Results 261 to 270 of about 178,886 (297)
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Journal of Statistical Computation and Simulation, 1984
Two variations of the dispersion-mean correspondence model for varince components lead to the ML and REML equations. This formulation provides for addition of a nonnegativity constraint to the computational method of T. W. Anderson (1971, 1973), an iterative procedure for obtaining ML and REML estimates, which not only assures that estimates will be ...
Kenneth G. Brownt, Michael A. Burgess
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Two variations of the dispersion-mean correspondence model for varince components lead to the ML and REML equations. This formulation provides for addition of a nonnegativity constraint to the computational method of T. W. Anderson (1971, 1973), an iterative procedure for obtaining ML and REML estimates, which not only assures that estimates will be ...
Kenneth G. Brownt, Michael A. Burgess
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Computers and Geosciences, 1997
Abstract Maximum likelihood and restricted maximum likelihood are appealing parametric alternatives to other classical nonparametric methods to estimate the covariance parameters of spatial variables. MLREML, an ANSI FORTRAN-77 computer program which performs both kinds of inference methods is presented.
Eulogio Pardo-Igúzquiza
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Abstract Maximum likelihood and restricted maximum likelihood are appealing parametric alternatives to other classical nonparametric methods to estimate the covariance parameters of spatial variables. MLREML, an ANSI FORTRAN-77 computer program which performs both kinds of inference methods is presented.
Eulogio Pardo-Igúzquiza
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Journal of Statistical Computation and Simulation, 1989
maximum Likelihood (ML), Restricted Maximum Likelihood (REML) and Bayesian methods are often preferred over other methods for estimating variance components in animal breeding. Iterative computing stategies are required for obtaining estimates with unbalanced data and models with at least two variance components.
Ina Hoeschele
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maximum Likelihood (ML), Restricted Maximum Likelihood (REML) and Bayesian methods are often preferred over other methods for estimating variance components in animal breeding. Iterative computing stategies are required for obtaining estimates with unbalanced data and models with at least two variance components.
Ina Hoeschele
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On algorithms for restricted maximum likelihood estimation
Computational Statistics and Data Analysis, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Journal of Statistical Computation and Simulation, 2022 In this article, we propose a restricted gamma ridge regression estimator (RGRRE) by combining the gamma ridge regression (GRR) and restricted maximum likelihood estimator (RMLE) to combat multicollinearity problem for estimating the parameter beta in ...
Muhammad Qasim +2 more
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Metrika, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
De Oliveira, Victor +1 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
De Oliveira, Victor +1 more
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Restricted Maximum Likelihood Estimators for Poisson Parameters
Journal of the American Statistical Association, 1976Abstract Let X, Xi, Xij, j = 1, …, n, j = 1, …, ni be independent Poisson random variables with parameters λ, λ i , λ ij . The maximum likelihood estimators for the parameters subject to (1) and subject to (1) and (2) are obtained. The bias and MSE of these restricted maximum likelihood estimators (RMLE's) are approximated by analytic and Monte Carlo ...
Richard L. Dykstra, Richard W. Madsen
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A restricted maximum likelihood estimator for truncated height samples
Economics & Human Biology, 2004A restricted maximum likelihood (ML) estimator is presented and evaluated for use with truncated height samples. In the common situation of a small sample truncated at a point not far below the mean, the ordinary ML estimator suffers from high sampling variability.
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Computers & Geosciences, 1998
The power variogram model γ(h)=α·|h|β, α>0, β∈]0, 2[, is an important theoretical model when only the intrinsic hypothesis is assumed for a random function and has been extensively used in practice, e.g. for variables such as piezometric level in groundwater hydrology and rainfall in surface hydrology.
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The power variogram model γ(h)=α·|h|β, α>0, β∈]0, 2[, is an important theoretical model when only the intrinsic hypothesis is assumed for a random function and has been extensively used in practice, e.g. for variables such as piezometric level in groundwater hydrology and rainfall in surface hydrology.
openaire +1 more source