We develop asymptotic theory for weighted likelihood estimators (WLE) under two-phase stratified sampling without replacement. We also consider several variants of WLEs involving estimated weights and calibration. A set of empirical process tools are developed including a Glivenko-Cantelli theorem, a theorem for rates of convergence of M-estimators, and a Donsker theorem for the inverse probabi...

The population parameters are estimated using sample surveys. However, the current investigation’s goal is to estimate subpopulations such as total through a calibration approach. proposed estimator’s properties have been given under simple random sampling. In addition, class of estimators for subpopulation has discussed. estimator evaluated theoretically and empirically in comparative study. r...

Statistical disclosure limitation is widely used by data collecting institutions to provide safe individual data. However, the choice of the disclosure limitation method severely affects the quality of the data. In particular, estimators for nonlinear models based on data which are masked by standard disclosure limitation techniques such as blanking or noise addition lead to inconsistent parame...

Design weights in surveys are often adjusted to accommodate auxiliary information and to meet pre-specified range restrictions, typically via some ad hoc algorithmic adjustment to a generalised regression estimator. In this paper, we present a simple solution to this problem using empirical likelihood methods or generalised regression. We first develop algorithms for computing empirical likelih...

In quantile regression of survival data, the estimation of the regression coefficients for extreme quantiles can be affected by severe censoring. Measurement error in covariates also leads to bias and loss in efficiency of estimators. In this seminar, we discuss the methodologies that effectively use the auxiliary information to improve the efficiency of censored quantile regression estimators....

Estimating equations have found wide popularity recently in parametric problems, yielding consistent estimators with asymptotically valid inferences obtained via the sandwich formula. Motivated by a problem in nutritional epidemiology, we use estimating equations to derive nonparametric estimators of a \parameter" depending on a predictor. The nonparametric component is estimated via local poly...

Abstract In this paper, two modified, design-based calibration ratio-type estimators are presented. The suggested were developed under stratified random sampling using information on an auxiliary variable in the form of robust statistical measures, including Gini’s mean difference, Downton’s method and probability weighted moments. properties (biases MSEs) proposed studied up to terms first-ord...

kernel density estimators are the basic tools for density estimation in non-parametric statistics.  the k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. in this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

In a finite population sampling survey, auxiliary information is commonly used to improve the Horvitz–Thompson estimators and calibration has been extensively by national statistical agencies over last decades for that purpose. This method enables make consistent with known totals of variables reduce variance if are explanatory variable interest. Nowadays, it not unusual anymore have high-dimen...

One of the most useful indicators relative dispersion is coefficient variation. The characteristics variation have contributed to its widespread use in scientific and academic disciplines, with real life applications. traditional estimators are based on conventional moments; therefore, these highly affected by presence extreme values. In this article, we develop some novel calibration-based for...