Displaying similar documents to “Comparison of six almost unbiased ratio estimators.”

Almost unbiased ratio and product-type estimators in systematic sampling.

R. Singh, H. P. Singh (1998)

Qüestiió

Similarity:

In this paper we have suggested almost unbiased ratio-type and product-type estimators for estimating the population mean Y of the study variate y using information on an auxiliary variate x in systematic sampling. The variance expressions of the suggested estimators have been obtained and compared with usual unbiased estimator y*, Swain's (1964) ratio estimator y* and Shukla's product estimator y*. It has been shown that the proposed estimators are more efficient than usual unbiased...

An empirical evaluation of small area estimators.

Álex Costa, Albert Satorra, Eva Ventura (2003)

SORT

Similarity:

This paper compares five small area estimators. We use Monte Carlo simulation in the context of both artificial and real populations. In addition to the direct and indirect estimators, we consider the optimal composite estimator with population weights, and two composite estimators with estimated weights: one that assumes homogeneity of within area variance and squared bias and one that uses area-specific estimates of variance and squared bias. In the study with real population, we found...

Improving small area estimation by combining surveys: new perspectives in regional statistics.

Alex Costa, Albert Satorra, Eva Ventura (2006)

SORT

Similarity:

A national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be devised. We investigate such methods and assess them by a Monte Carlo study. We explore how a complementary survey can be exploited in small area estimation. We use the context of the Spanish Labour...

Constructing median-unbiased estimators in one-parameter families of distributions via stochastic ordering

Ryszard Zieliński (2003)

Applicationes Mathematicae

Similarity:

If θ ∈ Θ is an unknown real parameter of a given distribution, we are interested in constructing an exactly median-unbiased estimator θ̂ of θ, i.e. an estimator θ̂ such that a median Med(θ̂ ) of the estimator equals θ, uniformly over θ ∈ Θ. We shall consider the problem in the case of a fixed sample size n (nonasymptotic approach).

An alternative analysis of variance.

Nicholas T. Longford (2008)

SORT

Similarity:

The one-way analysis of variance is a staple of elementary statistics courses. The hypothesis test of homogeneity of the means encourages the use of the selected-model based estimators which are usually assessed without any regard for the uncertainty about the outcome of the test. We expose the weaknesses of such estimators when the uncertainty is taken into account, as it should be, and propose synthetic estimators as an alternative.

Improving both domain and total area estimation by composition.

Alex Costa, Albert Satorra, Eva Ventura (2004)

SORT

Similarity:

In this article we propose small area estimators for both the small and large area parameters. When the objective is to estimate parameters at both levels, optimality is achieved by a sample design that combines fixed and proportional allocation. In such a design, one fraction of the sample is distributed proportionally among the small areas and the rest is evenly distributed. Simulation is used to assess the performance of the direct estimator and two composite small area estimators,...

Kernel estimators and the Dvoretzky-Kiefer-Wolfowitz inequality

Ryszard Zieliński (2007)

Applicationes Mathematicae

Similarity:

It turns out that for standard kernel estimators no inequality like that of Dvoretzky-Kiefer-Wolfowitz can be constructed, and as a result it is impossible to answer the question of how many observations are needed to guarantee a prescribed level of accuracy of the estimator. A remedy is to adapt the bandwidth to the sample at hand.