
An Introduction to Statistical Learning by Gareth James
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years.-
A Modern Approach to Regression with R
-
All of Statistics
-
Time Series Analysis and Its Applications
-
A Modern Introduction to Probability and Statistics
-
Modern Mathematical Statistics with Applications
-
Introduction to Time Series and Forecasting
-
Statistics and Data Analysis for Financial Engineering
-
Probability with Applications in Engineering, Science, and Technology
-
Bayesian Essentials with R
-
A First Course in Bayesian Statistical Methods
-
Monte Carlo Statistical Methods
-
The Bayesian Choice
-
Plane Answers to Complex Questions
-
Fundamentals of High-Dimensional Statistics
-
Log-Linear Models and Logistic Regression
-
Basics of Modern Mathematical Statistics
-
Statistical Learning from a Regression Perspective
-
Pathologie des Thymus
-
Applied Regression Analysis
-
Lectures on Advanced Topics in Categorical Data Analysis
-
Counting for Something
-
Regression Analysis
-
Statistical Methods: The Geometric Approach
-
Applied Multivariate Data Analysis
-
Probability
-
Introduction to Statistics
-
Studying Human Populations
-
Introduction to Statistical Inference
-
Elements of Statistics for the Life and Social Sciences
-
Analysis of Variance in Experimental Design
-
Prescriptions for Working Statisticians
Gareth James is the John H. Harland Dean of Goizueta Business School at Emory University. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.
Daniela Witten is a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book with that title. Hastie co-developed much of the statistical modeling software and environment in R, and invented principal curves and surfaces. Tibshirani invented the lasso and is co-author of the very successful book, An Introduction to the Bootstrap. They are both elected members of the US National Academy of Sciences.
Jonathan Taylor is a professor of statistics at Stanford University. His research focuses on selective inference and signal detection in structured noise.
| SKU | Unavailable |
| ISBN 13 | 9783031387463 |
| Title | An Introduction to Statistical Learning |
| Author | Gareth James |
| Series | Springer Texts In Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer International Publishing AG |
| Year published | 2023-07-01 |
| Number of pages | 60 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |






























