Change-Point Detection in Binomial Thinning Processes, with Applications in Epidemiology

DOI10.1080/07474946.2013.803821zbMath1294.62174OpenAlexW2063280601MaRDI QIDQ2854359

Xian Yu, Michael Baron, Pankaj K. Choudhary

Publication date: 18 October 2013

Published in: Sequential Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07474946.2013.803821

zbMATH Keywords

CUSUMMarkov chainSIRchange-point detectionadaptive thresholdthinning process

Mathematics Subject Classification ID

Epidemiology (92D30) Markov processes: estimation; hidden Markov models (62M05) Fault detection; testing in circuits and networks (94C12) Sequential statistical analysis (62L10) Optimal stopping in statistics (62L15)

Related Items

Minimax and pointwise sequential changepoint detection and identification for general stochastic models ⋮ Detection and identification of changes of hidden Markov chains: asymptotic theory ⋮ Detecting changes in real-time data: a user’s guide to optimal detection ⋮ Fluctuations and precise deviations of cumulative INAR time series ⋮ Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection ⋮ Truncated sequential change-point detection for Markov chains with applications in the epidemic statistical analysis ⋮ Inference for bivariate integer-valued moving average models based on binomial thinning operation ⋮ Changepoint detection in non-exchangeable data

Cites Work

• Unnamed Item
• Unnamed Item
• Decision theoretic optimality of the cusum procedure
• Mathematical epidemiology.
• Optimal stopping times for detecting changes in distributions
• Discrete analogues of self-decomposability and stability
• SPRT and CUSUM in hidden Markov models
• Asymptotic operating characteristics of an optimal change point detection in hidden Markov models
• Optimality of the CUSUM procedure in continuous time.
• Testing for a change in the parameter values and order of an autoregressive model
• Asymptotic properties of CUSUM and Shiryaev's procedures for detecting a change in a nonhomogeneous Gaussian process
• A Note on “The Optimal Stopping Time for Detecting Changes in Discrete Time Markov Processes” by Han and Tsung
• Modelling Count Data Time Series with Markov Processes Based on Binomial Thinning
• The Optimal Stopping Time for Detecting Changes in Discrete Time Markov Processes
• A New Class of Autoregressive Models for Time Series of Binomial Counts
• Mathematical and Statistical Estimation Approaches in Epidemiology
• Information bounds and quick detection of parameter changes in stochastic systems
• Procedures for Reacting to a Change in Distribution