Suppose our data {Xn} come from the model Xt = ∞ j = 0 cjZt-j, where {Zn} are i.i.d. with a symmetric distribution function which lies in the domain of normal attraction of a stable law with index ∈ (1; 2). Further we assume that cj = j d-1 L(j), where parameter d ∈ (0; 1 -1= ) and L is a normalized
Estimating the degree of time variance in a parametric model
✍ Scribed by Matias Waller; Henrik Saxén
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 162 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0005-1098
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✦ Synopsis
A method capable of directional considerations in tracking parameters which exhibit di!erent time-variant characteristics is suggested. The method, which can be considered an ad hoc modi"cation of the recursive Kalman "lter, is suited to estimate the parameters in a parametric model and, unlike the Kalman "lter, does not require any prior knowledge of the variations. The e$ciency of the proposed method is illustrated through simulated examples as well as by an application to a full-scale industrial problem.
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Estimations of the time-average variance for meteorological time series play a central role in climatic studies. They depend on the finite sample length and the correlation structure of the climatic time series. A general equation for these estimations is derived theoretically for autoregressive int