Pages that link to "Item:Q2729114"

The following pages link to Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation (Q2729114):

Displaying 33 items.

• Partial marginal likelihood estimation for general transformation models (Q391907) (← links)
• Trajectory averaging for stochastic approximation MCMC algorithms (Q605929) (← links)
• Fitting nonlinear ordinary differential equation models with random effects and unknown initial conditions using the stochastic approximation expectation-maximization (SAEM) algorithm (Q736434) (← links)
• Influence analyses of nonlinear mixed-effects models (Q956835) (← links)
• High-dimensional exploratory item factor analysis by a Metropolis-Hastings Robbins-Monro algorithm (Q971529) (← links)
• Maximum likelihood estimation for social network dynamics (Q993234) (← links)
• A baseline-free procedure for transformation models under interval censorship (Q995966) (← links)
• Simulation-based approach to estimation of latent variable models (Q1010464) (← links)
• Noise contrastive estimation: asymptotic properties, formal comparison with MC-MLE (Q1616322) (← links)
• Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants (Q1621320) (← links)
• Approximate maximum likelihood estimation of the autologistic model (Q1623803) (← links)
• A model for analyzing spatially correlated binary data clustered in uncorrelated lattices (Q1756181) (← links)
• On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods (Q1790298) (← links)
• Long range search for maximum likelihood in exponential families (Q1950807) (← links)
• Convergence and convergence rate of stochastic gradient search in the case of multiple and non-isolated extrema (Q2018557) (← links)
• Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood (Q2259849) (← links)
• Approximate computations for binary Markov random fields and their use in Bayesian models (Q2361473) (← links)
• Annealing stochastic approximation Monte Carlo algorithm for neural network training (Q2384162) (← links)
• Markov chain Monte Carlo estimation of spatial dynamic panel models for large samples (Q2419151) (← links)
• Bayesian and non-Bayesian analysis of gamma stochastic frontier models by Markov chain Monte Carlo methods (Q2488426) (← links)
• On some recent advances on high dimensional Bayesian statistics (Q2786539) (← links)
• A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants (Q3012676) (← links)
• A Novel Approach for Markov Random Field With Intractable Normalizing Constant on Large Lattices (Q3391132) (← links)
• Inference on Survival Data with Covariate Measurement Error - An Imputation-based Approach (Q3411067) (← links)
• A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems (Q3838482) (← links)
• Local influence for generalized linear mixed models (Q4470645) (← links)
• Coupling a stochastic approximation version of EM with an MCMC procedure (Q4671811) (← links)
• A universal procedure for parametric frailty models (Q4825481) (← links)
• Bayesian Analysis of Crossclassified Spatial Data with Autocorrelation (Q5450456) (← links)
• Stochastic approximation (Q5907083) (← links)
• The Poisson transform for unnormalised statistical models (Q5963779) (← links)
• A synthetic likelihood approach for intractable Markov random fields (Q6177001) (← links)
• A mixed stochastic approximation EM (MSAEM) algorithm for the estimation of the four-parameter normal ogive model (Q6198872) (← links)