Abstract
AbstractThis chapter discusses the estimation problem for a general nonparametric regression model which is linked to the problem of parametric estimation. We start with a linear regression model, then a generalized linear modeling is discussed. We also mention median and quantile regression. Some classical estimation procedures are introduced in a unified manner. The list includes least squares and least absolute deviation methods, M-estimators, maximum likelihood, and quasi maximum likelihood method. Different approaches of parametrizing of a general regression models are presented, and we particularly focus on polynomial, piecewise polynomial, and spline methods.KeywordsRegression FunctionChebyshev PolynomialHermite PolynomialPolynomial SystemCanonical ParametrizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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