Computational Statistics Handbook with MATLAB, 2/e (Hardcover)
暫譯: MATLAB計算統計手冊(第二版,精裝本)

Wendy L. Martinez, Angel R. Martinez

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商品描述

As with the bestselling first edition, Computational Statistics Handbook with MATLAB®, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of the algorithms in data analysis. Updated for MATLAB® R2007a and the Statistics Toolbox, Version 6.0, this edition incorporates many additional computational statistics topics.

New to the Second Edition

•          New functions for multivariate normal and multivariate t distributions

•          Updated information on the new MATLAB functionality for univariate and bivariate histograms, glyphs, and parallel coordinate plots

•          New content on independent component analysis, nonlinear dimensionality reduction, and multidimensional scaling

•          New topics on linear classifiers, quadratic classifiers, and voting methods, such as bagging, boosting, and random forests

•          More methods for unsupervised learning, including model-based clustering and techniques for assessing the results of clustering

•          A new chapter on parametric models that covers spline regression models, logistic regression, and generalized linear models

•          Expanded information on smoothers, such as bin smoothing, running mean and line smoothers, and smoothing splines

With numerous problems and suggestions for further reading, this accessible text facilitates an understanding of computational statistics concepts and how they are employed in data analysis.

商品描述(中文翻譯)

與暢銷的第一版相同,使用 MATLAB® 的計算統計手冊,第二版 涵蓋了一些當前計算統計中最常用的技術。作者強調實踐,專注於方法的實施,並提供了程序的算法描述以及示例,說明如何在數據分析中使用這些算法。此版本已更新至 MATLAB® R2007a 和統計工具箱第 6.0 版,並納入了許多額外的計算統計主題。

第二版的新內容

• 新增多變量常態和多變量 t 分佈的函數

• 更新有關單變量和雙變量直方圖、圖形和平行座標圖的新 MATLAB 功能的信息

• 新增獨立成分分析、非線性降維和多維縮放的內容

• 新增線性分類器、二次分類器和投票方法(如 bagging、boosting 和隨機森林)的主題

• 更多無監督學習的方法,包括基於模型的聚類和評估聚類結果的技術

• 新增一章有關參數模型,涵蓋樣條回歸模型、邏輯回歸和廣義線性模型

• 擴展有關平滑器的信息,如箱平滑、移動平均和線性平滑器,以及平滑樣條

本書提供了大量問題和進一步閱讀的建議,幫助讀者理解計算統計概念及其在數據分析中的應用。

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