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Estimation of a Finite Population Distribution Function

  • Parimal Mukhopadhyay
Part of the Lecture Notes in Statistics book series (LNS, volume 153)

Abstract

Estimation of a finite population distribution function has attracted considerable attention of survey statisticians over the last two decades. Our problem here is to estimate the finite population distribution function (d.f.)
$${{F}_{N}}(t) = \frac{1}{N}\sum\limits_{{i = 1}}^{N} {\Delta (t - {{y}_{i}})}$$
(6.1.1)
where Δ(z) is a step function with
$$\Delta (z) = 1(0) {\text{if}} z \geqslant 0 ({\text{otherwise),}}$$
on the basis of a sample s selected according to a sampling design p with selection probability p(s) and observations of the data.

Keywords

Auxiliary Variable Average Root Mean Square Error Relative Root Mean Square Error Relative Mean Error Empirical Likelihood Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 2001

Authors and Affiliations

  • Parimal Mukhopadhyay
    • 1
  1. 1.Applied Statistics UnitIndian Statistical InstituteCalcuttaIndia

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