In this paper, we consider the problem of missing complete at random (MCAR) values in two phase probability proportional to size (pps) sampling for the estimation of population mean. A class of estimators is considered by the suitable use of auxiliary information with the traditional estimators for imputing the missing values. Theoretically, bias and mean squared errors of the proposed estimators are obtained up to the first order approximation. Two numerical studies are carried out for relative comparison of the proposed estimators with mean estimator under two phase pps sampling for each situation.WoSScopu
Missing values in sample survey can lead to biased estimation if not treated. Imputation was posted ...
Imputed values in surveys are often generated under the assumption that the sampling mechanism is no...
Missing data is an unavoidable issue when performing data analysis. If the missing probability is re...
Non-response is an unavoidable feature in sample surveys and it needs to be carefully handled to avo...
The present article offers more efficient imputation based estimators of the population mean under t...
The treatment of incomplete data is an important step in statistical data analysis of most survey da...
Some imputation techniques are suggested for estimating the population mean when the data values are...
In this paper, we have proposed a class of exponential dual to ratio type compromised imputation tec...
Multiple imputation is a commonly used approach to deal with missing values. In this approach, an im...
In the present paper, a new and improved method of ratio type imputation and corresponding point est...
This paper aims to deal with the problem of non-response, by suggesting an exponential chain type cl...
Missing observations due to non-response are commonly encountered in data collected from sample surv...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
In this paper, the problem of estimation of variance has been considered when the missing data have ...
In this paper we have proposed an almost unbiased estimator using known value of some population par...
Missing values in sample survey can lead to biased estimation if not treated. Imputation was posted ...
Imputed values in surveys are often generated under the assumption that the sampling mechanism is no...
Missing data is an unavoidable issue when performing data analysis. If the missing probability is re...
Non-response is an unavoidable feature in sample surveys and it needs to be carefully handled to avo...
The present article offers more efficient imputation based estimators of the population mean under t...
The treatment of incomplete data is an important step in statistical data analysis of most survey da...
Some imputation techniques are suggested for estimating the population mean when the data values are...
In this paper, we have proposed a class of exponential dual to ratio type compromised imputation tec...
Multiple imputation is a commonly used approach to deal with missing values. In this approach, an im...
In the present paper, a new and improved method of ratio type imputation and corresponding point est...
This paper aims to deal with the problem of non-response, by suggesting an exponential chain type cl...
Missing observations due to non-response are commonly encountered in data collected from sample surv...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
In this paper, the problem of estimation of variance has been considered when the missing data have ...
In this paper we have proposed an almost unbiased estimator using known value of some population par...
Missing values in sample survey can lead to biased estimation if not treated. Imputation was posted ...
Imputed values in surveys are often generated under the assumption that the sampling mechanism is no...
Missing data is an unavoidable issue when performing data analysis. If the missing probability is re...