Ratio estimators for the population variance in simple and stratified random sampling

Kadılar, Cem; Çıngı, Hülya

doi:10.1016/j.amc.2005.04.032

Cited by 93 publications

(74 citation statements)

References 10 publications

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“…The mean squared error of the estimators H is minimized when Kadilar and Cingi (2006) Upadhyaya and Singh (1999a) 6 t if   …”

Section: The Proposed Class Of Estimatorsmentioning

confidence: 99%

“…Das (1988) has proposed some wider classes of estimators for estimating finite population variance. Isaki (1983), Kadilar and Cingi (2006), Singh and Chandra (2008), Dubey and Sharma (2008), Gupta and Shabbir (2008), Singh and Solanki (2013) and Tailor and Lone (2013) and others contributed well in estimating the finite population variance.…”

Section: Introductionmentioning

confidence: 99%

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A Family of Estimators for Estimating Population Variance Using Auxiliary Information in Sample Survey

Lone

Tailor

2015

Pak.j.stat.oper.res.

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“…The mean squared error of the estimators H is minimized when Kadilar and Cingi (2006) Upadhyaya and Singh (1999a) 6 t if   …”

Section: The Proposed Class Of Estimatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Family of Estimators for Estimating Population Variance Using Auxiliary Information in Sample Survey

Lone

Tailor

2015

Pak.j.stat.oper.res.

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“…In this paper, the Optimal Stratification Method(also known as Accumulative Square Root Method) is used to determinate the boundaries of strata, which was proposed by Dalenius and Hodges [18], Firstly, a characteristic property of population should be determined, according to which population is stratified. Secondly, population is sorted by the values of the property of population from small to large.…”

Section: Determination Of Stratified Boundariesmentioning

confidence: 99%

Examination Method and Implementation for Field Survey Data of Crop Types Based on Multi-resolution Satellite Images

Yang

Zhu

2011

Computer and Computing Technologies in Agriculture IV

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Abstract. In order to examine the accuracy of large amount of the field survey data with less accurate, an examination method based on multi-resolution satellite images was proposed in this paper. As there were so large amount of data, stratified random sampling was used to obtain effective samples. Firstly, vegetation index derived from low-resolution satellite images at different times has been adopted as analysis factor. And wave curve charts were drawn with the vegetation index. From those charts, the statistics law of wave curves for different crop types was recognized using for crop types' classification. Secondly, high-resolution satellite images were used to correct the area of crop types to get the final classification results. Finally, the accuracy of the field survey data can be calculated by comparing the original survey data with the final classification results. Moreover, for convenience using, a software has been developed according to the above examination method.

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“…Isaki (1983) introduced the traditional ratio and regression estimator for population variance. Some related work in this direction is also due to Jhajj, Sharma & Grover (2005), Kadilar & Cingi (2006), Gupta & Shabbir (2008), Bansal, Javed & Khanna (2011), Singh, Chauhan, Swan & Smarandache (2011), Upadhyaya, Singh, Chatterjee & Yadav (2011), Subramani & Kumarapandiyan (2012, Nayak & Sahoo (2012) and Yadav & Kadilar (2013.…”

Section: Introductionmentioning

confidence: 99%

A Note on Generalized Exponential Type Estimator for Population Variance in Survey Sampling

Shabbir¹,

Gupta²

2015

Rev. colomb. estad.

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Recently a new generalized estimator for population variance using information on the auxiliary variable has been introduced by Asghar, Sanaullah & Hanif (2014). In that paper there was some inaccuracy in the bias and MSE expressions. In this paper, we provide the correct expressions for bias and MSE of the Asghar et al. (2014) estimator, up to the first order of approximation. We also propose a new generalized exponential type estimator for population variance which performs better than the existing estimators. Four data sets are used for numerical comparison of efficiencies.Key words: Auxiliary Variable, Bias, Efficiency, Mean Square Error, Variance. ResumenRecientemente, un nuevo estimador generalizado de varianza de la población utilizando información sobre la variable auxiliar ha sido introducida por Asghar et al. (2014). En ese documento había alguna inexactitud en las expresiones de sesgo y ECM. En este trabajo, proporcionamos las expresiones correctas de sesgo y ECM de Asghar et al. (2014) hasta el primer orden de aproximación. También proponemos un nuevo estimador tipo exponencial generalizado de la varianza de la población que se comporta mejor que los estimadores existentes. Cuatro conjuntos de datos se utilizan para la comparación numérica de la eficiencia.Palabras clave: error cuadrático medio, sesgo, variable auxiliar, varianza, eficiencia.a Professor.

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Ratio estimators for the population variance in simple and stratified random sampling

Cited by 93 publications

References 10 publications

A Family of Estimators for Estimating Population Variance Using Auxiliary Information in Sample Survey

A Family of Estimators for Estimating Population Variance Using Auxiliary Information in Sample Survey

Examination Method and Implementation for Field Survey Data of Crop Types Based on Multi-resolution Satellite Images

A Note on Generalized Exponential Type Estimator for Population Variance in Survey Sampling

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