Details of Research Outputs

This paper reviews Prof. Kai-Tai Fang’s major contribution to multivariate statistics in three aspects: generalized multivariate statistics; general symmetric multivariate distributions; growth curve models and miscellaneous fields. Generalized multivariate statistics is a large extension of traditional statistics with normal assumption. It aims to generalize the traditional statistical methodologies like parametric estimation, hypothesis testing, and modeling to a much wider family of multivariate distributions, which is called elliptically contoured distributions (ECD). General symmetric multivariate distributions form an even wider class of multivariate probability distributions that includes the ECD as its special case. Growth curve models (GCM) includes statistical methods that allow for consideration of inter-individual variability in intra-individual patterns of change over time. Outlier detection and identification of influential observations are important topics in the area of the GCM. Miscellaneous fields cover major contributions that Prof. Fang made in various areas of multivariate statistics beyond the three aspects mentioned above.