Moments and Cumulants

  • Paul Doukhan
Part of the Mathématiques et Applications book series (MATHAPPLIC, volume 80)


This chapter is devoted to moment methods. The use of moments relies on their importance in deriving asymptotic of several estimators, based on moments and limit distributions. Cumulants are linked with spectral or multispectral estimation which are main tools of time series analysis.
$$g(\lambda )=\sum _{k=-\infty }^\infty \mathrm {Cov}\,(X_0,X_k)e^{-ik\lambda }.$$
Such functions do not characterize the dependence of non-linear processes; indeed we have already examples of orthogonal and non-independent sequences. This motivates the introduction of higher order characteristics. A multispectral density is defined over \(\mathbb {C}^{p-1}\) by
$$g(\lambda _2,\ldots ,\lambda _p)=\sum _{k_2=-\infty }^\infty \!\!\cdots \!\!\sum _{k_p=-\infty }^\infty \kappa (X_0,X_{k_2},\ldots , X_{k_p}) e^{-i(k_2\lambda _2+\cdots +k_p\lambda _p)}.$$

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of MathematicsUniversity Cergy-PontoiseCergy-PontoiseFrance

Personalised recommendations