Smoothing Parameters for Recursive Kernel Density Estimators under Censoring

Comparison of presmoothing methods in kernel density estimation under censoring

1 Departamento de Matemáticas, Universidade da Coruña, Facultad de Ciencias, 15071 A Coruña (Spain) [email protected] 2 Department of Mathematics and University Center for Statistics, Katholieke Universiteit Leuven, Celestijnenlaan 200B, B-3001 Leuven (Heverlee), Belgium; Box 2400 [email protected] 3 Departamento de Matemáticas, Universidade da Coruña, Facultad de Informática, 15071 A...

Deconvoluting Kernel Density Estimators

This paper considers estimation of a continuous bounded probability density when observations from the density are contaminated by additive measurement errors having a known distribution. Properties of the estimator obtained by deconvolving a kernel estimator of the observed data are investigated. When the kernel used is sufficiently smooth the deconvolved estimator is shown to be pointwise con...

Pruning kernel density estimators

Asymptotic Normality for Deconvolving Kernel Density Estimators

Suppose that we have 11 observations from the convolution model Y = X + £, where X and £ are the independent unobservable random variables, and £ is measurement error with a known distribution. We will discuss the asymptotic normality for deconvolving kernel density estimators of the unknown density f x 0 of X by assuming either the tail of the characteristic function of £ behaves as II I~Oexp(...

Optimal bandwidth selection for semi-recursive kernel regression estimators

In this paper we propose an automatic selection of the bandwidth of the semi-recursive kernel estimators of a regression function defined by the stochastic approximation algorithm. We showed that, using the selected bandwidth and some special stepsizes, the proposed semi-recursive estimators will be very competitive to the nonrecursive one in terms of estimation error but much better in terms o...