Recent advances in estimation techniques have underscored the growing importance of shrinkage estimation and balanced loss functions in the analysis of multivariate normal distributions. These ...
Sankhyā: The Indian Journal of Statistics, Series B (2008-), Vol. 82, No. 1 (May 2020), pp. 34-69 (36 pages) Existing methods for estimating the parameters of the Growth Curve Model (GCM) rely on the ...
Sankhyā: The Indian Journal of Statistics, Series B (1960-2002), Vol. 51, No. 3 (Dec., 1989), pp. 425-428 (4 pages) A two-stage analogue of the chi-square test for the mean of a multivariate normal ...
In many applications, the manifest variables are not even approximately multivariate normal. If this happens to be the case with your data set, the default generalized least-squares and maximum ...
There are no completely satisfactory methods for determining the number of population clusters for any type of cluster analysis (Everitt 1979; Hartigan 1985; Bock 1985). It is always a good idea to ...
This course is available on the MPhil/PhD in Statistics, MSc in Data Science, MSc in Health Data Science, MSc in Marketing, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in ...
This paper presents a discrete random-field model for forward prices driven by the multivariate normal inverse Gaussian distribution. The model captures the idiosyncratic risk and adequately addresses ...
Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variable under consideration. Multivariate analysis techniques may be used for several ...
This course is available on the MPhil/PhD in Statistics, MSc in Data Science, MSc in Health Data Science, MSc in Marketing, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in ...
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