Investigating the performance of a family of exponential-type estimators in presence of measurement error

The measurement error (ME), one of the serious kinds of non sampling errors, is the difference between the value of a characteristic provided by the respondent and the true value of it. Generally, in practice it is almost impossible to get the true value due to non availability of an appropriate and feasible method of measuring it. In fact, the assumption of getting information from a unit without any biases or measuring a unit correctly is very unrealistic. ME may give rise to serious bias and variable errors both in an estimator obtained through a survey. This work is mainly concentrated on the study of variations that may occur in certain components of ME models considered and their impact on some existing usual estimators of population mean along with a proposed family of exponential-type estimators which might be considered as an alternative to classical ratio/product estimators in some sense. Some important features of these estimators in presence of ME have been dealt with. Results discussed have been highlighted with the help of an empirical data which reflects the extent of consequences of changes in model parameters on the performance of estimators.