Add to ORKGThrough Monte Carlo experiments, this paper compares the performances of different gradient optimization algorithms, when performing full information maximum likelihood (FIML) estimation of econometric models. Different matrices are used (Hessian, outer products matrix, GLS-type matrix, as well as a mixture of them)
Estimation of variance components by Monte Carlo (MC) expectation maximization (EM) restricted maxim...
The Bayesian approach allows one to estimate model parameters from prior expert knowledge about par...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...
Through Monte Carlo experiments, this paper compares the performances of different gradient optimiza...
Full information maximum likelihood estimation of econometric models, linear and nonlinear in variab...
In econometric models, estimates of the asymptotic covariance matrix of FIML coefficients are tradit...
This paper presents a Monte-Carlo study on the practical reliability of numerical algorithms for FIM...
A Monte Carlo simulation examined the performance of 4 missing data methods in structural equation m...
237 pagesIt seems that in the current age, computers, computation, and data have an increasingly imp...
This paper presents a numerical algorithm for computing full information maximum likelihood (FIML) a...
AbstractThis paper develops two algorithms. Algorithm 1 computes the exact, Gaussian, log-likelihood...
The Fisher information matrix (FIM) is a critical quantity in several aspects of mathematical modeli...
Gradient-based algorithms are popular when solving unconstrained optimization problems. By exploitin...
This paper addresses the issue of obtaining maximum likelihood estimates of parameters for structura...
Estimation of variance components by Monte Carlo (MC) expectation maximization (EM) restricted maxim...
Estimation of variance components by Monte Carlo (MC) expectation maximization (EM) restricted maxim...
The Bayesian approach allows one to estimate model parameters from prior expert knowledge about par...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...
Through Monte Carlo experiments, this paper compares the performances of different gradient optimiza...
Full information maximum likelihood estimation of econometric models, linear and nonlinear in variab...
In econometric models, estimates of the asymptotic covariance matrix of FIML coefficients are tradit...
This paper presents a Monte-Carlo study on the practical reliability of numerical algorithms for FIM...
A Monte Carlo simulation examined the performance of 4 missing data methods in structural equation m...
237 pagesIt seems that in the current age, computers, computation, and data have an increasingly imp...
This paper presents a numerical algorithm for computing full information maximum likelihood (FIML) a...
AbstractThis paper develops two algorithms. Algorithm 1 computes the exact, Gaussian, log-likelihood...
The Fisher information matrix (FIM) is a critical quantity in several aspects of mathematical modeli...
Gradient-based algorithms are popular when solving unconstrained optimization problems. By exploitin...
This paper addresses the issue of obtaining maximum likelihood estimates of parameters for structura...
Estimation of variance components by Monte Carlo (MC) expectation maximization (EM) restricted maxim...
Estimation of variance components by Monte Carlo (MC) expectation maximization (EM) restricted maxim...
The Bayesian approach allows one to estimate model parameters from prior expert knowledge about par...
A maximum likelihood (ML) estimation procedure is developed to find the mean of the exponential fami...