✦ LIBER ✦

Promotion Time Models with Time-Changing Exposure and Heterogeneity: Application to Infectious Diseases

✍ Scribed by Maud Tournoud; René Ecochard

Book ID: 101717441
Publisher: John Wiley and Sons
Year: 2008
Tongue: English
Weight: 196 KB
Volume: 50
Category: Article
ISSN: 0323-3847
DOI: 10.1002/bimj.200710405

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

A time-varying shared frailty model with

A time-varying shared frailty model with application to infectious diseases

✍ Enki, Doyo G.; Noufaily, Angela; Farrington, C. Paddy 📂 Article 📅 2014 🏛 Institute of Mathematical Statistics 🌐 English ⚖ 247 KB

Models for Exposure-Time-Response Relati

Models for Exposure-Time-Response Relationships with Applications to Cancer Epidemiology

✍ Thomas, D C 📂 Article 📅 1988 🏛 Annual Reviews 🌐 English ⚖ 725 KB

Infectious disease models with time-vary

Infectious disease models with time-varying parameters and general nonlinear incidence rate

✍ Xinzhi Liu; Peter Stechlinski 📂 Article 📅 2012 🏛 Elsevier Science 🌐 English ⚖ 831 KB

A robust P-spline approach to closed pop

A robust P-spline approach to closed population capture–recapture models with time dependence and heterogeneity

✍ Jakub Stoklosa; Richard M. Huggins 📂 Article 📅 2012 🏛 Elsevier Science 🌐 English ⚖ 350 KB

Non-homogeneous Markov process models wi

Non-homogeneous Markov process models with informative observation times, with an application to Alzheimer's disease

✍ Chen, Baojing; Zhou, Xiao-Hua Andrew 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 52 KB

Forecasting with latent structure time s

Forecasting with latent structure time series models: an application to nominal interest rates

✍ Sridhar Iyer; Rick L. Andrews 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 195 KB 👁 2 views

In this paper we develop a latent structure extension of a commonly used structural time series model and use the model as a basis for forecasting. Each unobserved regime has its own unique slope and variances to describe the process generating the data, and at any given time period the model predic