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高阶样本累积量谱泛函的极限行为AI 解读

This paper is concerned with establishing a broad class of estimators (and the limiting behavior, thereof) for parametrizations of higher than second-order structure. This includes parametrizations which reflect, for example, such properties as nonlinearity and/or non-Gaussianity and/or time irreversibility. Asymptotic distributions, almost-sure convergence, and probability-one bounds for such estimators are established. Several applications of such estimators are discussed.

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时间序列非线性的 Tukey 非可加性型检验AI 解读

The most general form of a nonlinear strictly stationary process is that referred to as a Volterra expansion; this is to a linear process what a polynomial is to a linear function. Because of this similarity, an analogue of Tukey's one degree of freedom for non-additivity test is constructed as a test for linearity versus a second-order Volterra expansion.

样本谱分布泛函的极限行为AI 解读

The parameters of a stationary process can be viewed as functions of the spectral distribution function. This work concerns (estimators) parameters defined as integrals of $m (\geq 1)$-dimensional kernel functions with respect to the (sample) spectral distribution function. Conditions for asymptotic normality, almost sure convergence, and probability one bounds are derived for such estimators. The approach taken is based upon the reduction of an $m$-dimensional problem to…

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