The nature of the kernel density estimator (KDE) is to find underlying probability function (p.d.f) for a given dataset. key training KDE determine optimal bandwidth or Parzen window. All data points share fixed (scalar univariate and vector multivariate KDE) in (FKDE). In this paper, we propose an improved variable (IVKDE) which determines each point dataset based on integrated squared error (...

SUMMARY The estimation of population quantiles is of great interest when one is not prepared to assume a parametric form for the u.nderlying distribution. In addition, quantiles often arise as the natural thing to estimate when the underlying distribution is skewed. The sample quantile is a popular nonparametric estimator of the corresponding population quantile. Being a function of at most two...

This paper investigates finite sample properties of localized version of moment estimator including Local Generalized Method of Moments(LGMM) and conditional Euclidean Empirical Likelihood(CEEL) estimator. By comparing the performance of LGMM estimator and C-EEL estimator with various nonparametric techniques including the choice of bandwidth, choice of kernel and trimming strategies, this pape...

Abstract A two-step estimator of a nonparametric regression function via Kernel regularized least squares (KRLS) with parametric error covariance is proposed. The KRLS, not considering any information in the covariance, improved by incorporating allowing for both heteroskedasticity and autocorrelation, estimating function. two step procedure used, where first step, estimated using KRLS residual...

The problem of estimating the kernel mean in a reproducing kernel Hilbert space (RKHS) is central to kernel methods in that it is used by classical approaches (e.g., when centering a kernel PCA matrix), and it also forms the core inference step of modern kernel methods (e.g., kernel-based non-parametric tests) that rely on embedding probability distributions in RKHSs. Previous work [1] has show...

A semiparametric regression estimator that exploits categorical (i.e. discretesupport) kernel functions is developed for a broad class of hierarchical models including the pooled regression estimator, the fixed-effects estimator familiar from panel data, and the varying coefficient estimator, among others. Separate shrinking is allowed for each coefficient. Regressors may be continuous or discr...