10,551 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
Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach
Poverty maps are used to aid important political decisions such as allocation
of development funds by governments and international organizations. Those
decisions should be based on the most accurate poverty figures. However, often
reliable poverty figures are not available at fine geographical levels or for
particular risk population subgroups due to the sample size limitation of
current national surveys. These surveys cannot cover adequately all the desired
areas or population subgroups and, therefore, models relating the different
areas are needed to 'borrow strength" from area to area. In particular, the
Spanish Survey on Income and Living Conditions (SILC) produces national poverty
estimates but cannot provide poverty estimates by Spanish provinces due to the
poor precision of direct estimates, which use only the province specific data.
It also raises the ethical question of whether poverty is more severe for women
than for men in a given province. We develop a hierarchical Bayes (HB) approach
for poverty mapping in Spanish provinces by gender that overcomes the small
province sample size problem of the SILC. The proposed approach has a wide
scope of application because it can be used to estimate general nonlinear
parameters. We use a Bayesian version of the nested error regression model in
which Markov chain Monte Carlo procedures and the convergence monitoring
therein are avoided. A simulation study reveals good frequentist properties of
the HB approach. The resulting poverty maps indicate that poverty, both in
frequency and intensity, is localized mostly in the southern and western
provinces and it is more acute for women than for men in most of the provinces.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS702 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Generalised regression estimation given imperfectly matched auxiliary data
Generalised regression estimation allows one to make use of available
auxiliary information in survey sampling. We develop three types of generalised
regression estimator when the auxiliary data cannot be matched perfectly to the
sample units, so that the standard estimator is inapplicable. The inference
remains design-based. Consistency of the proposed estimators is either given by
construction or else can be tested given the observed sample and links. Mean
square errors can be estimated. A simulation study is used to explore the
potentials of the proposed estimators
Combining multiple observational data sources to estimate causal effects
The era of big data has witnessed an increasing availability of multiple data
sources for statistical analyses. We consider estimation of causal effects
combining big main data with unmeasured confounders and smaller validation data
with supplementary information on these confounders. Under the unconfoundedness
assumption with completely observed confounders, the smaller validation data
allow for constructing consistent estimators for causal effects, but the big
main data can only give error-prone estimators in general. However, by
leveraging the information in the big main data in a principled way, we can
improve the estimation efficiencies yet preserve the consistencies of the
initial estimators based solely on the validation data. Our framework applies
to asymptotically normal estimators, including the commonly-used regression
imputation, weighting, and matching estimators, and does not require a correct
specification of the model relating the unmeasured confounders to the observed
variables. We also propose appropriate bootstrap procedures, which makes our
method straightforward to implement using software routines for existing
estimators
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