2014 Mitchel Prize and Savage Awards
The Prize Committee of ISBA is pleased to announce the call for submissions for the 2014 Mitchell Prize and Savage Awards.
The winner(s) will be announced at the Joint Statistical meetings in Seattle, 2015. The deadline for submissions is 31 May, 2014 (midnight UTC/GMT, 7pm EST, 4pm PST).
The Mitchell Prize is given in recognition of an outstanding paper that describes how a Bayesian analysis has solved an important applied problem. The prize includes a check for $1,000.00 and a plaque. Details on the Mitchell Prize, including names of past winners, eligibility details, and the on-line application procedure, can be found at http://www.bayesian.org/awards/MitchellPrize.html .
The Savage Award, named in honor of Leonard J. "Jimmie" Savage, is bestowed each year on two outstanding doctoral dissertations in Bayesian Econometrics and Statistics, one each in “Theory & Methods” and “Applied Methodology”. Doctoral dissertations submitted for the Savage Prize must be written in English. Up to two awards of $750.00 will be awarded. Finalists will be notified in mid-December and invited to present their dissertation research at a special contributed session at the 2015 JSM in Seattle, with the winners announced at the meeting. For details on the Savage Award, including names of past winners, eligibility details, and the on-line application procedure, please visit http://www.bayesian.org/awards/Savage.html.
Nominations for the Mitchell and Savage Award may be made by any ISBA or SBSS member.
For questions regarding any of the Prizes or Awards may be sent to the ISBA Prize Committee at firstname.lastname@example.org.
Continuing Education Courses at JSM 2014
The Section on Bayesian Statistical Science (SBSS) is pleased to sponsor the following Continuing Education Courses in the Joint Statistical Meetings to be held in Boston, MA, from August 02-07, 2014. Please note that the room numbers indicated below are subject to change. Participants are always encouraged to confirm these locations upon arrival at the conference front desk.
1. Sat, August, 02, 2014, 8:30 AM - 5:00 PM. Room CC-161.
Hierarchical Bayesian Modeling and Analysis for Spatial Data.
Instructor(s): Sudipto Banerjee, University of Minnesota, Bradley P. Carlin, University of Minnesota, Alan Gelfand, Duke University.
Abstract: In this course we will describe hierarchical modeling and related Markov chain Monte Carlo (MCMC) methods for spatial statistics. We will begin by outlining and providing illustrative examples of the three types of spatial data: point-level (geostatistical), areal (lattice), and spatial point patterns. We then describe both exploratory data analysis tools and traditional modeling approaches for point-referenced data. Since our approach is fully model-based through the use of Gaussian processes, we develop the basics of spatial Gaussian process models. Approaches from traditional geostatistics (variogram fitting, kriging, etc.) will be briefly covered here. We then turn to areal data models, again starting with choropleth maps and other displays and progressing towards more formal model specifications, e.g., Markov random fields that underlie the conditional, intrinsic, and simultaneous autoregressive (CAR, IAR, and SAR) models widely used in areal data settings. The remainder of our presentation will cover hierarchical modeling for both univariate and multivariate spatial response data, including Bayesian kriging and lattice modeling, as well as more advanced issues such as anisotropy and nonstationarity. We also include a discussion of spatial point process models, and modern computational approaches for very large data sets (the so-called ``big N problem"). Short course participants should have an M.S. understanding of mathematical statistics at, say, the Hogg/Craig/Tanis or Casella/Berger levels, as well as basic familiarity with Bayesian modeling and computing at the Carlin/Louis or Gelman et al. levels. We will not assume any significant previous exposure to spatial or spatiotemporal methods.
2. Sun, August, 03, 2014, 8:30 AM - 5:00 PM. Room CC-161.
Bayesian Dynamic Models: Time Series Analysis & Forecasting.
Instructor(s): Raquel Prado, University of California at Santa Cruz, Mike West, Duke University
Abstract: This short-course covers basic principles and methods of Bayesian dynamic modeling in time series analysis and forecasting, with methodological details of central model classes explored in a range of examples. A main focus is on dynamic linear models and related methods of inference and forecasting, including multivariate time series analysis. Links between time and frequency domain, and stationary time series models, will be covered, as well as selected developments in nonlinear and non-Gaussian dynamic models and associated Monte Carlo Markov chain simulation methods for analysis. The course will conclude by contacting some some recent modeling and applied developments in multivariate time series and forecasting. The course draws on a range of examples and case studies from business, finance, signal processing and the biomedical sciences. The course material will be accessible to advanced students, academics and/or professionals with strong statistical modelling backgrounds and prior exposure to essentials of Bayesian analysis. Familiarity with- and working facility in- multivariate distribution theory and statistical inference are prerequisites. Prior exposure to some areas of time series analysis will be useful though is not necessary.
3. Mon, August, 04, 2014, 8:30 AM - 5:00 PM. Room CC-160B.
Nonparametric Bayesian Data Analysis.
Instructor(s): Peter Mueller, University of Texas at Austin, Fernando Quintana, Pontificia Univ Catolica de Chile
Abstract: In this short course we will discuss the use of nonparametric Bayesian inference (BNP) for a number of common statistical inference problems, including density estimation, regression, mixed effects models, classification and clustering. In the course of this discussion we review some of the popular BNP models, including Dirichlet process (DP) models, Polya tree models, DP mixtures and dependent DP (DDP) models. We will review some of the general modeling principles, including species sampling models, stick breaking priors, product partition models for random partition and normalized random measures with indpendent increments. We will briefly discuss some of the main computational algorithms and available software. Prerequisites: working knowledge of Bayesian data anlysis (at the level of a first graduate class in Bayesian inference), and basic knowledge of Markov chain Monte Carlo posterior simulation.
4. Tue, August, 05, 2014, 8:30 AM - 5:00 PM. Room CC-161.
Instructor(s): Bruno Sanso, University of California Santa Cruz.
Abstract: Bayesian methods have become increasingly popular with the advent of fast computational algorithms for the exploration of high dimensional probability distributions. The Bayesian paradigm provides a coherent framework to build models of high complexity, incorporate quantitative and structural prior information and account for all uncertainties in a probabilistic way. This course reviews the bases of Bayesian inference. As such, it focuses on key general concepts rather than the technical detail of specific methods. The course will start by presenting the basic elements of statistical inference that uses likelihood functions. We will then consider the problem of specifying prior distributions, proceed by describing the tools for both pointwise and interval estimation and prediction and present the Bayesian theory of hypothesis testing and model comparison. Finally we will review the elements of modern computational methods used in the applications of Bayesian models. The course targets students or professionals with a good knowledge of statistics that want to learn or refresh their knowledge of basic Bayesian inference. The level of mathematical sophistication will be kept as low as possible. Calculus and basic probability theory are considered a pre-requisite.
Upcoming Web-Based Lectures
Title: Survival Analysis: Overview of Nonparametric, Parametric, and Semiparametric Approaches
Presenter: Joseph Gardinar (Michigan State University)
Date and Time: Wednesday, June 25, 2014, 12:00 p.m. - 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section
Registration Deadline: Monday, June 23, at 12:00 p.m. Eastern time
*** More information and registration at the webinar page.
Time to event and duration outcomes arise in several fields including
biostatistics, demography, economics, engineering and sociology. The
terms duration analysis, event-history analysis, failure-time analysis,
reliability analysis, and transition analysis refer essentially to the
same group of techniques although the emphases in certain modeling
aspects could differ across disciplines. In insurance and finance a
similar group of techniques apply to the severity of a random event.
Simply stated, the outcome of interest is a positive random variable,
whose distribution is skewed and the observed outcome might be left or
right or interval censored, and or left or right truncated. SASÒ
Software offers a suite of procedures for analyses of time to event and
severity data. They include LIFETEST, LIFEREG, PHREG, RELIABILITY,
SEVERITY, QUANTLIFE and QLIM which have different capabilities and
address different needs.
In this webinar we focus on techniques widely used in biostatistics.
Methods include Kaplan-Meier estimation, accelerated life-testing
models, and the ubiquitous Cox model. Recent developments in SAS extend
their reach to include analyses of multiple failure times, recurrent
events, frailty models, Markov models and use of Bayesian methods. We
present an overview of some of these methods with examples illustrating
their application in the appropriate context.