Comparison of Least Square Estimators with Rank Regression Estimators of Weibull Distribution - A Simulation Study

Clicks: 3
ID: 316223
2013
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #143 of 182 articles by views in Journal of Statistics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 182 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
We estimated the parameters of two-parameter Weibull Distribution with the Least Square method from an optimally constructed grouped sample and compared the efficiencies of these estimators with the parameters estimated with Rank Regression method from an ungrouped (complete) sample, though the estimators from both the methods are in closed form, we resorted to Monte-Carlo simulation for computing Bias, Variance and Mean Square Error. The Least Square Estimation method for optimally grouped sample will give an efficient shape parameter than the Rank Regression Estimation method for complete sample. A numerical example is presented to illustrate the methods proposed here.
Reference Key
imported_1780934263_6a26e6779f55d Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chandika Rama Mohan, AnantasettyVasudevaRao, Gollapudi Venkata Sita Rama Anjaneyulu
Journal Journal of Statistics
Year 2013
DOI
DOI not found
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.