Add to ORKG40 pages, 6 figuresInternational audienceFor general data, the number of complex solutions to the likelihood equations is constant and this number is called the (maximum likelihood) ML-degree of the model. In this article, we describe the special locus of data for which the likelihood equations have a solution in the model's singular locus
AbstractIn statistical inference, mixture models consisting of several component subpopulations are ...
The likelihood function for a model parameterised by (a vector in a p-dimensional space ) given obs...
13 pages, 1 article*Statistical Inference Using Maximum Likelihood Estimation and the Generalized Li...
Maximum likelihood estimation in statistics leads to the problem of maximizing a product of powers o...
The approach proposed by J. Jacquelin to explain the maximum likelihood method (MLH) is highly appre...
We study multivariate Gaussian models that are described by linear conditions on the concentration m...
Maximum likelihood is by far the most pop-ular general method of estimation. Its wide-spread accepta...
© 2015, International Press of Boston, Inc. All rights reserved. Maximum likelihood estimation is a ...
Transformed additive models accounting for the factors influencing a binomial probability sometimes ...
A property of distributions admitting sufficient statistics is obtained, connecting the likelihood f...
24 pages, 1 article*Maximum Likelihood Algorithms for Generalized Linear Mixed Models* (McCulloch, C...
It is shown that maximum likelihood estimation of unknown parameters of a linear system with singula...
<p>The numbers along the branches are the support values for the maximum-likelihood inference (ML) a...
Statistical models with latent structure have a history going back to the 1950s and have seen widesp...
The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive li...
AbstractIn statistical inference, mixture models consisting of several component subpopulations are ...
The likelihood function for a model parameterised by (a vector in a p-dimensional space ) given obs...
13 pages, 1 article*Statistical Inference Using Maximum Likelihood Estimation and the Generalized Li...
Maximum likelihood estimation in statistics leads to the problem of maximizing a product of powers o...
The approach proposed by J. Jacquelin to explain the maximum likelihood method (MLH) is highly appre...
We study multivariate Gaussian models that are described by linear conditions on the concentration m...
Maximum likelihood is by far the most pop-ular general method of estimation. Its wide-spread accepta...
© 2015, International Press of Boston, Inc. All rights reserved. Maximum likelihood estimation is a ...
Transformed additive models accounting for the factors influencing a binomial probability sometimes ...
A property of distributions admitting sufficient statistics is obtained, connecting the likelihood f...
24 pages, 1 article*Maximum Likelihood Algorithms for Generalized Linear Mixed Models* (McCulloch, C...
It is shown that maximum likelihood estimation of unknown parameters of a linear system with singula...
<p>The numbers along the branches are the support values for the maximum-likelihood inference (ML) a...
Statistical models with latent structure have a history going back to the 1950s and have seen widesp...
The method of maximum likelihood is widely used in epidemiology, yet many epidemiologists receive li...
AbstractIn statistical inference, mixture models consisting of several component subpopulations are ...
The likelihood function for a model parameterised by (a vector in a p-dimensional space ) given obs...
13 pages, 1 article*Statistical Inference Using Maximum Likelihood Estimation and the Generalized Li...