Sufficient Dimension Reduction: Methods and Applications with R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
暫譯: 充分維度縮減：使用 R 的方法與應用（Chapman & Hall/CRC 統計與應用機率專著）

Bing Li

Sufficient Dimension Reduction: Methods and Applications with R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

買這商品的人也買了...

商品描述

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of variables. Sufficient Dimension Reduction: Methods and Applications with R introduces the basic theories and the main methodologies, provides practical and easy-to-use algorithms and computer codes to implement these methodologies, and surveys the recent advances at the frontiers of this field.

 

Features

 

 

 

 

 

 

 

 

 

 

  • Provides comprehensive coverage of this emerging research field.
  • Synthesizes a wide variety of dimension reduction methods under a few unifying principles such as projection in Hilbert spaces, kernel mapping, and von Mises expansion.
  • Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as sufficient dimension reduction for functional data.
  • Includes a set of computer codes written in R that are easily implemented by the readers.
  • Uses real data sets available online to illustrate the usage and power of the described methods.

 

Sufficient dimension reduction has undergone momentous development in recent years, partly due to the increased demands for techniques to process high-dimensional data, a hallmark of our age of Big Data. This book will serve as the perfect entry into the field for the beginning researchers or a handy reference for the advanced ones.

 

The author

 

Bing Li obtained his Ph.D. from the University of Chicago. He is currently a Professor of Statistics at the Pennsylvania State University. His research interests cover sufficient dimension reduction, statistical graphical models, functional data analysis, machine learning, estimating equations and quasilikelihood, and robust statistics. He is a fellow of the Institute of Mathematical Statistics and the American Statistical Association. He is an Associate Editor for The Annals of Statistics and the Journal of the American Statistical Association.

 

商品描述(中文翻譯)

充分維度縮減是一個快速發展的研究領域，廣泛應用於回歸診斷、數據可視化、機器學習、基因組學、影像處理、模式識別和醫學等領域，因為這些領域產生的大數據集通常具有大量變數。《充分維度縮減：使用 R 的方法與應用》介紹了基本理論和主要方法論，提供實用且易於使用的算法和計算機代碼來實現這些方法，並調查了該領域前沿的最新進展。

特點

- 提供對這一新興研究領域的全面覆蓋。
- 在幾個統一原則下綜合了各種維度縮減方法，例如希爾伯特空間中的投影、核映射和馮·米塞斯展開。
- 反映了最新的進展，如非線性充分維度縮減、張量數據的維度折疊，以及功能數據的充分維度縮減。
- 包含一組用 R 編寫的計算機代碼，讀者可以輕鬆實現。
- 使用在線可用的真實數據集來說明所描述方法的使用和效能。

充分維度縮減在近年來經歷了重大的發展，部分原因是對處理高維數據技術的需求增加，這是我們大數據時代的一個特徵。本書將成為初學研究者進入該領域的完美入門書籍，或是進階研究者的便捷參考。

作者

李冰（Bing Li）獲得芝加哥大學的博士學位。目前是賓夕法尼亞州立大學的統計學教授。他的研究興趣涵蓋充分維度縮減、統計圖形模型、功能數據分析、機器學習、估計方程和準似然、以及穩健統計。他是數學統計學會和美國統計協會的會士，也是《統計年鑑》（The Annals of Statistics）和《美國統計協會期刊》（Journal of the American Statistical Association）的副編輯。

類似商品