Horvitz-Thompson estimator of population mean under inverse sampling designs

Authors

  • Mohammad Mohammadi School of Mathematical Science, Isfahan University of Technology, Isfahan, Iran.
  • Mohammad Salehi Marzijarani Department of Mathematical Sciences, Isfahan University of Technology 84156-83111, Iran; Department of Mathematics, Statistics and Physics, Qatar University, P.O.Box 2713, Doha, Qatar.
Abstract:

Inverse sampling design is generally considered to be appropriate technique when the population is divided into two subpopulations, one of which contains only few units. In this paper, we derive the Horvitz-Thompson estimator for the population mean under inverse sampling designs, where subpopulation sizes are known. We then introduce an alternative unbiased estimator, corresponding to post-stratification approach. Both of these are not location-invariant, but this is ignorable for alternative estimator. Using a simulation study, we find that Horvitz-Thompson estimator is an efficient estimator when the mean of the off-interest subpopulation is close to zero while the alternative estimator appears to be an efficient estimator in general.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

horvitz-thompson estimator of population mean under inverse sampling designs

inverse sampling design is generally considered to be appropriate technique when the population is divided into two subpopulations, one of which contains only few units. in this paper, we derive the horvitz-thompson estimator for the population mean under inverse sampling designs, where subpopulation sizes are known. we then introduce an alternative unbiased estimator, corresponding to post-str...

full text

The Horvitz-Thompson Estimator

The Horvitz-Thompson estimator is a general estimator for a population total, which can be used for any probability sampling plan. This includes both sampling with and without replacement. • Let π i be the probability that the i th unit of the population is included in the sample (inclusion probability). • On each unit i, we measure a response y i , and typically seek to estimate: τ = N i=1 y i...

full text

On the Simple Inverse Sampling with Replacement

In this paper we derive some unbiased estimators of the population mean under simple inverse sampling with replacement, using the class of Hansen-Hurwitz and Horvitz-Thompson type estimators and the post-stratification approach. We also compare the efficiency of resulting estimators together with Murthy's estimator. We show that in despite of general belief, the strategy consisting of inverse s...

full text

A New Exponential Type Estimator for the Population Mean in Simple Random Sampling

‎In this paper‎, ‎a new estimate of exponential type of auxiliary information to help simple random sampling without replacement of the finite population mean is introduced‎. ‎This new estimator with a few other estimates using two real data sets are compared with the mean square error‎.

full text

On Design-weighted Local Fitting and Its Relation to the Horvitz-thompson Estimator

Weighting is a widely used concept in many fields of statistics and has frequently caused controversies on its justification and benefit. In this paper, we analyze design-weighted versions of the well-known local polynomial regression estimators, derive their asymptotic bias and variance, and observe that the asymptotically optimal weights are in conflict with (practically motivated) weighting ...

full text

Computational Approach to Generalized Ratio Type Estimator of Population Mean Under Two Phase Sampling

In the present draft, we propose the computational approach to generalized ratio type estimator of population mean of the main variable under study using auxiliary information. The expressions for the bias and mean square errors (MSE) have been obtained up to the first order of approximation. The minimum value of the MSE of the proposed estimator is also obtained for the optimum value of the ch...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 38  issue 2

pages  333- 347

publication date 2012-07-15

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023