Robust likelihood inferences for multivariate correlated data

Abstract

Multivariate normal, due to its well-established theories, is commonly utilized to analyze correlated data of various types. However, the validity of the resultant inference is, more often than not, erroneous if the model assumption fails. We present a modification for making the multivariate normal likelihood acclimatize itself to general correlated data. The modified likelihood is asymptotically legitimate for any true underlying joint distributions so long as they have finite second moments. One can, hence, acquire full likelihood inference without knowing the true random mechanisms underlying the data. Simulations and real data analysis are provided to demonstrate the merit of our proposed parametric robust method.

Keywords:

• robust likelihood
• correlated data
• multivariate normal

Acknowledgements

This work is supported by grant NSC 98-2118-M-008-002-MY2 of National Science Council, National Central University-Cathay General Hospital Joint Research grant 98CGH-NCU-A1 and Landseed Hospital-National Central University Research grant 99LSH-NCU-5 Taiwan, ROC.