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Statistical Modeling and Computation de Dirk P. Kroese

Statistical Modeling and Computation: Springer Texts in Statistics

Autor Dirk P. Kroese, Joshua Chan
en Limba Engleză Hardback – 22 oct 2024
This book, Statistical Modeling and Computation, provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications.
The 2nd edition changes the programming language used in the text from MATLAB to Julia. For all examples with computing components, the authors provide data sets and their own Julia codes. The new edition features numerous full color graphics to illustrate the concepts discussed in the text, and adds three entirely new chapters on a variety of popular topics, including:
  • Regularization and the Lasso regression
  • Bayesian shrinkage methods
  • Nonparametric statistical tests
  • Splines and the Gaussian process regression
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Specificații

ISBN-13: 9781071641316
ISBN-10: 107164131X
Pagini: 480
Ilustrații: Approx. 480 p.
Dimensiuni: 155 x 235 mm
Greutate: 0.89 kg
Ediția:Second Edition 2025
Editura: Springer Us
Colecția Springer
Seria Springer Texts in Statistics

Locul publicării:New York, NY, United States

Cuprins

​​Probability Models.- Random Variables and Probability Distributions.- Joint Distributions.- Common Statistical Models.- Statistical Inference.- Likelihood.- Monte Carlo Sampling.- Bayesian Inference.- Generalized Linear Models.- Dependent Data Models.- State Space Models.- References.- Solutions.- MATLAB Primer.- Mathematical Supplement.- Index.

Notă biografică

Joshua Chan is Professor of Economics, and holds the endowed Olson Chair at Purdue University. He is an elected fellow at the International Association for Applied Econometrics and served as Chair for the Economics, Finance and Business Section of the International Society for Bayesian Analysis from 2020-2022. His research focuses on building new high-dimensional time-series models and developing efficient estimation methods for these models. He has published over 50 papers in peer-reviewed journals, including some top-field journals such as Journal of Econometrics, Journal of the American Statistical Association and Journal of Business and Economic Statistics.
Dirk Kroese is Professor of Mathematics and Statistics at the University of Queensland. He is known for his significant contributions to the fields of applied probability, mathematical statistics, machine learning, and Monte Carlo methods. He has published over 140 articles and 7 books. He is a pioneer of the well-known Cross-Entropy (CE) method, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance.  In addition to his scholarly contributions, Dirk Kroese is recognized for his role as an educator and mentor, having supervised and inspired numerous students and researchers.

Textul de pe ultima copertă

This book, Statistical Modeling and Computation, provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications.
The 2nd edition changes the programming language used in the text from MATLAB to Julia. For all examples with computing components, the authors provide data sets and their own Julia codes. The new edition features numerous full color graphics to illustrate the concepts discussed in the text, and adds three entirely new chapters on a variety of popular topics, including:
  • Regularization and the Lasso regression
  • Bayesian shrinkage methods
  • Nonparametric statistical tests
  • Splines and the Gaussian process regression

Caracteristici

An integrated treatment of statistical inference and computation helps the reader gain a firm understanding of both theory and practice Discusses modern computation techniques including Markov chain Monte Carlo methods and the Expectation Maximization algorithm Includes computer codes and a brief programming primer in Julia for students