Transcription of Lecture 12 Nonparametric Regression
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Lecture 12 Nonparametric Regression
RS EC2 - Lecture 1111 Lecture 12 Nonparametric Regression The goal of a Regression analysis is to produce a reasonable analysis to the unknown response function f, where for Ndata points (Xi,Yi), the relationship can be modeled as - Note: m(.) = E[y|x]if E[ |x]=0 , x We have different ways to model the conditional expectation function (CEF), m(.):-Parametric approach- Nonparametric approach- Semi-parametric Parametric Regression : IntroductionNixmyiii,,1,)( RS EC2 - Lecture 112 Parametric approach: m(.) is known and smooth. It is fully described by a finite set of parameters, to be estimated. Easy interpretation. For example, a linear model: Nonparametric approach: m(.) is smooth, flexible, but unknown. Let the data determine the shape of m(.). Difficult interpretation. Semi-parametric approach: m(.) have some parameters -to be estimated-, but some parts are determined by the Parametric Regression : IntroductionNixyiii,,1,' Nixmyiii,,1,)( Nizmxyiizii,,1,)(' 4 Non Parametric Regression : IntroductionRS EC2 - Lecture 1135 Regression : Smoothing We want to relate y with x, without assuming any functional form.
RS – EC2 - Lecture 11 6 Figure 1. Expenditure of potatoes as a function of net income. h = 0.1, 1.0, N = 7125, year = 1973. Blue line is the smooth. From Hardle (1990). Regression: Smoothing – Example 2 12 Regression: Smoothing - Interpretation • Suppose the weights add up to 1 for all xi. The I Ý(x) is a least squares
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