Adaptive Nonparametric Density Estimation with B-Spline Bases

A proposed nonparametric mixture density estimation using B-spline functions

In this paper, we suppose that a density of probability f is expressed as a finite linear combination of second order B-spline functions. Then, we obtain a finite mixture of B-spline. We extend the Expectation Maximization (EM) algorithm in order to estimate the new mixture density. The experiments show that the proposed estimator using B-spline functions can produce a satisfactory estimation o...

Estimation of B-spline Nonparametric Regression Models using Information Criteria

Nonparametric regression modelling has received considerable attention and many methods have been proposed to draw information from data with complex structure. We consider the use of B-spline nonparametric regression models estimated by penalized likelihood methods. A crucial point in constructing the models is in the choice of a smoothing parameter and the number of knots, for which several a...

Nonparametric density estimation with adaptive varying window size

We propose a new method of kernel density estimation with a varying adaptive window width. This method is different from traditional ones in two aspects. First, we use symmetric as well as nonsymmetric left and right kernels with discontinuities and show that the fusion of these estimates results in accuracy improvement. Second, we develop estimates with adaptive varying window widths based on ...

Sequential nonparametric density estimation

Nonparametric volatility density estimation

B E RT VA N E S , P E T E R S P R E I J 1 and HARRY VAN ZANTEN 2 Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Plantage Muidergracht 24, 1018 TV Amsterdam, The Netherlands. E-mail: [email protected]; [email protected] Division of Mathematics and Computer Science, Faculty of Sciences, Free University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands...