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Sequential Change-point Detection for Time Series

Description:

Sequential change-point detection for time series enables us to sequentially check the hypothesisthat the model still holds as more and more data are observed. It’s widely used in data monitoring in practice. In this work, we propose two models: Binomial AR(1) model and Generalized Beta AR(p) model, for modeling binomial...

Keyword:

compositional time series, time series of counts, ergodicity, strong mixing processes, and sequential change-point detection

Subject:

Statistics

Creator:

Liu, Yajun

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

06/22/2022

Date Created:

2022-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:16079 and etdadmin_upload_902877