Bayesian Statistics from Methods to Models and Applications (inbunden)
Inbunden (Hardback)
Antal sidor
2015 ed.
Springer International Publishing AG
Frühwirth-Schnatter, Sylvia (ed.), Bitto, Angela (ed.), Kastner, Gregor (ed.), Posekany, Alexandra (ed.)
10 schwarz-weiße Tabellen 20 schwarz-weiße und 35 farbige Abbildungen Bibliographie
10 Tables, black and white; 25 Illustrations, color; 15 Illustrations, black and white; XIII, 167 p.
234 x 156 x 13 mm
436 g
Antal komponenter
1 Hardback
Bayesian Statistics from Methods to Models and Applications (inbunden)

Bayesian Statistics from Methods to Models and Applications

Research from BAYSM 2014

Inbunden,  Engelska, 2015-06-02
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The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to the 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Universite Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session with 30 contributions. Selected contributions have been drawn from the conference for this book. All contributions in this volume are peer-reviewed and share original research in Bayesian computation, application, and theory.
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Övrig information

Sylvia Fruhwirth-Schnatter is a Professor of Applied Statistics and Econometrics at the Department of Finance, Accounting, and Statistics at the WU Vienna University of Economics and Business, Austria. She received her PhD in Mathematics from the Vienna University of Technology in 1988. She has published in many leading journals in applied statistics and econometrics on topics such as Bayesian inference, finite mixture models, Markov switching models, state space models, and their application in economics, finance, and business. In 2014, she became elected member of the Austrian Academy of Science. Angela Bitto holds a Masters in Mathematics and is currently working on her PhD in Statistics at the Vienna University of Technology. Her research focuses on the Bayesian estimation of sparse time-varying parameter models. Prior to joining the Institute of Statistics and Mathematics at the WU Vienna University of Economics and Business, she worked as a research analyst for the European Central Bank. Gregor Kastner is an Assistant Professor at the WU Vienna University of Economics and Business and a Lecturer at the University of Applied Sciences in Wiener Neustadt, Austria. He holds Masters in Mathematics, Computer Science, Informatics Management, and Physical Education; in 2014 he received his PhD in Mathematics. Gregor researches the Bayesian modeling of economic time series, in particular the efficient estimation of univariate and high-dimensional stochastic volatility models. His work has been published in leading journals in computational statistics and computer software. Alexandra Posekany is an Assistant Professor at the Institute of Statistics and Mathematics, WU Vienna University of Economics and Business, Austria. She holds a PhD in Mathematics from the Vienna University of Technology. Her research includes applications of Bayesian analysis in computational biology and econometrics, as well as the development of algorithms and statistical methods in Bayesian computing and big data analysis.


On Bayesian based adaptive confidence sets for linear functionals.- A new finite approximation for the NGG mixture model: an application to density estimation.- Distributed Estimation of Mixture Models.- Bayesian Survival Model based on Moment Characterization.- Identifying the Infectious Period Distribution for Stochastic Epidemic Models Using the Posterior Predictive Check.- A subordinated stochastic process model.- Jeffreys priors for mixture estimation.- Bayesian Variable Selection for Generalized Linear Models Using the Power-Conditional-Expected-Posterior Prior.- A new strategy for testing cosmology with simulations.- Mixture Model for Filtering Firms' Profit Rates.- Bayesian Estimation of the Aortic Stiffness based on Non-Invasive Computed Tomography Images.- Formal and Heuristic Model Averaging Methods for Predicting the US Unemployment Rate.- Bayesian Filtering for Thermal Conductivity Estimation given Temperature Observations.- Application of Interweaving in DLMs to an Exchange and Specialization Experiment.