Robust likelihood inferences for multivariate correlated data
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.
Year of publication: |
2011
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Authors: | Chen, Chien-Hung ; Tsou, Tsung-Shan |
Published in: |
Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 38.2011, 12, p. 2901-2910
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Publisher: |
Taylor & Francis Journals |
Saved in:
Online Resource
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