Efficient parametric estimation for a signal-plus-noise Gaussian model from discrete time observations

DOI10.1007/S11203-020-09225-1zbMath1469.62335arXiv1903.06447OpenAlexW3047914813MaRDI QIDQ2040939

K. El Waled, Vincent Monsan, Dominique Dehay

Publication date: 15 July 2021

Published in: Statistical Inference for Stochastic Processes (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1903.06447

zbMATH Keywords

maximum likelihood estimationHellinger distanceminimax efficiencyBayesian estimationasymptotic properties of estimatorshigh frequency samplinglow frequency samplingtriangular Gaussian array

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Signal detection and filtering (aspects of stochastic processes) (60G35)

Cites Work

• Global smoothness estimation of a Gaussian process from general sequence designs
• Adaptive estimation of an ergodic diffusion process based on sampled data
• Asymptotic statistics. With a view to stochastic processes
• On the Hellinger type distances for filtered experiments
• Quadratic approximation for log-likelihood ratio processes
• Approximate discrete-time schemes for statistics of diffusion processes
• Designs for Regression Problems With Correlated Errors: Many Parameters
• Maximnm contrast estimation for diffusion processes from discrete observations
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