AbstractIn this study we consider the problem where two variables can have missing values not necessarily for the same units. We propose a class of imputed estimators by means of a ratio technique with random disturbance. Next we derive the asymptotically optimal estimator into the class. The simulation study shows that the proposed method produces an important increase in efficiency when parameters such as the median, quartiles and variance are estimated
Existence of missing values creates a big problem in real world data. Unless those values are missi...
The problem of imputing missing observations under the linear regression model is considered. It is ...
Missing data are an important practical problem in many applications of statistics, including social...
AbstractIn this study we consider the problem where two variables can have missing values not necess...
In this paper, the problem of estimation of variance has been considered when the missing data have ...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
AbstractThe problem of imputing missing observations under the linear regression model is considered...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
Abstract In multiple imputation, the resulting estimates are consistent if the im-putation model is ...
Ratio and regression type estimators have been used by previous authors to estimate a population mea...
For multivariate datasets with missing values, we present a procedure of statistical inference and s...
We propose a variance estimator based on factor type imputation in the presence of non-response. Pro...
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...
Existence of missing values creates a big problem in real world data. Unless those values are missi...
The problem of imputing missing observations under the linear regression model is considered. It is ...
Missing data are an important practical problem in many applications of statistics, including social...
AbstractIn this study we consider the problem where two variables can have missing values not necess...
In this paper, the problem of estimation of variance has been considered when the missing data have ...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
AbstractThe problem of imputing missing observations under the linear regression model is considered...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
Abstract In multiple imputation, the resulting estimates are consistent if the im-putation model is ...
Ratio and regression type estimators have been used by previous authors to estimate a population mea...
For multivariate datasets with missing values, we present a procedure of statistical inference and s...
We propose a variance estimator based on factor type imputation in the presence of non-response. Pro...
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...
Existence of missing values creates a big problem in real world data. Unless those values are missi...
The problem of imputing missing observations under the linear regression model is considered. It is ...
Missing data are an important practical problem in many applications of statistics, including social...
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