An improved exponential estimator of finite population mean in simple random sampling using an auxiliary attribute

Grover, Lovleen Kumar; Kaur, Parmdeep

doi:10.1016/j.amc.2011.08.035

Cited by 10 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Expressions for the bias and MSE of the proposed estimator are obtained under first order of approximation. Based on both the theoretical and numerical comparisons, we showed that the proposed estimator always performs better than the sample mean estimator, traditional ratio and product estimators, linear regression estimator, Bahl & Tuteja (1991) estimators, Rao (1991) estimator, andKaur (2011) estimator. Hence, we recommend the use of the proposed estimator for a more efficient estimation of the finite population mean in simple random sampling.…”

Section: Resultsmentioning

confidence: 99%

“…Following Rao (1991) and Bahl & Tuteja (1991), Grover & Kaur (2011) suggested an exponential type estimator ofȲ , given bŷ…”

Section: Grover and Kaur (2011) Estimatormentioning

confidence: 99%

“…Recently many research articles have appeared where authors have tried to modify existing estimators or construct new hybrid type estimators. Some contribution in this area are due to Bahl & Tuteja (1991), Singh, Chauhan & Sawan (2008), Singh, Chauhan, Sawan & Smarandache (2009), Yadav & Kadilar (2013), Haq & Shabbir (2013), Singh, Sharma & Tailor (2014) and Grover & Kaur, (2011.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Difference-Cum-Exponential Type Estimator of Finite Population Mean in Simple Random Sampling

Shabbir¹,

Haq²,

Gupta³

2014

Rev. colomb. estad.

View full text Add to dashboard Cite

Auxiliary information is frequently used to improve the accuracy of the estimators when estimating the unknown population parameters. In this paper, we propose a new difference-cum-exponential type estimator for the finite population mean using auxiliary information in simple random sampling. The expressions for the bias and mean squared error of the proposed estimator are obtained under first order of approximation. It is shown theoretically, that the proposed estimator is always more efficient than the sample mean, ratio, product, regression and several other existing estimators considered here. An empirical study using 10 data sets is also conducted to validate the theoretical findings.Key words: Ratio estimator, Auxiliary Variable, Exponential type estimator, Bias, MSE, Efficiency. ResumenInformación auxiliar se utiliza con frecuencia para mejorar la precisión de los estimadores al estimar los parámetros poblacionales desconocidos. En este trabajo, se propone un nuevo tipo de diferencia-cum-exponencial estimador de la población finita implicar el uso de información auxiliar en muestreo aleatorio simple. Las expresiones para el sesgo y el error cuadrático medio del estimador propuesto se obtienen en primer orden de aproximación. Se muestra teóricamente, que el estimador propuesto es siempre más eficiente que la media de la muestra, la relación de, producto, regresión y varios otros a Professor.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Following Rao (1991) and Bahl & Tuteja (1991), Grover & Kaur (2011) suggested an exponential type estimator ofȲ , given bŷ…”

Section: Grover and Kaur (2011) Estimatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Difference-Cum-Exponential Type Estimator of Finite Population Mean in Simple Random Sampling

Shabbir¹,

Haq²,

Gupta³

2014

Rev. colomb. estad.

View full text Add to dashboard Cite

show abstract

“…For these reasons various authors such as [15,16,17,18,19] have paid their attention towards the improved estimation of population mean Y of the study variable y taking into consideration the point biserial correlation between a variable and an attribute. In order to have an estimate of the study variable y, assuming the knowledge of the population proportion P, [1] proposed the following estimator…”

Section: Introductionmentioning

confidence: 99%

An Almost Unbiased Estimator for Population Mean using Known Value of Population Parameter(s)

Singh¹,

Gupta²,

Malik³

2014

J. Stat. Appl. Pro.

View full text Add to dashboard Cite

In this paper we have proposed an almost unbiased estimator using known value of some population parameter(s) with known population proportion of an auxiliary variable. A class of estimators is defined which includes [1], [2] and [3] estimators. Under simple random sampling without replacement (SRSWOR) scheme the expressions for bias and mean square error (MSE) are derived. Numerical illustrations are given in support of the present study.

show abstract

“…In the literature, some authors have proposed many efficient ratio-, product-, and regression-type estimators using one auxiliary variable or attribute, including Singh and Vishwakarma [1], Grover and Kaur [2,3], Singh et al [4], Singh and Solanki [5], and Gupta and Shabbir [6]. More recently, several authors have proposed efficient estimators of finite population mean using two variables or attributes, including, Abu-Dayyeh et al [7], Kadilar and Cingi [8], Malik and Singh [9], Sharma and Singh [10], and Muneer et al [11].…”

Section: Introductionmentioning

confidence: 99%

Efficient Estimator of a Finite Population Mean Using Two Auxiliary Variables and Numerical Application in Agricultural, Biomedical, and Power Engineering

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To improve the efficiency of an estimator with two auxiliary variables, we propose a new estimator of a finite population mean under simple random sampling. The bias and mean square error expressions of the proposed estimator have been obtained. In a comparison study, we found that the new estimator was consistently better than those of Abu-Dayyeh et al., Kadilar and Cingi, and Malik and Singh, as well as the regression estimator using two auxiliary variables, and that the minimum MSE values of the previous three above reported estimators were equal. We used four numerical examples in agricultural, biomedical, and power engineering to support these theoretical results, thus enriching the theory of survey samples by the development of new estimators with two auxiliary variables.

show abstract

An improved exponential estimator of finite population mean in simple random sampling using an auxiliary attribute

Cited by 10 publications

References 4 publications

A New Difference-Cum-Exponential Type Estimator of Finite Population Mean in Simple Random Sampling

A New Difference-Cum-Exponential Type Estimator of Finite Population Mean in Simple Random Sampling

An Almost Unbiased Estimator for Population Mean using Known Value of Population Parameter(s)

Efficient Estimator of a Finite Population Mean Using Two Auxiliary Variables and Numerical Application in Agricultural, Biomedical, and Power Engineering

Contact Info

Product

Resources

About