Statistical Analysis of Stochastic Processes in Time

Author(s):  
J. K. Lindsey
2001 ◽  
Vol 04 (03) ◽  
pp. 511-534 ◽  
Author(s):  
ENRICO CAPOBIANCO

We study high frequency Nikkei stock index series and investigate what certain wavelet transforms suggest in terms of volatility features underlying the observed returns process. Several wavelet transforms are applied for exploratory data analysis. One of the scopes is to use wavelets as a pre-processing smoothing tool so to de-noise the data; we believe that this procedure may help in identifying, estimating and predicting the latent volatility. Evidence is shown on how a non-parametric statistical procedure such as wavelets may be useful for improving the generalization power of GARCH models when applied to de-noised returns.


2016 ◽  
Vol 61 (2) ◽  
pp. 753-760 ◽  
Author(s):  
A. Zieliński ◽  
M. Sroka ◽  
M. Miczka ◽  
A. Śliwa

AbstractThe investigations on microstructure of P92 steel in the as-received condition and after 105h ageing at 600 and 650 °C were carried out. For the recorded images of microstructure, the quantitative analysis of precipitates was performed. On that basis, a statistical analysis of collected data was made with the aim of estimating parameters of selected theoretical statistical distribution. Then, the forecast for average precipitate diameter and standard deviation of such a distribution for the time of 1,5*105h at 600 and 650 °C was calculated. The obtained results of investigations confirm the possibility of using, in evaluation of degradation degree for materials in use, the forecasting methods derived from mathematical statistics, in particular the theory of stochastic processes and methods of forecasting by analogy.


Technometrics ◽  
2005 ◽  
Vol 47 (3) ◽  
pp. 373-374
Author(s):  
B. D McCullough

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