Higher Order Cumulants of Random Vectors and Applications to Statistical Inference and Time Series

Rao Jammalamadaka, S and Subba Rao, T and Terdik, Gyorgy (2006) Higher Order Cumulants of Random Vectors and Applications to Statistical Inference and Time Series. [MIMS Preprint]

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Abstract

This paper provides a unified and comprehensive approach that is useful in deriving expressions for higher order cumulants of random vectors. The use of this methodology is then illustrated in three diverse and novel contexts, namely: (i) in obtaining a lower bound (Bhattacharya bound) for the variance- covariance matrix of a vector of unbiased estimators where the density depends on several parameters, (ii) in studying the asymptotic theory of multivariable statistics when the population is not necessarily Gaussian and finally, (iii) in the study of multivariate nonlinear time series models and in obtaining higher order cumulant spectra. The approach depends on expanding the characteristic functions and cumulant generating functions in terms of the Kronecker products of di¤erential operators. Our objective here is to derive such expressions using only elementary calculus of several variables and also to highlight some important applications in statistics.

Item Type: MIMS Preprint
Uncontrolled Keywords: Cumulants for multivariate variable bound, Taylor series expansion, Multivariate time series
Subjects: MSC 2010, the AMS's Mathematics Subject Classification > 60 Probability theory and stochastic processes
MSC 2010, the AMS's Mathematics Subject Classification > 62 Statistics
Depositing User: Dr Peter Neal
Date Deposited: 21 Mar 2006
Last Modified: 08 Nov 2017 18:18
URI: http://eprints.maths.manchester.ac.uk/id/eprint/188

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