Spatial-temporal change point detection problem for dependent data

Advisors: Nader Ebrahimi.Committee members: Sanjib Basu; Bernard Harris; Duchwan Ryu.Includes bibliographical references.Includes illustrations.In the classical change detection problem, a sequence of independent and identically distributed observations is monitored and at an unknown time the distribution of the data changes in a certain way. The problem is to detect this change as quickly as possible. However, if the assumption of independence is removed the problem is more challenging than the classical change point detection. Especially, if dependence comes from two directions, namely space and time. Moreover, greatly increased availability of data with the presence of dependency over space and time adds further complication and requires thorough investigation. This non-traditional problem assumption provides abundant new research opportunities for finding more efficient detection schemes useful in explaining and understanding the underlying structure of this type of data by accounting for both spatial and temporal dependence among geographic locations. In this dissertation, we provide two competing methods for identifying a single change in dependent over time and space data. Parametric models from spatial data analysis are used to incorporate dependence.Ph.D. (Doctor of Philosophy