Abstract: We propose new tests to detect a change in the mean of a time series. Like many existing tests, the new ones are based on the CUSUM process. Existing CUSUM tests require an estimator of a scale parameter to make them asymptotically distribution free under the no change null hypothesis. Even if the observations are independent, the estimation of the scale parameter is not simple since ...

A method for affine rectification of a plane exploiting knowledge of relative scale changes is presented. The rectifying transformation is fully specified by the relative scale change at three non-collinear points or by two pairs of points where the relative scale change is known; the relative scale change between the pairs is not required. The method also allows homography estimation between t...

Assuming a first-order auto-regressive model for the auto-correlation structure between observations, in this paper, a transformation method is first employed to eliminate the effect of auto-correlation. Then, a maximum likelihood estimator (MLE) of a step change in the parameters of the transformed model is derived and three separate EWMA control charts are used to monitor the parameters of th...

We present a novel method of velocity eld estimation for the points on moving contours in a 2-D image sequence. The method determines the corresponding point in a next image frame by minimizing the curvature change of a given contour point. As a rst step, snakes are used to locate smooth curves in 2-D imagery. Thereafter, the extracted curves are tracked continuously computing the corresponding...

ASYMPTOTICS AND CONFIDENCE ESTIMATION IN SEGMENTED REGRESSION MODELS Rebekah Ann Robinson May 11, 2012 Standard regularity assumptions for regression models are not satisfied in segmented regression models with an unknown change point, and consequently standard asymptotic results and inferential methods for confidence estimation are not applicable. This dissertation considers a clustered segmen...

Considering a Multi-Objective Optimization problem for a Dynamic System, we propose a controller that drives this system into a point on the Pareto front.At such a point, no change in any control variable can be made without deteriorating one of the objectives. Gradient Estimators are the core of this controller. A sinusoidal Perturbation based gradient estimation approach is adopted.

A setting of the change-point estimation problem with a “smooth” change is investigated. A special class of such models, where the change is defined as a gradual shift in the parameter, is considered. We show that these models can be analyzed via explicitly given procedures. It is demonstrated that M -estimators can consistently estimate the change in the parameter structure as well as the addi...

The paper considers generalized maximum likelihood asymptotic power one tests which aim to detect a change point in logistic regression when the alternative specifies that a change occurred in parameters of the model. A guaranteed nonasymptotic upper bound for the significance level of each of the tests is presented. For cases in which the test supports the conclusion that there was a change po...

We consider a density estimation problem with a change-point. We develop an adaptive wavelet estimator constructed from a block thresholding rule. Adopting the minimax point of view under the Lp risk (with p ≥ 1) over Besov balls, we prove that it is near optimal.