Likelihood-based inference for discretely observed birth-death-shift processes, with applications to evolution of mobile genetic elements

DOI10.1111/biom.12352zbMath1419.62481arXiv1411.0031OpenAlexW2963297750WikidataQ40289220 ScholiaQ40289220MaRDI QIDQ2809527

Jason Xu, Vladimir N. Minin, Peter Guttorp, Midori Kato-Maeda

Publication date: 30 May 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1411.0031

zbMATH Keywords

branching processesmissing dataexpectation maximizationmolecular epidemiology

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Markov processes: estimation; hidden Markov models (62M05) Applications of branching processes (60J85) Continuous-time Markov processes on discrete state spaces (60J27)

Related Items (7)

Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks ⋮ Gain-loss-duplication models for copy number evolution on a phylogeny: exact algorithms for computing the likelihood and its gradient ⋮ A numerical approach for evaluating the time-dependent distribution of a quasi birth-death process ⋮ Parameter estimation for discretely observed linear birth‐and‐death processes ⋮ Birth/birth-death processes and their computable transition probabilities with biological applications ⋮ Parameter estimation for multivariate population processes: a saddlepoint approach ⋮ Computational tools for assessing gene therapy under branching process models of mutation

Cites Work

• Unnamed Item
• Spatial birth-death swap chains
• Transition probabilities for general birth-death processes with applications in ecology, genetics, and evolution
• Estimating change rates of genetic markers using serial samples: applications to the transposon IS6110 in Mycobacterium tuberculosis
• Counting labeled transitions in continuous-time Markov models of evolution
• Maximum likelihood estimation in the birth-and-death process
• Estimating the dimension of a model
• Fitting birth-death processes to panel data with applications to bacterial DNA fingerprinting
• Statistical Inference in a Two-Compartment Model for Hematopoiesis
• Fast Fourier Methods in Computational Complex Analysis
• Bayesian inference in a hidden stochastic two-compartment model for feline hematopoiesis
• Estimation for General Birth-Death Processes

This page was built for publication: Likelihood-based inference for discretely observed birth-death-shift processes, with applications to evolution of mobile genetic elements