Author(s):
1. Sehar Saleem:
. College of Statistical and Actuarial Sciences, University of the Punjab,Lahore 5400-,Pakistan
2. Rehan Ahmad Khan Sherwani:
. College of Statistical and Actuarial Sciences, University of the Punjab,Lahore 5400-,Pakistan
Abstract:
Rank-based analysis of linear models is based on selecting an appropriate score function. The information about the shape of the underlying distribution is necessary for the optimal selection; leading towards asymptotically efficient analysis. In this study, we analyzed the multilevel model with cluster-correlated error terms following a family of skew-t distribution with the rank-based approach based on score function derived for the class of skewnormal distribution. The rank fit is compared with the Restricted Maximum Likelihood (REML) estimation in terms of validity and efficiency for different sample sizes. A Monte Carlo simulation study is carried out over skewed-t and contaminated-t distribution with a range of skewness parameter from moderately to highly skewed. The standard error of regression coefficients is significantly reduced in the rank-based approach and further reduces for a large sample size. Rank-based fit appeared asymptotically efficient than REML for each shape parameter of skewness in skew-t and contaminated-t distribution computed through a calculation of precision. The empirical validity of fixed effects is obtained up to the nominal level 0.95 in REML but not rank-based with skew-normal score function.
Page(s):
89-98
Published:
Journal: Pakistan Journal of Statistics and Operation Research, Volume: 17, Issue: 1, Year: 2021
Keywords:
REML
,
Multilevel Models
,
Skewt
,
Rankbased
,
Skewnormal
References:
References are not available for this document.
Citations
Citations are not available for this document.