PARAMETER ESTIMATION IN MULTIPLE LINEAR REGRESSION MODELS USING RANKED SET SAMPLING | Zendy

Yaprak Arzu Özdemir | Zendy; Alptekin Esin | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

PARAMETER ESTIMATION IN MULTIPLE LINEAR REGRESSION MODELS USING RANKED SET SAMPLING

Author(s) -

Yaprak Arzu Özdemir,

Alptekin Esin

Publication year - 1999

Publication title -

communications faculty of science university of ankara series a1mathematics and statistics

Language(s) - English

Resource type - Journals

ISSN - 1303-5991

DOI - 10.1501/commua1_0000000194

Subject(s) - rss , estimator , statistics , sampling (signal processing) , simple random sample , mathematics , linear regression , monte carlo method , regression analysis , sample size determination , population , computer science , demography , filter (signal processing) , sociology , computer vision , operating system

In statistical surveys, if the measurements of sampling units according to the variable under consideration is expensive in all sense, and if itis possible to rank sampling units according to the same variable by means ofa method which is not expensive at all, in those cases, Ranked Set Sampling(RSS) is a more e¢ cient sampling method than the Simple Random Sampling(SRS) to estimate the population mean. In this study, the egects of using RSSin multiple linear regression analysis are considered in terms of estimation ofmodel parameters. Firstly, according to RSS and SRS the estimates of multipleregression model parameters are obtained and then the egects concerning thevariances of the estimators are investigated by Monte Carlo simulation studybased on Relative E¢ ciency (RE) measure. It is shown that the estimatorsobtained based on RSS are more e¢ cient than the estimators based on SRSwhen the sample size is small

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research