|
Product Description
This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.
Customers Who Bought This Item Also Bought
- High-Dimensional Statistics: A Non-Asymptotic Viewpoint (Cambridge Series in Statistical and Probabilistic Mathematics)
- High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge Series in Statistical and Probabilistic Mathematics)
- All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
- Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
- An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
- The Book of Why: The New Science of Cause and Effect
- Statistical Inference
- Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
- Causal Inference in Statistics - A Primer
*If this is not the "All of Nonparametric Statistics (Springer Texts in Statistics)" product you were looking for, you can check the other results by clicking this link. Details were last updated on Nov 24, 2024 04:18 +08.