Outliers In Designed Experiments I-Classical Robust & Resistant Methods Ahmed

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ID: 316295
2004
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Abstract
The robust regression analysis and the designed experiments era two of the fastest growing fields in contemporary Statistics. There has been very little overlap between these fields. In designed experiments, designs were contrived for the efficient use of the least square estimates to maximize response, while in robust regression analysis, robust alternatives to the non-robust conventional least square estimates were developed. This paper, the first in the series of three, is an attempt to bridge the gap. It discusses classical robust, like el •stimators using a iti function developed by Huber, Hempel, & Tukey, and resistant estimators Of these for¬regression-analysis techniques to deal with outliers in the domain of designed experiments. Further, a Monte-Carlo simulation is carried out to appraise the efficiency of these methods in designed experiments and then a factorial experiment, with possible outliers, is reanalyzed. It is revealed that theee techniques, with some precautions and modifications, work excellent in designed experiments.
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imported_1780934489_6a26e759793ad Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ahmed F. Siddiqi
Journal Journal of Statistics
Year 2004
DOI
DOI not found
URL
https://jstat.gcu.edu.pk/index.php/jstat/article/view/170
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