Clarifies modern data analysis through nonparametric density estimation for a complete
working knowledge of the theory and methods Featuring a thoroughly revised presentation …

A kernel smoother is an intuitive estimate of a regression function or conditional expectation;
at each point x_0 the estimate of E(Y∣x_0) is a weighted mean of the sample Y_i, with …

For nonparametric regression, there are two popular methods for constructing kernel
estimators, involving choosing weights either by direct kernel evaluation or by the …

We consider local polynomial fitting for estimating a regression function and its derivatives
nonparametrically. This method possesses many nice features, among which automatic …

A Standard problem in sample survey inference is that of predicting the finite population total
H of a function h (y) of a random variable Y. The model-based approach to this problem first …

New local linear estimators are proposed for a wide class of nonparametric regression
models. The estimators are uniformly consistent regardless of satisfying traditional …

Nested estimation involves estimating an expectation of a function of a conditional
expectation via simulation. This problem has of late received increasing attention amongst …

Publisher Summary This chapter discusses the general nonparametric regression
estimation and testing in econometrics. The basic computer algorithm in nonparametric …

Consistent weighted least square estimators are proposed for a wide class of nonparametric
regression models with random regression function, where this real-valued random function …

It is well known that kernel regression estimators are subject to so-called boundary or edge
effects, a phenomenon in which the bias of an estimator increases near the endpoints of the …