This paper utilizes a change-point estimator based on <span>the </span><span style="font-style:italic;">φ</span><span>-</span><span>divergence. Since </span>&...This paper utilizes a change-point estimator based on <span>the </span><span style="font-style:italic;">φ</span><span>-</span><span>divergence. Since </span><span "=""><span>we seek a </span><span>near perfect</span><span> translation to reality, then locations of parameter change within a finite set of data have to be accounted for since the assumption of </span><span>stationary</span><span> model is too restrictive especially for long time series. The estimator is shown to be consistent through asymptotic theory and finally proven through simulations. The estimator is applied to the generalized Pareto distribution to estimate changes in the scale and shape parameters.</span></span>展开更多
The assumption of stationarity is too restrictive especially for long time series. This paper studies the change point problem through a change point estimator based on the <span style="color:#4F4F4F;font-fami...The assumption of stationarity is too restrictive especially for long time series. This paper studies the change point problem through a change point estimator based on the <span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">φ</span><span><span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">-divergence which provides a rich set of distance like measures between pairs of distributions. The change point problem is considered in the following sub-fields: the problem of divergence estimation, testing for the homogeneity between two samples as well as estimating the time of change. The asymptotic distribution of the change point estimator is estimated by the limiting distribution of a stochastic process within given bounds through asymptotic theory surrounding the likelihood theory. The distribution is found to converge to that of a standardized Brownian bridge process.</span></span></span>展开更多
文摘This paper utilizes a change-point estimator based on <span>the </span><span style="font-style:italic;">φ</span><span>-</span><span>divergence. Since </span><span "=""><span>we seek a </span><span>near perfect</span><span> translation to reality, then locations of parameter change within a finite set of data have to be accounted for since the assumption of </span><span>stationary</span><span> model is too restrictive especially for long time series. The estimator is shown to be consistent through asymptotic theory and finally proven through simulations. The estimator is applied to the generalized Pareto distribution to estimate changes in the scale and shape parameters.</span></span>
文摘The assumption of stationarity is too restrictive especially for long time series. This paper studies the change point problem through a change point estimator based on the <span style="color:#4F4F4F;font-family:Simsun;font-size:14px;white-space:normal;background-color:#FFFFFF;">φ</span><span><span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">-divergence which provides a rich set of distance like measures between pairs of distributions. The change point problem is considered in the following sub-fields: the problem of divergence estimation, testing for the homogeneity between two samples as well as estimating the time of change. The asymptotic distribution of the change point estimator is estimated by the limiting distribution of a stochastic process within given bounds through asymptotic theory surrounding the likelihood theory. The distribution is found to converge to that of a standardized Brownian bridge process.</span></span></span>
