Score test in robust M-procedure

Khan, Shahjahan ORCID: https://orcid.org/0000-0002-0446-086X and Yunus, Rossita M. (2014) Score test in robust M-procedure. Journal of Applied Probability and Statistics, 9 (2). pp. 55-75. ISSN 1930-6792

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Abstract

A score type test based on the M-estimation method for a linear regression model is more reliable than the parametric based-test under mild departures from model assumptions, or when dataset has outliers. An R-function is developed for the score M-test, and applied to two real datasets to illustrate the procedure. The asymptotic power function of the M-test under a sequence of (contiguous) local alternatives is derived. Through computation of power function from simulated data, the M-test is compared with its alternatives, the Student's $t$ and Wilcoxon's rank tests. Graphical illustration of the asymptotic power of the M-test is provided for randomly generated data from the normal, Laplace, Cauchy, and logistic distributions.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: c. 2014 Islamic Countries Society of Statistical Sciences. Author version deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Date Deposited: 20 Mar 2015 01:52
Last Modified: 02 May 2017 23:56
Uncontrolled Keywords: robust inference, M-test, student's t test, rank test, asymptotic power, and contiguity
Fields of Research (2008): 01 Mathematical Sciences > 0104 Statistics > 010406 Stochastic Analysis and Modelling
01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory
01 Mathematical Sciences > 0102 Applied Mathematics > 010201 Approximation Theory and Asymptotic Methods
Fields of Research (2020): 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490510 Stochastic analysis and modelling
49 MATHEMATICAL SCIENCES > 4905 Statistics > 490509 Statistical theory
49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490101 Approximation theory and asymptotic methods
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
URI: http://eprints.usq.edu.au/id/eprint/26906

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