Robust Regression and Outlier Detection pdf epub mobi txt 电子书下载 2026

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出版者:John Wiley & Sons Inc

作者:Rousseeuw, Peter J./ Leroy, Annick M.

出品人:

页数:360

译者:

出版时间:2003-10

价格:990.00元

装帧:Pap

isbn号码:9780471488552

丛书系列:

图书标签:

Robust Regression
Outlier Detection
Statistical Modeling
Data Analysis
Regression Analysis
Machine Learning
Data Mining
Applied Statistics
Computational Statistics
Data Science

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具体描述

WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The writing style is clear and informal, and much of the discussion is oriented to application. In short, the book is a keeper." -Mathematical Geology "I would highly recommend the addition of this book to the libraries of both students and professionals. It is a useful textbook for the graduate student, because it emphasizes both the philosophy and practice of robustness in regression settings, and it provides excellent examples of precise, logical proofs of theorems...Even for those who are familiar with robustness, the book will be a good reference because it consolidates the research in high-breakdown affine equivariant estimators and includes an extensive bibliography in robust regression, outlier diagnostics, and related methods. The aim of this book, the authors tell us, is 'to make robust regression available for everyday statistical practice.' Rousseeuw and Leroy have included all of the necessary ingredients to make this happen." -Journal of the American Statistical Association

这本《Robust Regression and Outlier Detection》书籍旨在为读者提供一种全面且系统的学习路径，帮助他们掌握在实际数据分析中应用稳健回归与异常检测技术的方法。通过详细的章节安排和丰富的实例，该书深入探讨了传统统计模型在面对数据偏离规则、存在异常点时可能遇到的问题，并介绍了一系列有效的解决方案。读者将了解如何在建模过程中选择合适的稳健方法，避免因极值或异常值而导致结果的不准确和不可靠。书中不仅涵盖了稳健回归算法，如M-estimator、RANSAC等，还详细分析了数据预处理的重要性，为读者打下坚实的理论基础。此外，书中特别注重展示各种实际应用案例，通过具体场景说明如何识别和处理异常点，从而提升模型的稳健性与可靠性。对于希望深入理解复杂统计现象的人来说，这本书提供了深入剖析的内容，使他们能够更自信地应对复杂数据环境中的挑战。在整个过程中，作者不仅注重理论推导，还强调实践操作，帮助读者将所学知识转化为具体应用。通过对各类技术手段、软件工具和分析方法的系统介绍，这本书为研究人员、数据科学家及相关领域从业者提供了一个全面且实用的学习资源。该书内容详尽，思路清晰，每一章都旨在为读者带来深刻的洞察与有效的解决方案，使其在处理复杂数据时具备更强的能力和自信。总体而言，这本书以详实且严谨的方式呈现了稳健回归和异常检测的核心概念和应用方法，特别适合那些希望提升数据分析水平、深入理解复杂统计问题的人群。通过丰富的内容与实际案例，该书不仅帮助读者掌握先进技术，还增强了他们解决真实数据问题的能力，真正实现从理论到实践的转化。