Hang Qian

Bayesian Econometric Modelling for Big Data

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 488
• Erscheinungstermin: 20. Juni 2025
• Englisch
• Abmessung: 254mm x 178mm
• ISBN-13: 9781032915258
• ISBN-10: 1032915250
• Artikelnr.: 72487600

Autorenporträt

Hang Qian is the principal developer of the Econometrics Toolbox for MATLAB and has been dedicated to statistical software development at MathWorks since 2012. As a researcher specializing in Bayesian statistics and computational finance, he focuses on Bayesian big data analysis. His work has been published in journals such as Bayesian Analysis, Journal of Business & Economic Statistics, and Journal of Econometrics.

Inhaltsangabe

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