文章基本信息

标题：Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)
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作者：Jeremy Ferwerda ; Jens Hainmueller ; Chad J. Hazlett 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2017
卷号：79
期号：1
页码：1-26
DOI：10.18637/jss.v079.i03
语种：English
出版社：University of California, Los Angeles
摘要：The Stata package krls as well as the R package KRLS implement kernel-based regularized least squares (KRLS), a machine learning method described in Hainmueller and Hazlett (2014) that allows users to tackle regression and classification problems without strong functional form assumptions or a specification search. The flexible KRLS estimator learns the functional form from the data, thereby protecting inferences against misspecification bias. Yet it nevertheless allows for interpretability and inference in ways similar to ordinary regression models. In particular, KRLS provides closed-form estimates for the predicted values, variances, and the pointwise partial derivatives that characterize the marginal effects of each independent variable at each data point in the covariate space. The method is thus a convenient and powerful alternative to ordinary least squares and other generalized linear models for regression-based analyses.
关键词：machine learning;regression;classification;prediction;Stata;R