An improved class of estimators for the population mean

Diana, Giancarlo; Giordan, Marco; Perri, Pier Francesco

doi:10.1007/s10260-010-0156-6

Cited by 32 publications

(23 citation statements)

References 31 publications

(46 reference statements)

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“…However, more light on this study can be focused if one would have included an empirical study. Overall this study is of academic interest as well as of practical importance, see, Diana et al (2011), Singh et al (2013) and Singh and Solanki (2013 a, b), Solanki and Singh(2013), Singh et al (2013), , Solanki et al (2015) and Singh and Pal (2016) etc.…”

Section: Discussionmentioning

confidence: 75%

A New Family of Estimators of the Population Variance using Information on Population Variance of Auxiliary Variable in Sample Surveys

Singh

Pal

2016

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This paper proposes a family of estimators of population variance 2 y S of the study variable y in the presence of known population variance 2 x S of the auxiliary variable x. It is identified that in addition to many, the recently proposed classes of estimators due to Sharma and Singh (2014) and Singh and Pal (2016) are members of the proposed family of estimators. Asymptotic expressions of bias and mean squared error (MSE) of the suggested family of estimators have been obtained. Asymptotic optimum estimator (AOE) in the family of estimators is identified. Some subclasses of estimators of the proposed family of estimators have been identified along with their properties. We have also given the theoretical comparisons among the estimators discussed in this paper. ASM Classification: 62D05.

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Section: Discussionmentioning

confidence: 75%

A New Family of Estimators of the Population Variance using Information on Population Variance of Auxiliary Variable in Sample Surveys

Singh

Pal

2016

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“…The first suggested class is obtained starting from a generalization of the third modified class, defined in ( 18 ), while the second class is the result of motivation from Diana et al. ( 2011 ).…”

Section: Suggested Classesmentioning

confidence: 99%

Some classes of estimators in the presence of non-response using auxiliary attribute

Riaz

Darda

2016

SpringerPlus

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In this paper, possible solutions of problem of non-response in the variable of interest are proposed when information about an auxiliary attribute is available. By taking motivation from the previous work, modified classes have been suggested for estimating population mean. Two new generalized classes of estimators are presented along with their asymptotic biases and variances. Efficacy analysis of the suggested classes is acquired with the usual regression estimator. Two real examples have been provided to show the efficiency of the proposed design approach and comparison of suggested estimators with the linear regression estimator.

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“…AssumingX known. Diana et al [3] have proposed a general class of biased estimators of the population meanȲ using known meanX in SRSWOR. We take motivation and propose a class of estimators in CSS assumingX is known…”

Section: First Classmentioning

confidence: 99%

“…The first class is modified by taking motivation from [3], while the second class is motivated from [21] (hereafter SS). We consider Diana et al and SS classes of estimators because these are more efficient than the regression estimator in simple random sampling without replacement (SRSWOR).…”

Section: Modified Classes Of Estimatorsmentioning

confidence: 99%

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Modified classes of estimators in circular systematic sampling

Riaz¹,

Diana²,

Shabbir³

2015

HJMS

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In this paper we consider the problem of estimation of population mean in circular systematic sampling design along with the non-response problem. For the population mean using auxiliary information three generalized classes of estimators are suggested. The biases and the mean square errors of the suggested classes of estimators are obtained and compared with sample mean, linear regression estimators, [23] estimator and [21] estimators. A numerical study is provided to show that the proposed classes of estimators based on circular systematic design can be more efficient than the estimators based on simple random sampling. Moreover, a simulation study is accomplished when some population parameters are assumed to be unknown.

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An improved class of estimators for the population mean

Abstract: Rao estimator, Regression estimator, Monte Carlo simulation, Efficiency comparisons,

Cited by 32 publications

References 31 publications

A New Family of Estimators of the Population Variance using Information on Population Variance of Auxiliary Variable in Sample Surveys

A New Family of Estimators of the Population Variance using Information on Population Variance of Auxiliary Variable in Sample Surveys

Some classes of estimators in the presence of non-response using auxiliary attribute

Modified classes of estimators in circular systematic sampling

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