Results 61 to 70 of about 63,643 (106)
Bootstrap Approximation to Prediction MSE for State-Space Models with Estimated Parameters
, 2004 We propose a simple but general bootstrap method for estimating the Prediction Mean Square Error (PMSE) of the state vector predictors when the unknown model parameters are estimated from the observed series.
Pfeffermann, Danny, Tiller, Richard
core
Robust Henderson III estimators of variance components in the nested error model [PDF]
Common methods for estimating variance components in Linear Mixed Models include Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML). These methods are based on the strong assumption of multivariate normal distribution and it is well know ...
Betsabé Pérez +2 more
core
Bayesian and Frequentist Approaches to Hedonic Modeling in a Geo-Statistical Framework [PDF]
We compare Least Squares, Maximum Likelihood and Bayesian approaches to estimation in a Hedonic context. The approaches are compared from theoretical and practical perspectives and from the viewpoint of a policy maker or urban planner. The approaches are
Carriazo, Fernando, Ghosh, Gaurav S.
core +1 more source
Robust priors in nonlinear panel data models [PDF]
Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to different values of the individual effects. Fixed effects, random effects, and Bayesian approaches all fall in this category.
Manuel Arellano, Stéphane Bonhomme
core
Semi-parametric regression estimation of the tail index [PDF]
, 2018 Dickson, Maria Michela +2 more
core +1 more source
Hierarchical Generalized Linear Models: The R Package HGLMMM [PDF]
The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution.
Emmanuel Lesaffre, Marek Molas
core +1 more source
A robust DF‐REML framework for variance components estimation in genetic studies
Bioinform., 2017 Vanda M. Lourenço +3 more
semanticscholar +1 more source