Parametric estimation of hidden Markov models by least squares type estimation and deconvolution
From MaRDI portal
Publication:2093141
DOI10.1007/s00362-022-01288-xzbMath1497.62220OpenAlexW2789913201MaRDI QIDQ2093141
Christophe Chesneau, Fabien Navarro, Salima El Kolei
Publication date: 4 November 2022
Published in: Statistical Papers (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00362-022-01288-x
Asymptotic properties of parametric estimators (62F12) Markov processes: estimation; hidden Markov models (62M05)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Parametric estimation of hidden stochastic model by contrast minimization and deconvolution
- Stochastic volatility with leverage: fast and efficient likelihood inference
- Statistical inference for fixed-effects partially linear regression models with errors in variables
- Adaptive density estimation in deconvolution problems with unknown error distribution
- Analysis, detection and correction of misspecified discrete time state space models
- Parameter estimation of state space models for univariate observations
- On the Markov chain central limit theorem
- Kalman filtering estimation of unobserved rational expectations with an application to the German hyperinflation
- Mixing: Properties and examples
- Minimax estimation of the noise level and of the deconvolution density in a semiparametric convolution model
- Fourier inference for stochastic volatility models with heavy-tailed innovations
- Convergence of a stochastic approximation version of the EM algorithm
- Semiparametric estimation in the (auto)-regressive \(\beta\)-mixing model with errors-in-variables
- Asymptotic normality and strong consistency of LS estimators in the EV regression model with NA errors
- Empirical likelihood for heteroscedastic partially linear errors-in-variables model with \(\alpha\)-mixing errors
- Adaptive estimation of the transition density of a Markov chain
- Parametric inference of autoregressive heteroscedastic models with errors in variables
- Least squares type estimation of the transition density of a particular hidden Markov chain
- Consistency of the maximum likelihood estimator for general hidden Markov models
- Nonparametric estimation of the stationary density and the transition density of a Markov chain
- Adaptive estimation of the transition density of a particular hidden Markov chain
- SUR UN MODÉLE AUTORÉGRESSIF NON LINÉAIRE, ERGODICITÉ ET ERGODICITÉ GÉOMÉTRIQUE
- Recursive estimation of mixed autoregressive-moving average order
- Asymptotic Statistics
- LARGE SAMPLE ANALYSIS OF AUTOREGRESSIVE MOVING-AVERAGE MODELS WITH ERRORS IN VARIABLES
- On the effect of estimating the error density in nonparametric deconvolution
- Density Estimation for the Case of Supersmooth Measurement Error
- Particle Markov Chain Monte Carlo Methods
- On the effect of misspecifying the error density in a deconvolution problem
- Estimation in autoregressive model with measurement error
- Particle filter-based approximate maximum likelihood inference asymptotics in state-space models
- Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form
- SMC2: An Efficient Algorithm for Sequential Analysis of State Space Models
- Methodology for Non-Parametric Deconvolution When the Error Distribution is Unknown
- Measurement Error in Linear Autoregressive Models
- Stochastic volatility models as hidden Markov models and statistical applications
