Displaying similar documents to “Unbiased estimation of reliability for two-parameter exponential distribution under time censored sampling”

Unbiased estimation for two-parameter exponential distribution under time censored sampling

S. Sengupta (2009)

Applicationes Mathematicae

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The problem considered is that of unbiased estimation for a two-parameter exponential distribution under time censored sampling. We obtain a necessary form of an unbiasedly estimable parametric function and prove that there does not exist any unbiased estimator of the parameters and the mean of the distribution. For reliability estimation at a specified time point, we give a necessary and sufficient condition for the existence of an unbiased estimator and suggest an unbiased estimator...

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

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

Qüestiió

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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...

A Cramer-Rao analogue for median-unbiased estimators.

N. K. Sung, Gabriela Stangenhaus, Herbert T. David (1990)

Trabajos de Estadística

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Adopting a measure of dispersion proposed by Alamo [1964], and extending the analysis in Stangenhaus [1977] and Stangenhaus and David [1978b], an analogue of the classical Cramér-Rao lower bound for median-unbiased estimators is developed for absolutely continuous distributions with a single parameter, in which mean-unbiasedness, the Fisher information, and the variance are replaced by median-unbiasedness, the first absolute moment of the sample score, and the reciprocal of twice the...

Sampling design variance estimation of small area estimators in the Spanish Labour Force survey.

Montserrat Herrador, Domingo Morales, María Dolores Esteban, Ángel Sánchez, Laureano Santamaría, Yolanda Marhuenda, Agustín Pérez (2008)

SORT

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The main goal of this paper is to investigate how to estimate sampling design variances of modelbased and model-assisted small area estimators in a complex survey sampling setup. For this purpose the Spanish Labour Force Survey is considered. Sample and aggregated data are taken from the Canary Islands in the second trimester of 2003 in order to obtain some small area estimators of ILO unemployment totals. Several problems arising from the application of standard small area estimation...

Estimation of the size of a closed population

S. Sengupta (2010)

Applicationes Mathematicae

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The problem considered is that of estimation of the size (N) of a closed population under three sampling schemes admitting unbiased estimation of N. It is proved that for each of these schemes, the uniformly minimum variance unbiased estimator (UMVUE) of N is inadmissible under square error loss function. For the first scheme, the UMVUE is also the maximum likelihood estimator (MLE) of N. For the second scheme and a special case of the third, it is shown respectively that an MLE and...

On unbiased Lehmann-estimators of a variance of an exponential distribution with quadratic loss function.

Jadwiga Kicinska-Slaby (1982)

Trabajos de Estadística e Investigación Operativa

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Lehmann in [4] has generalised the notion of the unbiased estimator with respect to the assumed loss function. In [5] Singh considered admissible estimators of function λ-r of unknown parameter λ of gamma distribution with density f(x|λ, b) = λb-1 e-λx xb-1 / Γ(b), x>0, where b is a known parameter, for loss function L(λ -r, λ-r...

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

Ryszard Zieliński (2003)

Applicationes Mathematicae

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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).