stochastic maximum likelihood estimation
Maximum likelihood estimation for stochastic processes - a martingale approach
... the likelihood function is a ...the likelihood and the maximum likelihood estimate (MLE), we can often use these limit theorems to prove asymptotic properties of the ...the stochastic ...
228
ECONOMIC ANALYSIS OF FRUITED PUMPKIN (Telfaria occidental alis Hook F.) PRODUCTION IN GUIUEA SAVANNAH, NIGERIA
... study Maximum likelihood Estimation (MLE) technique was used in stochastic frontier production for Total fixed cost for marginal, small and medium farmer are ...is Maximum in case of ...
8
Estimation of stochastic volatility models via Monte Carlo maximum likelihood
... can be applied in this context. By retaining the state space form, Fridman and Harris (1996) estimation technique offers the same advantages as the MCL. Some of the disadvantages of this method consist of ...
31
Parameter estimation for the stochastic SIS epidemic model
... parameter estimation in stochastic differential equations (SDEs) is a non-trivial problem [2, ...to estimation im- possible to ...parameter estimation methods for continuous time ...between ...
26
Model-Free IRL Using Maximum Likelihood Estimation
... expert’s stochastic transition function is fully known to the learner as in IRL for apprenticeship learning (Abbeel and Ng 2004) and in Bayesian IRL (Ramachandran ...
8
Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model
... = (4) As many authors have stated (Hosmer and Lemeshow (1989) [2] and Ryan (1997) [3], among others), the lo- gistic regression model becomes unstable when there exists strong dependence among explanatory variables ...
15
Maximum likelihood estimation for multiscale Ornstein Uhlenbeck processes
... A necessary step in statistical modelling is to fit the chosen model to the data by in- ferring the value of the unknown parameters. In the case of stochastic differential equations (SDE), this is a well studied ...
28
Estimation in Interacting Diffusions: Continuous and Discrete Sampling
... parameter estimation in one-dimen- sional stochastic differential equations from continuous and discrete observations by maximum likelihood and Bayes methods are extensively studied in Bishwal ...
5
On the Approximate Maximum Likelihood Estimation for Diffusion Processes
... approximate likelihood functions, which are maximized to obtain the approximate maximum likelihood estimators ...approximate likelihood converges to the true likelihood as the number of ...
39
A simple approach to maximum intractable likelihood estimation
... the likelihood function, even up to a normalising constant, is impossible or computationally ...and stochastic dynamical systems pro- vide numerous examples of this (see ...Composite Likelihood ...
24
Approximate maximum likelihood estimation for population genetic inference
... approximate maximum likelihood estimate is obtained using a stochastic version of the Nelder-Mead ...simulated maximum indirect likelihood (SMIL) estimator that is also based on ...
22
Maximum likelihood estimation for stochastic Lotka–Volterra model with jumps
... the maximum likelihood estimation for the drift coefficients based on continuous time ...The likelihood function and explicit estimator are derived by using semimartingale ...
22
Maximum Likelihood Estimation of Factored Regular Deterministic Stochastic Languages
... of stochastic languages defined with the co- emission product of finitely many proba- bilistic, deterministic finite-state acceptors (PDFA) and for every data sequence D of finitely many strings drawn ...some ...
12
Maximum likelihood estimation of a stochastic frontier model with residual covariance
... The stochastic frontier methodology has subsequently been extended in many directions using both cross-sectional and panel data. One advantage of using panel data is that it gives opportunity to examine and model ...
12
Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... The maximum likelihood estimates themselves are usually computed via the EM algorithm, which is a derivative-free method, but they can also be computed directly from the likelihood or by setting the ...
26
Asymptotics of Maximum Composite Likelihood Estimation for Geostatistical Data
... Stein et al. (2004) extended the above work by investigating the effect of conditioning on observations that are not necessarily the nearest neighbours. This is done in the context of restricted maximum com- ...
94
Maximum likelihood estimation for directional conditionally autoregressive models
... From the results based on the ML estimates of CAR model, we observe that for all the cases there are no significant biases at 5% level. The ESE of ρ is a good approximation to finite sample variance when the spatial ...
33
Computational approaches for maximum likelihood estimation for nonlinearmixed models.
... The radius by method interaction is signicant when all methods are included in the analysis. Even though ulaplace does fairly well for some replicates, there is no reason to keep considering it; this method has the same ...
175
Research on Initialization on EM Algorithm Based on Gaussian Mixture Model
... popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very ...
7
A maximum likelihood approach to correlation dimension and entropy estimation
... To obtain the correlation dimension and entropy from an experimental time series we derive estimators for these quantities together with expressions for their variances [r] ...
14