منابع مشابه
Prediction in moving average processes
For the stationary invertible moving average process of order one with unknown innovation distribution F , we construct root-n consistent plug-in estimators of conditional expectations E(h(Xn+1)|X1, . . . , Xn). More specifically, we give weak conditions under which such estimators admit Bahadur type representations, assuming some smoothness of h or of F . For fixed h it suffices that h is loca...
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The sample autocorrelation function (acf) of a stationary process has played a central statistical role in traditional time series analysis, where the assumption is made that the marginal distribution has a second moment. Now, the classical methods based on acf are not applicable in heavy tailed modeling. Using the codifference function as dependence measure for such processes be shown it be as...
متن کاملMoving Average Processes
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متن کاملChapter 3: Autoregressive and moving average processes
2 Moving average models Definition. The moving average model of order q, or MA(q), is defined to be Xt = t + θ1 t−1 + θ2 t−2 + · · ·+ θq t−q, where t i.i.d. ∼ N(0, σ). Remarks: 1. Without loss of generality, we assume the mean of the process to be zero. 2. Here θ1, . . . , θq (θq 6= 0) are the parameters of the model. 3. Sometimes it suffices to assume that t ∼WN(0, σ). Here we assume normality...
متن کاملLocalisable moving average stable and multistable processes
We study a particular class of moving average processes which possess a property called localisability. This means that, at any given point, they admit a “tangent process”, in a suitable sense. We give general conditions on the kernel g defining the moving average which ensures that the process is localisable and we characterize the nature of the associated tangent processes. Examples include t...
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ژورنال
عنوان ژورنال: Journal of Statistical Planning and Inference
سال: 2008
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2007.01.007