Efficient Wald-Type Estimators for Simple Linear Measurement Error Model
Abstract
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In this paper, Wald-type estimators for simple linear measurement error model presented based on L Ranked Set Sampling (LRSS) technique. The proposed estimators are compared to their counterparts based on Simple Random Sampling (SRS) and Ranked Set Sampling (RSS). It appears that the suggested estimators are more efficient. A real data set of student achievements is studied, a simulated data also is used to show how much efficient the use of the ranked data to estimate the EIV model parameter.
In this paper, Wald-type estimators for simple linear measurement error model presented based on L Ranked Set Sampling (LRSS) technique. The proposed estimators are compared to their counterparts based on Simple Random Sampling (SRS) and Ranked Set Sampling (RSS). It appears that the suggested estimators are more efficient. A real data set of student achievements is studied, a simulated data also is used to show how much efficient the use of the ranked data to estimate the EIV model parameter.
DOI Code:
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Keywords:
Errors–in-Variables; Grouping Methods; Ranked Set Sampling; L Ranked Set Sampling; Simple Random Sampling
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