Sequential testing and change detection algorythms

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I propose to do research in the areas of Sequential Statistical Algorithms and Change-Point Analysis.1. Sequential Testing is necessary when observations come in one by one over a period of time. Monitoring is ongoing to detect change in the data generating mechanism, such as change in climate, manufacturing quality, etc. In my earlier work new type of algorithms have been defined for the independent observations case. These are superior to existing methods as, although optimal for minimizing the reaction time after change had occurred, those have unacceptable type I error. This is the reason why many sequential testing methods remained mostly in the theoretical domain, and practitioners have not adapted them widely. The new algorithms have tight control over type I errors, easy to perform, and pay for this with only slightly increased delay time in raising alarm (declaring that the data generating mechanism has changed). Having solved this problem for independent observations other data structures have to be considered as the independence assumptions are often violated in practice. Work is ongoing and expanding for different time series observations that are arising in practice.  Data structures that arise in clinical trials are of special interest. The implementation of sequential monitoring for them is based on solving theoretically challenging problems.2. Change-Point Detection: In these algorithms we look at the data retrospectively to see if any disturbance has occurred. Such methods are essential, as if there was a structural change in the data generating procedure, and it was not detected, then all model fitting and forecast are erroneous. Potential and existing applications of these methods are in environmental data analysis, econometrics, biostatistics, etc.