For a large variety of discrete choice models (or contingency table models) efficientand stable maximum likelihood methods can be constructed basedon the majorization method. The course introduces majorization methods for algorithm construction. We show how to use the majorization principle to reduce complicated optimization problems to sequences of weighted or unweighted least squares problems.Majorization methods are then applied to data analysis techniques used in economics, political science, psychometrics, ecology, sociology, and education
A simple optimization principle f (θ)g(θ) b κ Objective: min θ∈Θ f (θ) Principle called Majorization...
So far, many ad hoc techniques have been proposed to compute maxium likelihood estimates for various...
A general procedure is described for setting up monotonically convergent algorithms to solve some ge...
For a large variety of discrete choice models (or contingency table models) efficientand stable maxi...
textabstractFor many least-squares decomposition models efficient algorithms are well known. A more ...
Majorization algorithms generalize the EM algorithm. In this paper we discuss and compare various qu...
The problem of minimizing a general matrix, trace function, possibly subject to certain constraints,...
Majorization algorithms generalize the EM algorithm. In this paper we discuss several distinct, alth...
This is a programmatic paper that reviews the construction of majorization algorithms and their rate...
International audienceIn a learning context, data distribution are usually unknown. Observation mode...
Abstract. In this paper, the diagonal majorization algorithm (DMA) has been investigated. The resear...
The aim of this paper is to study a certain class of nonlinear optimization problems of particular i...
A simple optimization principle f (θ)g(θ) b κ Objective: min θ∈Θ f (θ) Principle called Majorization...
So far, many ad hoc techniques have been proposed to compute maxium likelihood estimates for various...
A general procedure is described for setting up monotonically convergent algorithms to solve some ge...
For a large variety of discrete choice models (or contingency table models) efficientand stable maxi...
textabstractFor many least-squares decomposition models efficient algorithms are well known. A more ...
Majorization algorithms generalize the EM algorithm. In this paper we discuss and compare various qu...
The problem of minimizing a general matrix, trace function, possibly subject to certain constraints,...
Majorization algorithms generalize the EM algorithm. In this paper we discuss several distinct, alth...
This is a programmatic paper that reviews the construction of majorization algorithms and their rate...
International audienceIn a learning context, data distribution are usually unknown. Observation mode...
Abstract. In this paper, the diagonal majorization algorithm (DMA) has been investigated. The resear...
The aim of this paper is to study a certain class of nonlinear optimization problems of particular i...
A simple optimization principle f (θ)g(θ) b κ Objective: min θ∈Θ f (θ) Principle called Majorization...
So far, many ad hoc techniques have been proposed to compute maxium likelihood estimates for various...
A general procedure is described for setting up monotonically convergent algorithms to solve some ge...