Statistical Learning for Big Dependent Data
analysis of big dependent data
$221.59
- Hardcover
560 pages
- Release Date
28 March 2021
Summary
Mastering Statistical Learning for Big, Dependent Data
Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource
Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large se…
Book Details
| ISBN-13: | 9781119417385 |
|---|---|
| ISBN-10: | 1119417384 |
| Series: | Wiley Series in Probability and Statistics |
| Author: | Daniel Peña, Ruey S. Tsay |
| Publisher: | John Wiley & Sons Inc |
| Imprint: | John Wiley & Sons Inc |
| Format: | Hardcover |
| Number of Pages: | 560 |
| Release Date: | 28 March 2021 |
| Weight: | 1.29kg |
| Dimensions: | 259mm x 185mm x 31mm |
You Can Find This Book In
About The Author
Daniel Peña
Daniel Peña, PhD, is Professor of Statistics at Universidad Carlos III de Madrid, Spain. He received his PhD from Universidad Politecnica de Madrid in 1976 and has taught at the Universities of Wisconsin-Madison, Chicago and Carlos III de Madrid, where he was Rector from 2007 to 2015.
Ruey S. Tsay, PhD, is the H.G.B Alexander Professor of Econometrics & Statistics at the Booth School of Business, University of Chicago, United States. He received his PhD in 1982 from the University of Wisconsin-Madison. His research focuses on areas of business and economic forecasting, financial econometrics, risk management, and analysis of big dependent data.
Returns
This item is eligible for free returns within 30 days of delivery. See our returns policy for further details.