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Statistical modeling and computation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical modeling and computation/ by Joshua C. C. Chan, Dirk P. Kroese.
作者:	Chan, Joshua.
其他作者:	Kroese, Dirk P.
出版者:	New York, NY :Springer US : : 2025.,
面頁冊數:	xxi, 492 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Mathematical statistics. -
電子資源:	https://doi.org/10.1007/978-1-0716-4132-3
ISBN:	9781071641323

Statistical modeling and computation
Chan, Joshua.

Statistical modeling and computation[electronic resource] /by Joshua C. C. Chan, Dirk P. Kroese. - Second edition. - New York, NY :Springer US :2025. - xxi, 492 p. :ill., digital ;24 cm. - Springer texts in statistics,2197-4136. - Springer texts in statistics..

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.

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 Joshua C. C. 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.

ISBN: 9781071641323

Standard No.: 10.1007/978-1-0716-4132-3doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Statistical modeling and computation
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520 $a 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 Joshua C. C. 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.
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