Doctor of Philosophy (PhD)
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Fifth Committee Member
Number of Pages
Point processes often serve as a natural language to chronicle an event's temporal evolution, and significant changes in the flow, synonymous with non-stationarity, are usually triggered by assignable and frequently preventable causes, often heralding devastating ramifications. Examples include amplified restlessness of a volcano, increased frequencies of airplane crashes, hurricanes, mining mishaps, among others. Guessing these time points of changes, therefore, merits utmost care. Switching the way time traditionally propagates, we posit a new genre of bidirectional tests which, despite a frugal construct, prove to be exceedingly efficient in culling out non-stationarity under a wide spectrum of environments. A journey surveying a lavish class of intensities, ranging from the tralatitious power laws to the deucedly germane rough steps, tracks the established unidirectional forward and backward test's evolution into a p-value induced dual bidirectional test, the best member of the proffered category. Niched within a hospitable Poissonian framework, this dissertation, through a prudent harnessing of the bidirectional category's classification prowess, incites a refreshing alternative to estimating changes plaguing a soporific flow, by conducting a sequence of tests. Validation tools, predominantly graphical, rid the structure of forbidding technicalities, aggrandizing the swath of applicability. Extensive simulations, conducted especially under hostile premises of hard non-stationarity detection, document minimal estimation error and reveal the algorithm's obstinate versatility at its most unerring.
bi-directional tests; change point identification; empirical recurrence rates and ratios; point processes; repairable systems; step intensities
Industrial Engineering | Industrial Technology | Mathematics | Statistics and Probability
University of Nevada, Las Vegas
Bhaduri, Moinak, "Bi-Directional Testing for Change Point Detection in Poisson Processes" (2018). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3217.
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