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NONPARAMETRIC ESTIMATORS FOR TIME SERIES
Author(s) -
Robinson P. M.
Publication year - 1983
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1983.tb00368.x
Subject(s) - mathematics , estimator , series (stratigraphy) , multivariate statistics , univariate , multivariate kernel density estimation , conditional probability distribution , nonparametric statistics , kernel density estimation , conditional expectation , statistics , mixing (physics) , joint probability distribution , kernel method , variable kernel density estimation , paleontology , physics , quantum mechanics , artificial intelligence , computer science , support vector machine , biology
. Kernel multivariate probability density and regression estimators are applied to a univariate strictly stationary time series X r We consider estimators of the joint probability density of X t at different t ‐values, of conditional probability densities, and of the conditional expectation of functionals of X v given past behaviour. The methods seem of particular relevance in light of recent interest in non‐Gaussian time series models. Under a strong mixing condition multivariate central limit theorems for estimators at distinct points are established, the asymptotic distributions being of the same nature as those which would derive from independent multivariate observations.