Estimation of unknown parameters in nonlinear and non-Gaussian state-space models - Tanizaki, Hisashi (Journal of statistical planning and inference, 2001)
Keywords: Exact sciences and technology; Inference from stochastic processes; time series analysis; Mathematics; Metropolis–Hastings algorithm; Mode estimation; Monte Carlo optimization; Nonparametric density estimation; Nonparametric inference; Probability and statistics; Sciences and techniques of general use; Simulated annealing; State-space model; Statistics; 62M10; Monte Carlo optimization; Metropolis–Hastings algorithm; Simulated annealing; Mode estimation; Nonparametric density estimation; State-space model; 62M20; Density estimation; Parameter estimation; Error estimation; Non Gaussian system; Optimization method; Non parametric estimation; State space; Stochastic process; Linear model; Optimization; State variable; Measurement equation; Function maximization; Sampling; Likelihood function; Sampling error; Monte Carlo method; Non linear estimation; Filtering; Time series; Metropolis Hastings algorithm; Statistical estimation; State space method; Gibbs sampling; Statistical forecasting; Non linear system; Filter; Non linear model; Variable parameter; Transition; Maximum likelihood; Bayes methods; Autocorrelation