Bayesian Estimation of Change Point Of Exponentiated Inverted Weibull Distribution under Precautionary Loss Function
uma srivastava, harish kumar
Exponentiated Inverted Weibull Distribution, Precautionary Loss Function, Bayesian Estimation, R-simulation.
Quick detection of common changes is critical in sequential monitoring of multistream data where a common change is a change that only occurs in a portion of panels. After a common change is detected by using a combined cumulative sum (CUSUM) procedure, we first study the joint distribution for values of the CUSUM process and the estimated delay detection time for the unchanged panels. Change-points divide statistical models into homogeneous segments. Inference about change-points is discussed in many researches in the context of testing the hypothesis of 'no change', point and interval estimation of a change-point, changes in nonparametric models, changes in regression, and detection of change in distribution of sequentially observed data. In this paper we consider the problem of single change-point estimation in the mean of a Exponentiated Inverted Weibull Distribution under Precautionary Loss Function. We propose a robust estimator of parameter. Then, we propose to follow the classical inference approach, by plugging this estimator in the criteria used for change-points estimation. We show that the asymptotic properties of these estimators are the same as those of the classical estimators in the independent framework. This method is implemented in the R package for Comprehensive numerical study. This package is used in the simulation section in which we show that for finite sample sizes taking into account the dependence structure improves the statistical performance of the change-point estimators and of the selection criterion.
Article Details
Unique Paper ID: 157037

Publication Volume & Issue: Volume 9, Issue 5

Page(s): 810 - 0
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