The Contribution of Young Researchers to Bayesian Statistics: Proceedings of Baysm2013

ISBN-13: 9783319020839 / Angielski / Twarda / 2013 / 214 str.

Ettore Lanzarone; Francesca Ieva

Opis

The first Bayesian Young Statisticians Meeting, BAYSM 2013, has provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and post-docs dealing with Bayesian statistics to connect with the Bayesian community at large, exchange ideas, and network with scholars working in their field. The Workshop, which took place June 5th and 6th 2013 at CNR-IMATI, Milan, has promoted further research in all the fields where Bayesian statistics may be employed under the guidance of renowned plenary lecturers and senior discussants. A selection of the contributions to the meeting and the summary of one of the plenary lectures compose this volume.

Spis treści

Part 1 Theoretical Bayes.- Chapter 1 A nonparametric model for stationary time series.- Chapter 2 Estimation of optimally combine-biomarker accuracy in the absence of a gold standard reference test.- Chapter 3 On Bayesian transformation selection: problem formulation and preliminary results.- Chapter 4 A simple proof for the multinomial version of representation theorem.- Chapter 5 A sequential Monte Carlo framework for adaptive Bayesian model discrimination designs using mutual information.- Chapter 6 Joint parameter estimation and biomass tracking in a stochastic predator-prey system.- Chapter 7 Adaptive Bayes test for monotonicity.- Chapter 8 Bayesian inference on individual-based models by controlling the random inputs.- Chapter 9 Consistency of Bayesian nonparametric hidden Markov models.- Chapter 10 Bayesian methodology in the stochastic event reconstruction problems.- Part 2 Computational Bayes.- Chapter 11 Efficient fitting of Bayesian regression models with spatio-temporally varying coefficients.- Chapter 12 Scalable automation of Monte Carlo methods.- Chapter 13 Approximate Bayesian computation for the elimination of nuisance parameters.- Chapter 14 Reweighting schemes based on particle methods.- Chapter 15 Bayesian inference of money flows due to international travelers on planned and unplanned domains.- Chapter 16 Parallel slice sampling.- Chapter 17 Approximate Bayesian computation (ABC) in quantile regression.- Part 3 Bayes @ work: appraisal of applications to the real world.- Chapter 18 Spatio-temporal model for short-term predictions of air pollution data.- Chapter 19 Predicting rainfall fields from lightnings records: a hierarchical Bayesian approach.- Chapter 20 An approach to environmental problem based on PFLOTRAN package.- Chapter 21 Bayesian hierarchical modeling of growth via Gompertz model: an application in poultry.- Chapter 22 Bayesian prediction of SMART power semiconductor lifetime with Bayesian networks.- Chapter 23 Consumer-oriented new-product development in fruit flavour breeding a Bayesian approach.- Chapter 24 Bayesian layer-counting in ice-cores reconstructing the time scale.- Part 4 A Bayesian approach to bio-statistics and health sciences.- Chapter 25 Bayesian analysis and prediction of patients' demands for visits in home care.- Chapter 26 Exploiting adaptive Bayesian regression shrinkage to identify exome sequence variants associated with gene expression.- Chapter 27 Randomized phase II trials: a Bayesian two-stage design.- Chapter 28 Bayesian matrix factorization for outlier detection: an application in population genetics.- Chapter 29 Noise model selection for multichannel diffusion-weighted MRI.- Chapter 30 Analysis of hospitalizations of patients affected by chronic heart disease.- Chapter 31 A semiparametric Bayesian multivariate model for survival probabilities after acute myocardial infarction.- Chapter 32 Particle learning approach to Bayesian model selection: an application from neurology.- Part 5 Bayesian models for stochastic and economic processes.- Chapter 33 Locally adaptive Bayesian covariance regression.- Chapter 34 Locally adaptive Bayesian covariance regression.- Chapter 35 Efficient Bayesian inference for multivariate factor stochastic volatility models.- Chapter 36 Poisson driven stationary Markovian models.- Chapter 37 Claim sizes in the compound Poisson process from a Bayesian viewpoint.- Chapter 38 Land rental market and agricultural production efficiency: a Bayesian perspective.- Part 6 Suggestions for young readers.- Chapter 39 The point is to publish?.

O autorze

Ettore Lanzarone is a Permanent Researcher at the division of Milan of the Institute of Applied Mathematics and Information Technology (IMATI) of the National Research Council of Italy (CNR), Milan, Italy. He is also Adjunct Professor Mathematical Analysis at the Politecnico di Milano, Milan, Italy. He obtained his Ph.D. in Bioengineering in June 2008 at the Politecnico di Milano, and his master degree in Biomedical Engineering cum laude in April 2004 at the Politecnico di Milano. He is member of the European Working Group on Operational Research Applied to Health Services (ORAHS) and of the Italian National Bioengineering Group (GNB).  His current research interests include: parameter estimation and stochastic evolution of dynamic systems described by ordinary and partial differential equations; stochastic models for estimating the demand and planning the activities in healthcare structures; modelling and in-vitro studies of the cardiovascular fluid dynamics.

 

Francesca Ieva is a Postdoctoral Research Fellow at the Modeling and Scientific Computing Lab (MOX), Department of Mathematics, Politecnico di Milano, Milan, Italy. She obtained her Ph.D. in Mathematical Models and Methods for Engineering and her master degree in Mathematical Engineering at the Politecnico di Milano in 2012 and 2008, respectively. She is member of ISBA (and Program Chair of the junior section), RSS, SIS (and Chair of the young section) and SIAM. Her research activities include Statistical Learning in Biomedical context, focused on modelling data arising from integration of clinical surveys and administrative databanks, Clinical Biostatistics, Healthcare assessment, Mixed Effects Models and Semi-parametric Bayesian hierarchical models, Depth Measures and Multivariate Functional Data Analysis for applications to ECG signals, Multi State Models for the analysis of chronic diseases progression like heart failures.