Nonparametric weighted estimators for biased data

Nonparametric weighted estimators for biased data

Weighted Kernel Estimators in Nonparametric Binomial Regression

This paper is concerned with nonparametric binomial regression. Two kernel-based binomial regression estimators and their bias-adjusted versions are proposed, whose kernels are weighted by the inverses of variance estimators of the observed proportion at each covariate. Asymptotic theories for deriving asymptotic mean squared errors (AMSEs) of proposed estimators are developed. Comparisons with...

Nonparametric density deconvolution by weighted kernel estimators

JSM, Denver, 4 August 2008 – 3 / 23 We observe a univariate random sample Y1, . . . , Yn from a density g, where Yi = Xi + Zi (i = 1, . . . , n). Here X1, . . . , Xn are independent and identically distributed with unknown continuous density f , and the measurement errors Z1, . . . , Zn form a random sample from the continuous density η which we assume to be known. Our goal is to obtain a nonpa...

Maintaining Nonparametric Estimators over Data Streams

An effective processing and analysis of data streams is of utmost importance for a plethora of emerging applications like network monitoring, traffic management, and financial tickers. In addition to the management of transient and potentially unbounded streams, their analysis with advanced data mining techniques has been identified as a research challenge. A well-established class of mining te...

Density Estimators for Truncated Dependent Data

In some long term studies, a series of dependent and possibly truncated lifetime data may be observed. Suppose that the lifetimes have a common continuous distribution function F. A popular stochastic measure of the distance between the density function f of the lifetimes and its kernel estimate fn is the integrated square error (ISE). In this paper, we derive a central limit theorem for t...