Using auxiliary information in statistical function estimation

Sergey Tarima; Dmitri Pavlov

ESAIM: Probability and Statistics (2005)

  • Volume: 10, page 11-23
  • ISSN: 1292-8100

Abstract

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In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of estimates based on samples obtained from some other mutually independent data sources. This method uses the fact that there is a correlation effect between estimators based on the current sample and auxiliary information from other sources. If variance covariance matrices of vectors of estimators used in the estimating procedure are known, this method produces more efficient estimates in terms of their variances compared to the estimates based on the current sample only. If these variance-covariance matrices are not known, their consistent estimates can be used as well such that the large sample properties of the method remain unchangeable. This approach allows to improve statistical properties of many standard estimators such as an empirical cumulative distribution function, empirical characteristic function, and Nelson-Aalen cumulative hazard estimator.

How to cite

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Tarima, Sergey, and Pavlov, Dmitri. "Using auxiliary information in statistical function estimation." ESAIM: Probability and Statistics 10 (2005): 11-23. <http://eudml.org/doc/104344>.

@article{Tarima2005,
abstract = { In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of estimates based on samples obtained from some other mutually independent data sources. This method uses the fact that there is a correlation effect between estimators based on the current sample and auxiliary information from other sources. If variance covariance matrices of vectors of estimators used in the estimating procedure are known, this method produces more efficient estimates in terms of their variances compared to the estimates based on the current sample only. If these variance-covariance matrices are not known, their consistent estimates can be used as well such that the large sample properties of the method remain unchangeable. This approach allows to improve statistical properties of many standard estimators such as an empirical cumulative distribution function, empirical characteristic function, and Nelson-Aalen cumulative hazard estimator. },
author = {Tarima, Sergey, Pavlov, Dmitri},
journal = {ESAIM: Probability and Statistics},
keywords = {Auxiliary information; multiple data sources; partially grouped samples; convergence rates.; partially grouped samples; convergence rates},
language = {eng},
month = {12},
pages = {11-23},
publisher = {EDP Sciences},
title = {Using auxiliary information in statistical function estimation},
url = {http://eudml.org/doc/104344},
volume = {10},
year = {2005},
}

TY - JOUR
AU - Tarima, Sergey
AU - Pavlov, Dmitri
TI - Using auxiliary information in statistical function estimation
JO - ESAIM: Probability and Statistics
DA - 2005/12//
PB - EDP Sciences
VL - 10
SP - 11
EP - 23
AB - In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of estimates based on samples obtained from some other mutually independent data sources. This method uses the fact that there is a correlation effect between estimators based on the current sample and auxiliary information from other sources. If variance covariance matrices of vectors of estimators used in the estimating procedure are known, this method produces more efficient estimates in terms of their variances compared to the estimates based on the current sample only. If these variance-covariance matrices are not known, their consistent estimates can be used as well such that the large sample properties of the method remain unchangeable. This approach allows to improve statistical properties of many standard estimators such as an empirical cumulative distribution function, empirical characteristic function, and Nelson-Aalen cumulative hazard estimator.
LA - eng
KW - Auxiliary information; multiple data sources; partially grouped samples; convergence rates.; partially grouped samples; convergence rates
UR - http://eudml.org/doc/104344
ER -

References

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  9. R.J.A. Little and D.B. Rubin, Statistical analysis with missing data. Wiley (2002).  Zbl1011.62004
  10. A.B. Owen, Empirical likelihood. Chapman and Hall (2001).  Zbl0989.62019
  11. V.N. Pugachev, Mixed methods of determining probabilistic characteristics [in Russian]. Soviet Radio, Moscow (1973).  
  12. J.N.K. Rao, J.G. Kovar and H.J. Mantel, On estimating distribution functions and quantiles from survey data using auxiliary information. Biometrika77 (1990) 365–375.  Zbl0716.62013
  13. B. Zhang, Confidence intervals for a distribution function in the presence of auxiliary information. Comput. Statist. Data Anal.21 (1996) 327–342.  Zbl0875.62136

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