MODELING A NONLINEAR BIOPHYSICAL TREND FOLLOWED BY LONG-MEMORY EQUILIBRIUM WITH UNKNOWN CHANGE POINT

• 成果类型: Article
• 署名作者: Zhang, Wenyu; Griffin, Maryclare; Matteson, David S.
• 署名单位: Cornell University; University of Massachusetts System; University of Massachusetts Amherst
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1655
• 发表日期: 2023
• 页码: 860-880

摘要

Measurements of many biological processes are characterized by an ini-tial trend period followed by an equilibrium period. Scientists may wish to quantify features of the two periods as well as the timing of the change point. Specifically, we are motivated by problems in the study of electrical cell -substrate impedance sensing (ECIS) data. ECIS is a popular new technology which measures cell behavior noninvasively. Previous studies using ECIS data have found that different cell types can be classified by their equilib-rium behavior. However, it can be challenging to identify when equilibrium has been reached and to quantify the relevant features of cells' equilibrium behavior. In this paper we assume that measurements during the trend period are independent deviations from a smooth nonlinear function of time, and that measurements during the equilibrium period are characterized by a sim-ple long memory model. We propose a method to simultaneously estimate the parameters of the trend and equilibrium processes and locate the change point between the two. We find that this method performs well in simulations and in practice. When applied to ECIS data, it produces estimates of change points and measures of cell equilibrium behavior which offer improved clas-sification of infected and uninfected cells.