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Efficient Estimation for Rank‐Based Regression with Clustered Data
Author(s) -
Fu Liya,
Wang YouGan
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2012.01760.x
Subject(s) - heteroscedasticity , outlier , robustness (evolution) , computer science , rank (graph theory) , inference , variance (accounting) , regression , statistics , data mining , robust regression , mathematics , artificial intelligence , machine learning , biochemistry , chemistry , accounting , combinatorics , business , gene
Summary Rank‐based inference is widely used because of its robustness. This article provides optimal rank‐based estimating functions in analysis of clustered data with random cluster effects. The extensive simulation studies carried out to evaluate the performance of the proposed method demonstrate that it is robust to outliers and is highly efficient given the existence of strong cluster correlations. The performance of the proposed method is satisfactory even when the correlation structure is misspecified, or when heteroscedasticity in variance is present. Finally, a real dataset is analyzed for illustration.