disorganization schizotypal dimensions) provided a better fit to the data than did other competing models (one-, two-, three-, four, and five-factor models). This result is in agreement with recent findings supporting the notion of a multidimensional construct of the schizotypy and related schizophrenia phenotype.

/pdf/factorial-composition-of-self-rated-schizotypal-traits-among-3ww8w9t1sb.pdf

Factorial Composition of Self-Rated Schizotypal Traits Among Young Males Undergoing Military Training

Modelling football match outcomes is becoming increasingly popular nowadays for both team managers and betting funs. Most of the existing literature deals with modelling the number of goals scored by each team. In this paper, we work in a different direction. Instead of modelling the number of goals directly, we focus on the difference of the number of goals, i.e. the margin of victory. Modelling the differences instead of the scores themselves has some major advantages. Firstly, we eliminate correlation imposed by the fact that the two opponent teams compete each other, and secondly, we do not assume that the scored goals by each team are marginally Poisson distributed. Application of the Bayesian methodology for the Skellam’s distribution using covariates is discussed. Illustrations using real data from the English Premiership for the season 2006–2007 are provided. The advantages of the proposed approach are also discussed.

Ioannis Ntzoufras

Papers

Bayesian Modeling Using WinBUGS

On Bayesian model and variable selection using MCMC

Analysis of sports data by using bivariate Poisson models

Factorial Composition of Self-Rated Schizotypal Traits Among Young Males Undergoing Military Training

Bayesian modelling of football outcomes: using the Skellam's distribution for the goal difference