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RESEARCH PRODUCT

Panel Data Analysis via Mechanistic Models

Carles BretóCarles BretóAaron A. KingEdward L. Ionides

subject

FOS: Computer and information sciencesStatistics and ProbabilityMultivariate statisticsSeries (mathematics)Longitudinal dataComputer science05 social sciences01 natural sciencesMethodology (stat.ME)010104 statistics & probabilityNonlinear system0502 economics and business0101 mathematicsStatistics Probability and UncertaintyParticle filterAlgorithmStatistics - Methodology050205 econometrics Panel dataSequence (medicine)

description

Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models therefore consist of a collection of independent stochastic processes, generally linked through shared parameters while also having unit-specific parameters. To give the scientist flexibility in model specification, we are motivated to develop a framework for inference on panel data permitting the consideration of arbitrary nonlinear, partially observed panel models. We build on iterated filtering techniques that provide likelihood-based inference on nonlinear partially observed Markov process models for time series data. Our methodology depends on the latent Markov process only through simulation; this plug-and-play property ensures applicability to a large class of models. We demonstrate our methodology on a toy example and two epidemiological case studies. We address inferential and computational issues arising due to the combination of model complexity and dataset size.

https://doi.org/10.1080/01621459.2019.1604367