A framework for the comparison of maximum pseudo-likelihood and maximum likelihood estimation of exponential family random graph models
Tóm tắt
Từ khóa
Tài liệu tham khảo
Agresti, 2002
Barndorff-Nielsen, 1978
Besag, J., 2000. Markov Chain Monte Carlo for statistical inference. Working Paper 9, Center for Statistics and the Social Sciences, University of Washington.
Boer, 2003
Borgatti, 1999
Butts, 2008, network: A package for managing relational data in R, Journal of Statistical Software, 24, 10.18637/jss.v024.i02
Corander, J., Dahmström, K., Dahmström, P., 1998. Maximum likelihood estimation for Markov graphs. Research Report 8, Department of Statistics, University of Stockholm.
Corander, 2002, Maximum likelihood estimation for exponential random graph models, 1
Crouch, 1998, Markov Chain Monte Carlo maximum likelihood estimation for p∗ social network models
Firth, 1993, Bias reduction in maximum likelihood estimates, Biometrika, 80, 27, 10.1093/biomet/80.1.27
Frank, 1991, Statistical analysis of change in networks, Statistica Neerlandica, 45, 283, 10.1111/j.1467-9574.1991.tb01310.x
Frank, 1986, Markov graphs, Journal of the American Statistical Association, 81, 832, 10.2307/2289017
Geyer, 1992, Constrained Monte Carlo maximum likelihood for dependent data, Journal of the Royal Statistical Society B, 54, 657
Handcock, M.S., 2002. Degeneracy and inference for social network models. Paper presented at the Sunbelt XXII International Social Network Conference in New Orleans, LA.
Handcock, M.S., 2003. Assessing degeneracy in statistical models of social networks. Working Paper 39, Center for Statistics and the Social Sciences, University of Washington.
Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., Morris, M., 2003. statnet: Software Tools for the Statistical Modeling of Network Data. Statnet Project http://statnet.org/. R package Version 2.0, Seattle, WA.
Handcock, 2008, statnet: Software tools for the representation, visualization, analysis and simulation of network data, Journal of Statistical Software, 24, 10.18637/jss.v024.i01
Heinze, 2002, A solution to the problem of separation in logistic regression, Statistics in Medicine, 21, 2409, 10.1002/sim.1047
Hunter, 2006, Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics, 15, 565, 10.1198/106186006X133069
Hunter, 2008, ergm: A package to fit, simulate and diagnose exponential-family models for networks, Journal of Statistical Software, 24, 10.18637/jss.v024.i03
Lazega, 2001
Lubbers, 2007, A comparison of various approaches to the exponential random graph model: A reanalysis of 104 student networks in school classes, Social Networks, 29, 489, 10.1016/j.socnet.2007.03.002
Morris, 2008, Specification of exponential-family random graph models: terms and computational aspects, Journal of Statistical Software, 24, 10.18637/jss.v024.i04
R Development Core Team, 2007. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Version 2.6.1, Vienna, Austria. ISBN 3-900051-07-0.
Robins, 2007, Recent developments in exponential random graph (p∗) models for social networks, Social Networks, 29, 192, 10.1016/j.socnet.2006.08.003
Robins, 2005, Interdependencies and social processes: dependence graphs and generalized dependence structures, 192
Saul, 2007, July Exploring biological network structure using exponential random graph models, Bioinformatics, 23, 2604, 10.1093/bioinformatics/btm370
Snijders, 2002, Markov Chain Monte Carlo estimation of exponential random graph models, Journal of Social Structure, 3
Snijders, 2006, New specifications for exponential random graph models, Sociological Methodology, 36, 99, 10.1111/j.1467-9531.2006.00176.x
Strauss, 1990, Pseudolikelihood estimation for social networks, Journal of the American Statistical Association, 85, 204, 10.2307/2289546
Wang, 2008
Wasserman, 2005, An introduction to random graphs, dependence graphs, and p∗, 148
Wasserman, 1994