A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations

DOI10.1016/j.csda.2014.11.007OpenAlexW2090094974WikidataQ115045092 ScholiaQ115045092MaRDI QIDQ1623807

Jennifer A. Hoeting, Chihoon Lee, Libo Sun

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1305.4390

zbMATH Keywords

stochastic differential equationsEuler-Maruyama schemechronic wasting diseaseauxiliary importance samplingpartially observed discrete sparse datapenalized simulated maximum likelihood estimation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Markov processes: estimation; hidden Markov models (62M05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10)

Related Items

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Uses Software

• R
• sde
• dfoptim

Cites Work

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• Efficient high-dimensional importance sampling
• A regularized bridge sampler for sparsely sampled diffusions
• A comparison of three different stochastic population models with regard to persistence time
• Inference methods for discretely observed continuous-time stochastic volatility models: A commented overview
• Time series analysis via mechanistic models
• Higher-order implicit strong numerical schemes for stochastic differential equations
• Stochastic epidemic models and their statistical analysis
• Comparison of deterministic and stochastic SIS and SIR models in discrete time
• Closed-form likelihood expansions for multivariate diffusions
• Efficient importance sampling maximum likelihood estimation of stochastic differential equations
• Asymptotics of an efficient Monte Carlo estimation for the transition density of diffusion processes
• A comparison of persistence-time estimation for discrete and continuous stochastic population models that include demographic and environmental variability
• Application of Girsanov theorem to particle filtering of discretely observed continuous-time non-linear systems
• Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation
• Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential Equations
• Malaria in Northwest India: Data Analysis via Partially Observed Stochastic Differential Equation Models Driven by Lévy Noise
• Filtering via Simulation: Auxiliary Particle Filters
• Deterministic Nonperiodic Flow
• Particle Markov Chain Monte Carlo Methods
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation