title: On the adaptive partition approach to the detection of multiple change-points.
abstract: With an adaptive partition procedure, we can partition a "time course" into consecutive non-overlapped intervals such that the population means/proportions of the observations in two adjacent intervals are significantly different at a given level . However, the widely used recursive combination or partition procedures do not guarantee a global optimization. We propose a modified dynamic programming algorithm to achieve a global optimization. Our method can provide consistent estimation results. In a comprehensive simulation study, our method shows an improved performance when it is compared to the recursive combination/partition procedures. In practice, can be determined based on a cross-validation procedure. As an application, we consider the well-known Pima Indian Diabetes data. We explore the relationship among the diabetes risk and several important variables including the plasma glucose concentration, body mass index and age.
authors.affiliation: Department of Statistics and Biostatistics Center, The George Washington University, Washington, DC, United States of America. ylai@gwu.edu
authors.forename: Yinglei
authors.initials: Y
authors.lastname: Lai
mesh_terms: D000465:Algorithms