2006.35: Higher Order Cumulants of Random Vectors and Applications to Statistical Inference and Time Series
2006.35: S Rao Jammalamadaka, T Subba Rao and Gyorgy Terdik (2006) Higher Order Cumulants of Random Vectors and Applications to Statistical Inference and Time Series.
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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 2000 > 60 Probability theory and stochastic processes|
MSC 2000 > 62 Statistics
|Deposited By:||Dr Peter Neal|
|Deposited On:||21 March 2006|