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A Tutorial on MM Algorithms
The principle behind MM algorithms is explained, some methods for constructing them are suggested, and some of their attractive features are discussed.
Goodness of Fit of Social Network Models
A systematic examination of a real network data set using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed networks concludes that these models capture aspects of the social structure of adolescent friendship relations not represented by previous models.
ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks.
- D. Hunter, M. Handcock, C. Butts, S. Goodreau, M. Morris
- Computer ScienceJournal of statistical software
- 5 May 2008
Ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets from a fitted ERGM using Markov chain Monte Carlo; and assessing how well a fittedERGM does at capturing characteristics of a particular networkData set.
MM algorithms for generalized Bradley-Terry models
- D. Hunter
- 1 February 2003
The Bradley-Terry model for paired comparisons is a simple and muchstudied means to describe the probabilities of the possible outcomes when individuals are judged against one another in pairs. Among…
mixtools: An R Package for Analyzing Finite Mixture Models
The mixtools package for R provides a set of functions for analyzing a variety of nite mixture models. These functions include both traditional methods, such as EM algorithms for univariate and…
Optimization Transfer Using Surrogate Objective Functions
Because optimization transfer algorithms often exhibit the slow convergence of EM algorithms, two methods of accelerating optimization transfer are discussed and evaluated in the context of specific problems.
Inference in Curved Exponential Family Models for Networks
This article first reviews the method of maximum likelihood estimation using Markov chain Monte Carlo in the context of fitting linear ERGMs, then extends this methodology to the situation where the model comes from a curved exponential family.
Variable Selection using MM Algorithms.
This article proposes a new class of algorithms for finding a maximizer of the penalized likelihood for a broad class of penalty functions and proves that when these MM algorithms converge, they must converge to a desirable point.
Curved exponential family models for social networks
- D. Hunter
- Computer ScienceSoc. Networks
- 1 May 2007
mixtools: An R Package for Analyzing Mixture Models
The mixtools package for R provides a set of functions for analyzing a variety of finite mixture models. These functions include both traditional methods, such as EM algorithms for univariate and…