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Showing 3 results for Moving Average
Mehrnaz Mohammadpour, Fereshte Rezanezhad ,
Volume 16, Issue 2 (3-2012)
Abstract
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 a new tool for order identification of stable moving average processes. Based on the empirical characteristic function, we propose a consistent estimator of the codifference function. In addition, we derive the limiting distribution. Finally, simulation study shows the method is good.
Razieh Dehghanian, Rahim Chinipardaz, Behzad Mansouri,
Volume 18, Issue 2 (3-2014)
Abstract
Classical methods in discrimination such as linear and quadratic do not have good efficiency in the case of nongaussian or nonlinear time series data. In nonparametric kernel discrimination in which the kernel estimators of likelihood functions are used instead of their real values has been shown to have good performance. The misclassification rate of kernel discrimination is usually less than linear and quadratic methods because of its flexibility. However, the kernel estimates are depend on the bandwidth. This paper is concerned with the selection of bandwidth parameter to achieve an optimal discrimination with minimum rate misclassification. The methods obtained bandwidth examined via a simulation study.
Atefe Mokhtari Hasanabadi, Manouchehr Kheradmandnia,
Volume 18, Issue 2 (3-2014)
Abstract
Recently several
control charts have been introduced in the statistical process control literature which are based on the idea of Bayesian Predictive Density
(BPD). Among these charts is the
variation control chart which we refer to it as VBPD chart. In this paper we add the idea of Moving Average to VBPD
chart and introduce a new variation control chart which has all advantages of the original VBPD
chart and in addition has a new advantage which is its sensitivity to small changes in process variance. We
refer to this new chart as MAVBPD chart. In both VBPD and MAVBP charts , the parameters are assumed unknown
but the control statistic has a known F distribution which means that, the
control limits can be obtained without
simulation.
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