When conducting inferential and epidemiologic studies, researchers are often interested in the distribution of time until the occurrence of some specified event, a form of incidence calculation. Furthermore, this interest often extends to the effects of intervening factors on this distribution. In this paper we impose the assumption that the phenomena being investigated are governed by a stationary Markov chain and review how one may estimate the above distribution. We then introduce and relate two different methods of investigating the effects of intervening factors. In particular, we show how an investigator may evaluate the effect of potential intervention programs. Finally, we demonstrate the proposed methodology using data from a population study.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1287/opre.30.6.1070 | DOI Listing |
Sci Rep
January 2025
Seenovate, Paris, 75009, France.
Optimizing athletic training programs with the support of predictive models is an active research topic, fuelled by a consistent data collection. The Fitness-Fatigue Model (FFM) is a pioneer for modelling responses to training on performance based on training load exclusively. It has been subject to several extensions and its methodology has been questioned.
View Article and Find Full Text PDFPLoS One
January 2025
School of Management, Xi'an Polytechnic University, Xi'an, Shaanxi, China.
This article compares the population agglomeration characteristics of the Xi'an metropolitan area in western China with those of metropolitan areas in other regions officially approved by the Chinese government. The kernel density estimation method and Markov chain model were used to conduct the study. The results revealed that from 2010 to 2020, the population agglomeration level of the Xi'an metropolitan area showed a trend of first increasing and then decreasing.
View Article and Find Full Text PDFJ Comput Graph Stat
October 2023
Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA.
Mixture Markov Model (MMM) is a widely used tool to cluster sequences of events coming from a finite state-space. However, the MMM likelihood being multi-modal, the challenge remains in its maximization. Although Expectation-Maximization (EM) algorithm remains one of the most popular ways to estimate the MMM parameters, however, convergence of EM algorithm is not always guaranteed.
View Article and Find Full Text PDFJ R Soc Interface
January 2025
Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield Grove, Bristol, BS8 2BN, UK.
COVID-19 vaccine programmes must account for variable immune responses and waning protection. Existing descriptions of antibody responses to COVID-19 vaccination convey limited information about the mechanisms of antibody production and maintenance. We describe antibody dynamics after COVID-19 vaccination with two biologically motivated mathematical models.
View Article and Find Full Text PDFBayesian Anal
June 2024
Department of Statistics, Purdue University, West Lafayette, IN 47907, USA.
The exponential random graph model (ERGM) is a popular model for social networks, which is known to have an intractable likelihood function. Sampling from the posterior for such a model is a long-standing problem in statistical research. We analyze the performance of the stochastic gradient Langevin dynamics (SGLD) algorithm (also known as noisy Longevin Monte Carlo) in tackling this problem, where the stochastic gradient is calculated via running a short Markov chain (the so-called inner Markov chain in this paper) at each iteration.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!