Abstract: Statistical methods using linear regression are based on the assumptions that errors, and hence the regression responses, are normally distributed. Variable transformations increase the scope and applicability of linear regression toward real applications, but many modeling problems cannot fit in the confines of these model assumptions. This chapter focuses on simple alternatives to basic least-squares regression. These estimators are constructed to be less sensitive to the outliers that can affect regular regression estimators. Robust regression estimators are made specifically for this purpose. Nonparametric or rank regression relies more on the order relations in the paired data rather than the actual data measurements, and isotonic regression represents a nonparametric regression model with simple constraints built in, such as the response being monotone with respect to one or more inputs.
Publication Year: 2022
Publication Date: 2022-10-28
Language: en
Type: other
Indexed In: ['crossref']
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