Parameter estimation by contrast minimization for noisy observations of a diffusion process
type: Dataset
description: We consider the estimation of unknown parameters in the drift and diffusion coefficients of a one-dimensional ergodic diffusion X when the observation Y is a discrete sampling of X with an additive noise, at times i *delta, i = 1 ... N. Assuming that the sampling interval tends to 0 while the total length time interval tends to infinity, we prove limit theorems for functionals associated with the observations, based on local means of the sample. We apply these results to obtain a contrast function. The associated minimum contrast estimators are shown to be consistent. We provide an illustration on simulated and real data from neuronal activity.
landing page: https://hal.science/hal-00493967
keyword: contrast-function
keyword: diffusion-process
keyword: discrete-time-noisy-observations
keyword: hidden-markov-models
keyword: infoeu-reposemanticspreprint
keyword: mathmath-stmathematics-mathstatistics-mathst
keyword: parametric-inference
keyword: preprints-working-papers-
keyword: statthstatistics-statstatistics-theory-statth
issued: 2026-05-29T04:18:17.403918
modified: 2026-05-29T04:18:17.403928
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Parameter estimation by contrast minimization for noisy observations of a diffusion process
type: Distribution
format: HTML
access URL: https://hal.science/hal-00493967
issued: 2026-05-29T04:18:17.442808
modified: 2026-05-29T04:18:17.349020