Statistical Modeling and Computation
Autor Joshua C. C. Chan, Dirk P. Kroeseen Limba Engleză Hardback – 22 ian 2025
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
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 737.44 lei 6-8 săpt. | |
| Springer – 23 aug 2016 | 737.44 lei 6-8 săpt. | |
| Hardback (1) | 747.90 lei 3-5 săpt. | |
| Springer – 22 ian 2025 | 747.90 lei 3-5 săpt. |
Preț: 747.90 lei
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Specificații
ISBN-13: 9781071641316
ISBN-10: 107164131X
Pagini: 516
Ilustrații: Approx. 480 p.
Dimensiuni: 160 x 241 x 34 mm
Greutate: 0.93 kg
Ediția:Second Edition 2025
Editura: Springer
Locul publicării:New York, NY, United States
ISBN-10: 107164131X
Pagini: 516
Ilustrații: Approx. 480 p.
Dimensiuni: 160 x 241 x 34 mm
Greutate: 0.93 kg
Ediția:Second Edition 2025
Editura: Springer
Locul publicării:New York, NY, United States
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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.
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:
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
Recenzii
“Fundamentals of probability and modeling are presented in a rigorous language and the transition to more advanced chapters is almost smooth. Explanations are precise, both verbally and mathematically. Throughout the book, cross-references are made so that the reader can find further or related topics in other parts of the book. Readers interested in mathematical rigor will find this book rewarding. …
Another strength of the book lies in the wealth and variety of exercises at the end of each chapter. The exercises (some with complete solutions) range from mathematical proofs and model building to programming. Solutions for select problems are presented at the end of the book. …
[This] book is outstanding in terms of coverage of topics, rigorous language and integration of computation.” (Abdolvahab Khademi, Journal of Statistical Software, August 2015)
Another strength of the book lies in the wealth and variety of exercises at the end of each chapter. The exercises (some with complete solutions) range from mathematical proofs and model building to programming. Solutions for select problems are presented at the end of the book. …
[This] book is outstanding in terms of coverage of topics, rigorous language and integration of computation.” (Abdolvahab Khademi, Journal of Statistical Software, August 2015)