NONLINEAR MULTIPLE REGRESSION METHODS:A SURVEY AND EXTENSIONS

KENNETH O. COGGER

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NONLINEAR MULTIPLE REGRESSION METHODS:A SURVEY AND EXTENSIONS

机译：非线性多元回归方法：研究与扩展

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This paper reviews some nonlinear statistical procedures useful in function approximation, classification, regression and time-series analysis. Primary emphasis is on piecewise linear models such as multivariate adaptive regression splines, adaptive logic networks, hinging hyperplanes and their conceptual differences. Potential and actual applications of these methods are cited. Software for implementation is discussed, and practical suggestions are given for improvement. Examples show the relative capabilities of the various methods, including their ability for universal approximation.

机译：本文回顾了一些对函数逼近，分类，回归和时间序列分析有用的非线性统计程序。主要重点在于分段线性模型，例如多元自适应回归样条，自适应逻辑网络，铰链超平面及其概念差异。列举了这些方法的潜在和实际应用。讨论了用于实现的软件，并提出了改进建议。示例显示了各种方法的相对功能，包括它们的通用逼近能力。

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来源
《International journal of intelligent systems in accounting, finance & management》 |2010年第1期|p.19-39|共21页
作者
KENNETH O. COGGER;
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作者单位

Kenneth O. Cogger, Peak Consulting, Inc., Conifer, CO 80433, USA;

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原文格式 PDF
正文语种 eng
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关键词
piecewise linear; multivariate adaptive regression spline; adaptive logic networks; hinging hyperplane;

机译：分段线性多元自适应回归样条;自适应逻辑网络;铰链超平面;

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NONLINEAR MULTIPLE REGRESSION METHODS:A SURVEY AND EXTENSIONS

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