Assessing the Effect of Nonresponse and Measurement Error Using a Novel Class of Efficient Estimators

Abstract

The purpose of any statistical analysis is to uncover the trend using real situations and provide an accurate model for future policies. In order to fulfill this aim, it is important to collect and use the data carefully and minimize the possibility of various types of errors. Nonresponse (NR) and measurement error (ME) are the two major types of nonsampling error that occur in almost every survey. We propose an estimator of the population mean and study its efficacy in the combined and separate effects of NR and ME. Some existing estimators are also considered for comparison. A simulation study was performed to see its efficiency numerically.

Impact of Correlated Measurement Errors on Some Efficient Classes of Estimators

“…Ahmad et al [13] discussed the estimation of the fnite population mean using a dual supplementary variable for nonresponse under SRS. Tiwari et al [14] suggested a novel class of efcient estimators to assess the impact of nonresponse and ME. Tiwari and Sharma [15] developed an efcient estimation procedure of the population mean under the joint infuence of nonresponse and ME.…”

Using ORRT Models for Mean Estimation under Nonresponse and Measurement Errors in Stratified Successive Sampling

“…Furthermore, Zahid et al [18] have also been proposed a generalized class of estimators for a sensitive variable in the presence of nonresponse and measurement errors under stratifed sampling. Furthermore, Tiwari et al [19,20], Kumar and Kour [21], and others have addressed the issue of nonresponse and measurement error in various sampling strategies, using prior studies as inspiration and realizing the essence of how to handle these faws in a sample survey. Furthermore, the exponential estimator(s) proposed by Bahl and Tuteja [22] are known to perform better than the corresponding usual ratio and product-type estimators under certain efciency conditions.…”