Monte Carlo profile confidence intervals for dynamic systems
| Autor: | Edward L. Ionides, Joonha Park, Carles Bretó, Aaron A. King, Richard A. Smith |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Time Factors Computer science likelihood-based inference Monte Carlo method Population Dynamics Biomedical Engineering Biophysics Bioengineering sequential Monte Carlo computer.software_genre 01 natural sciences Biochemistry Models Biological Biomaterials Hybrid Monte Carlo panel data 010104 statistics & probability 03 medical and health sciences symbols.namesake Frequentist inference Animals Computer Simulation Quasi-Monte Carlo method 0101 mathematics phylodynamic inference Likelihood Functions Models Statistical Markov chain Monte Carlo 030104 developmental biology spatio-temporal data symbols Monte Carlo integration Data mining Life Sciences–Mathematics interface time series Likelihood function Algorithm computer Monte Carlo Method Biotechnology Monte Carlo molecular modeling Research Article |
| Zdroj: | Journal of the Royal Society Interface |
| ISSN: | 1742-5662 |
| Popis: | Monte Carlo methods to evaluate and maximize the likelihood function enable the construction of confidence intervals and hypothesis tests, facilitating scientific investigation using models for which the likelihood function is intractable. When Monte Carlo error can be made small, by sufficiently exhaustive computation, then the standard theory and practice of likelihood-based inference applies. As datasets become larger, and models more complex, situations arise where no reasonable amount of computation can render Monte Carlo error negligible. We develop profile likelihood methodology to provide frequentist inferences that take into account Monte Carlo uncertainty. We investigate the role of this methodology in facilitating inference for computationally challenging dynamic latent variable models. We present examples arising in the study of infectious disease transmission, demonstrating our methodology for inference on nonlinear dynamic models using genetic sequence data and panel time-series data. We also discuss applicability to nonlinear time-series and spatio-temporal data. |
| Databáze: | OpenAIRE |
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