您尚未登录,请登录后浏览更多内容! 登录 | 注册

QQ登录

只需一步,快速开始

 找回密码
 注册

QQ登录

只需一步,快速开始

查看: 816|回复: 8

Sufficient Dimension Reduction: Methods and Applications with R by Li Bing

[复制链接]
  • TA的每日心情
    开心
    2016-3-19 06:18
  • 签到天数: 18 天

    [LV.4]偶尔看看III

    发表于 2018-8-8 19:31:45 | 显示全部楼层 |阅读模式
    Sufficient Dimension Reduction: Methods and Applications with R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)May 1, 2018
    by Bing Li

    1498704476.01.S001.LXXXXXXX.jpg

    高清pdf 回复可见
    游客,如果您要查看本帖隐藏内容请回复


    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.



    回复

    使用道具 举报

  • TA的每日心情
    开心
    2016-5-5 12:23
  • 签到天数: 1 天

    [LV.1]初来乍到

    发表于 2018-8-8 21:28:19 来自手机 | 显示全部楼层
    不错,下载看看。。。。。。。。
    回复 支持 反对

    使用道具 举报

  • TA的每日心情
    郁闷
    2017-7-5 13:19
  • 签到天数: 84 天

    [LV.6]常住居民II

    发表于 2018-8-8 23:00:10 | 显示全部楼层
    谢谢分享啊
    回复 支持 反对

    使用道具 举报

  • TA的每日心情
    开心
    2019-1-1 22:16
  • 签到天数: 620 天

    [LV.9]以坛为家II

    发表于 2018-8-14 13:24:50 | 显示全部楼层
    好书,下载学习了
    回复 支持 反对

    使用道具 举报

  • TA的每日心情
    开心
    2018-11-22 08:46
  • 签到天数: 1399 天

    [LV.10]以坛为家III

    发表于 2018-8-15 13:22:13 | 显示全部楼层
    lookmit thank you
    回复 支持 反对

    使用道具 举报

    您需要登录后才可以回帖 登录 | 注册

    本版积分规则

    数学建模与统计建模论坛微信群
        
      手机版|Archiver|鄂ICP备16007464号

    GMT+8, 2020-7-15 04:39 , Processed in 0.180055 second(s), 25 queries .

    © 2001-2011 Powered by Discuz! X3.2. Theme By Yeei! Licensed

    返回顶部