Bayesian Econometric Modelling for Big Data

This book delves into scalable Bayesian statistical methods designed to tackle the challenges posed by big data. It explores a variety of divide-and-conquer and subsampling techniques. The book is an essential resource for graduate students, early-career statisticians, data analysts, and statistical software users and developers.

Autorentext

Hang Qian is the principal engineer of the Econometrics Toolbox for MATLAB and has been dedicated to statistical software development at MathWorks since 2012. He earned his PhD in economics, specializing in Bayesian statistics, big data analysis, and computational finance. His research has been published in journals such as Bayesian Analysis, Journal of Business & Economic Statistics, and Journal of Econometrics.

Inhalt

Preface 1. Linear Regressions 2. Markov Chain Monte Carlo Methods 3. Shrinkage and Variable Selection 4. Correlation, Heteroscedasticity and Non-Gaussian Regressions 5. Limited Dependent Variable Models 6. Linear State Space Models 7. Nonlinear State Space Models 8. Applications of State Space Models Bibliography Index