21,215 research outputs found
On Improvement in Estimating Population Parameter(s) Using Auxiliary Information
The purpose of writing this book is to suggest some improved estimators using
auxiliary information in sampling schemes like simple random sampling and
systematic sampling.
This volume is a collection of five papers. The following problems have been
discussed in the book:
In chapter one an estimator in systematic sampling using auxiliary
information is studied in the presence of non-response. In second chapter some
improved estimators are suggested using auxiliary information. In third chapter
some improved ratio-type estimators are suggested and their properties are
studied under second order of approximation.
In chapter four and five some estimators are proposed for estimating unknown
population parameter(s) and their properties are studied.
This book will be helpful for the researchers and students who are working in
the field of finite population estimation.Comment: 63 pages, 8 tables. Educational Publishing & Journal of Matter
Regularity (Beijing
A nonparametric model-based estimator for the cumulative distribution function of a right censored variable in a finite population
In survey analysis, the estimation of the cumulative distribution function
(cdf) is of great interest: it allows for instance to derive quantiles
estimators or other non linear parameters derived from the cdf. We consider the
case where the response variable is a right censored duration variable. In this
framework, the classical estimator of the cdf is the Kaplan-Meier estimator. As
an alternative, we propose a nonparametric model-based estimator of the cdf in
a finite population. The new estimator uses auxiliary information brought by a
continuous covariate and is based on nonparametric median regression adapted to
the censored case. The bias and variance of the prediction error of the
estimator are estimated by a bootstrap procedure adapted to censoring. The new
estimator is compared by model-based simulations to the Kaplan-Meier estimator
computed with the sampled individuals: a significant gain in precision is
brought by the new method whatever the size of the sample and the censoring
rate. Welfare duration data are used to illustrate the new methodology.Comment: 18 pages, 5 figure
Exact balanced random imputation for sample survey data
Surveys usually suffer from non-response, which decreases the effective
sample size. Item non-response is typically handled by means of some form of
random imputation if we wish to preserve the distribution of the imputed
variable. This leads to an increased variability due to the imputation
variance, and several approaches have been proposed for reducing this
variability. Balanced imputation consists in selecting residuals at random at
the imputation stage, in such a way that the imputation variance of the
estimated total is eliminated or at least significantly reduced. In this work,
we propose an implementation of balanced random imputation which enables to
fully eliminate the imputation variance. Following the approach in Cardot et
al. (2013), we consider a regularized imputed estimator of a total and of a
distribution function, and we prove that they are consistent under the proposed
imputation method. Some simulation results support our findings
- …