scholarly journals A Two-Parameter Ratio-Product-Ratio Estimator Using Auxiliary Information

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Peter S. Chami ◽  
Bernd Sing ◽  
Doneal Thomas
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Information ◽  
Degree Of Approximation ◽  
Simple Random Sample ◽  
Ratio Estimator ◽  
Sample Mean ◽  
Product Ratio ◽  
Squared Error ◽  
Parameter Ratio ◽  
Two Parameter

We propose a two-parameter ratio-product-ratio estimator for a finite population mean in a simple random sample without replacement following the methodology in the studies of Ray and Sahai (1980), Sahai and Ray (1980), A. Sahai and A. Sahai (1985), and Singh and Espejo (2003).The bias and mean squared error of our proposed estimator are obtained to the first degree of approximation. We derive conditions for the parameters under which the proposed estimator has smaller mean squared error than the sample mean, ratio, and product estimators. We carry out an application showing that the proposed estimator outperforms the traditional estimators using groundwater data taken from a geological site in the state of Florida.

scholarly journals A General Class of Dual to Ratio Estimator

2020 ◽  
pp. 421-431
Author(s):  
Housila Prasad Singh ◽  
Pragati Nigam
Keyword(s):  
General Class ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Ratio Estimator ◽  
Sample Surveys ◽  
Sample Mean ◽  
Population Mean ◽  
Squared Error ◽  
Class Of Estimators ◽  
Ratio Estimators

In this paper we have considered the problem of estimating the population mean using auxiliary information in sample surveys. A class of dual to ratio estimators has been defined. Exact expressions for bias and mean squared error of the suggested class of dual to ratio estimator have been obtained. In particular, properties of some members of the proposed class of dual to ratio estimators have been discussed. It has been shown that the proposed class of estimators is more efficient than the sample mean, ratio estimator, dual to ratio estimator and some members of the suggested class of estimators in some realistic conditions. Some numerical illustrations are given in support of the present study.

scholarly journals An Improved Family of Estimators of Finite Population Mean Using Information on an Auxiliary Variable in Sample Surveys

2017 ◽  
Vol 13 (2) ◽  
pp. 77-108
Author(s):  
H. P. Singh ◽  
A. Yadav
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Sample Surveys ◽  
Sample Mean ◽  
Population Mean ◽  
Squared Error ◽  
Parameter Ratio ◽  
Large Sample Approximation ◽  
Class Of Estimators

Abstract In this paper we have suggested a family of estimators of the population mean using auxiliary information in sample surveys. The bias and mean squared error of the proposed class of estimators have been obtained under large sample approximation. We have derived the conditions for the parameters under which the proposed class of estimators has smaller mean squared error than the sample mean, ratio, product, regression estimator and the two parameter ratio-product-ratio estimators envisaged by Chami et al (2012). An empirical study is carried out to demonstrate the performance of the proposed class of estimators over other existing estimators.

scholarly journals ON THE DEVELOPMENT OF RATIO TYPE ESTIMATORS USING AUXILIARY INFORMATION

2021 ◽  
Vol 21 (1) ◽  
pp. 163-170
Author(s):  
MUHAMMAD IJAZ ◽  
ATTA ULLAH ◽  
TOLGA ZAMAN
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Information ◽  
Ratio Estimator ◽  
Order Of Approximation ◽  
Large Sample ◽  
First Order ◽  
Squared Error ◽  
Ratio Estimators ◽  
Modified Forms

The paper produces some new modified forms of the ratio estimators using the auxiliary information. The large sample properties, that is, the bias and mean squared error up to the first order of approximation are determined. The comparison is made with other existing estimators by using an applied data. It has been observed that the proposed estimators have a fewer mean squared error and leads to the efficient results as compared to the classical ratio estimator, Sisodia and Dwivedi, Singh and Kakran, Upadhyaya and Singh estimators.

scholarly journals On Improving Ratio/Product Estimator by Ratio/Product-cum-Mean-per-Unit Estimator Targeting More Efficient Use of Auxiliary Information

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Angela Shirley ◽  
Ashok Sahai ◽  
Isaac Dialsingh
Keyword(s):  
Auxiliary Information ◽  
Simple Random Sample ◽  
Sample Mean ◽  
Parameter Ratio ◽  
Large Sample Approximation ◽  
Product Estimator ◽  
Sample Approximation ◽  
The Mean ◽  
Classical Linear Regression ◽  
Linear Regression Estimator

To achieve a more efficient use of auxiliary information we propose single-parameter ratio/product-cum-mean-per-unit estimators for a finite population mean in a simple random sample without replacement when the magnitude of the correlation coefficient is not very high (less than or equal to 0.7). The first order large sample approximation to the bias and the mean square error of our proposed estimators are obtained. We use simulation to compare our estimators with the well-known sample mean, ratio, and product estimators, as well as the classical linear regression estimator for efficient use of auxiliary information. The results are conforming to our motivating aim behind our proposition.

scholarly journals Estimation of Mean with Two-Parameter Ratio-Product-Ratio Estimator in Double Sampling using Ancillary Information under Non-Response

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Surya K. Pal ◽  
Housila P. Singh
Keyword(s):  
Double Sampling ◽  
Ratio Estimator ◽  
Product Ratio ◽  
Parameter Ratio ◽  
Ratio Estimators ◽  
Ancillary Information ◽  
Two Parameters ◽  
Two Parameter

Ratio-product-ratio estimators with two parameters in double sampling under non-response are considered along with their properties. Practical conditions are obtained in which the suggested estimators are more proficient than other existing estimators. An example is given.

scholarly journals Some Improved Estimators of Population Mean Under Systematic Sampling

2021 ◽  
Vol 3 (1) ◽  
pp. 15-27
Author(s):  
Shagufta Mehnaz ◽  
Shakeel Ahmed
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Information ◽  
Degree Of Approximation ◽  
Systematic Sampling ◽  
Population Parameters ◽  
Auxiliary Variables ◽  
Population Mean ◽  
Sampling Schemes ◽  
Squared Error ◽  
Improved Estimators

Auxiliary information is very important in constructing estimators for the population parameters for increasing the efficiency different sampling schemes. In this paper, we consider the problem of estimation of population mean using information on auxiliary variables in systematic sampling. We derive the expressions for the bias and mean squared error (MSE) of the suggested estimators up to the 1st degree of approximation. Proposed estimators are compared with usual mean estimator in systematic sampling scheme theoretically as well as empirically.

A Class of Estimators of the Population Mean Using Multi­Auxiliary Information

1983 ◽  
Vol 32 (1-2) ◽  
pp. 47-56 ◽  
Author(s):  
S. K. Srivastava ◽  
H. S. Jhajj
Keyword(s):  
Mean Squared Error ◽  
Broad Class ◽  
Auxiliary Variable ◽  
Population Parameters ◽  
Auxiliary Variables ◽  
Sample Mean ◽  
Squared Error ◽  
Class Of Estimators ◽  
The Mean ◽  
Asymptotic Mean Squared Error

For estimating the mean of a finite population, Srivastava and Jhajj (1981) defined a broad class of estimators which we information of the sample mean as well as the sample variance of an auxiliary variable. In this paper we extend this class of estimators to the case when such information on p(> 1) auxiliary variables is available. The estimators of the class involve unknown constants whose optimum values depend on unknown population parameters. When these population parameters are replaced by their consistent estimates, the resulting estimators are shown to have the same asymptotic mean squared error. An expression by which the mean squared error of such estimators is smaller than those which use only the population means of the auxiliary variables, is obtained.

scholarly journals A Family of Difference-Cum-Exponential Type Estimators for Estimating the Population Variance Using Auxiliary Information in Sample Surveys

2014 ◽  
Vol 1 ◽  
pp. 15-21
Author(s):  
H.S. Jhajj ◽  
Kusam Lata
Keyword(s):  
Linear Regression ◽  
Exponential Type ◽  
Random Sampling ◽  
Sampling Design ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Double Sampling ◽  
Population Variance ◽  
Squared Error

Using auxiliary information, a family of difference-cum-exponential type estimators for estimating the population variance of variable under study have been proposed under double sampling design. Expressions for bias, mean squared error and its minimum values have been obtained. The comparisons have been made with the regression-type estimator by using simple random sampling at both occasions in double sampling design. It has also been shown that better estimators can be obtained from the proposed family of estimators which are more efficient than the linear regression type estimator. Results have also been illustrated numerically as well asgraphically.

scholarly journals Modified Robust Ridge M-Estimators in Two-Parameter Ridge Regression Model

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Seyab Yasin ◽  
Sultan Salem ◽  
Hamdi Ayed ◽  
Shahid Kamal ◽  
Muhammad Suhail ◽  
...  
Keyword(s):  
Ridge Regression ◽  
Mean Squared Error ◽  
Least Square ◽  
Ridge Estimator ◽  
Least Square Estimator ◽  
Squared Error ◽  
Joint Problem ◽  
Ridge Regression Estimator ◽  
Simulation Results ◽  
Two Parameter

The methods of two-parameter ridge and ordinary ridge regression are very sensitive to the presence of the joint problem of multicollinearity and outliers in the y-direction. To overcome this problem, modified robust ridge M-estimators are proposed. The new estimators are then compared with the existing ones by means of extensive Monte Carlo simulations. According to mean squared error (MSE) criterion, the new estimators outperform the least square estimator, ridge regression estimator, and two-parameter ridge estimator in many considered scenarios. Two numerical examples are also presented to illustrate the simulation results.

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