S. Wasserman and P. Pattison. Logit models and logistic regression for social networks: I. an introduction to markov graphs and p  Home/Search Document Not in Database Summary Related Articles Check |
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Estimation and Prediction for Stochastic Blockstructures - Nowicki, Snijders (Correct)
....covariates (defined at the level of the vertex or, more generally, of the ordered pair (i, j) A second possibility is to adopt more complex conditional dependence assumptions for entries in Y conditional on the latent classes X. The structural parameters contained in the p # model proposed by Wasserman and Pattison (1996) provide a natural point of departure for such an analysis. A model combining latent classes of vertices with the structural parameters of the p # model seems feasible in principle, but the elaboration of statistical procedures for such a model will require considerable e#ort. Another possibility ....
WASSERMAN, S., and PATTISON, P. (1996), "Logit models and logistic regression for social networks: I. An introduction to Markov graphs and p # ". Psychometrika, 61, 401 -- 425.
The Statistical Evaluation of Social Network Dynamics - Snijders (2001) (1 citation) (Correct)
....of a Markov chain with stationary intensity matrix on a finite outcome space tends to a unique limiting distribution as t ##, independent of the initial distribution. For a certain specification of our model, this limiting distribution is the p # model for social networks proposed by Wasserman and Pattison (1996), generalizing the Markov graph distribution proposed by Frank and Strauss (1986) The p # model is a family of probability distributions for a single observation x on a stochastic directed graph X. The probability distribution for the p # model is defined by P X = x = exp(# # z(x) #(#) 41) ....
Wasserman, S., and P. Pattison. 1996. Logit models and logistic regression for social networks: I. An introduction to Markov graphs and p # . Psychometrika 61: 401 -- 425.
Statistics in Sociology, 1950-2000: A Vignette - Raftery (1999) (Correct)
.... 1994) Frank and Strauss (1986) developed formal statistical models for such networks related to the Markov random eld models used in Bayesian image analysis, and derived using the Hammsersley Cli ord theorem (Besag 1974) This has led to the promising p class of models for social networks (Wasserman and Pattison 1996). Methods for the analysis of social networks have focused mostly on small data sets with complete data. In practical applications, however, such as the e ect of sexual network patterns on the spread of sexually transmitted diseases (Morris 1997) the data tend to be large and very incomplete, ....
Wasserman, S., and Pattison, P. (1996), \Logit Models and Logistic Regressions for Social Networks. 1. An Introduction to Markov Graphs and p," Psychometrika, 61, 401-425.
A Latent Mixed Membership Model for Relational Data - Edoardo Airoldi David (Correct)
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S. Wasserman and P. Pattison. Logit models and logistic regression for social networks: I. an introduction to markov graphs and p
Latent Structure in Multiplex Relations - Carter Butts Social (Correct)
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Pattison, P. and Wasserman, S. (1999). Logit models and logistic regressions for social networks: II. multivariate relations. British Journal of Mathematical and Statistical Psychology, 52:169--193.
Finding Underlying Connections: - Fast Graph-Based Method (2003) (Correct)
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Wasserman, S., & Pattison, P. (1996). Logit models and logistic regression for social networks: I. an introduction to markov graphs and p*. Psychometrika, 60, 401--425.
cGraph: A Fast Graph-Based Method for Link Analysis and.. - Kubica, Moore, Cohn.. (2003) (1 citation) (Correct)
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Stanley Wasserman and Philippa Pattison. Logit models and logistic regression for social networks: I. an introduction to markov graphs and p*. Psychometrika, 60:401--425, 1996.
Finding Underlying Connections: - Fast Graph-Based Method (Correct)
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Wasserman, S., & Pattison, P. (1996). Logit models and logistic regression for social networks: I. an introduction to markov graphs and p*. Psychometrika, 60, 401--425.
A Latent Mixed Membership Model for Relational Data - Edoardo Airoldi David (Correct)
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S. Wasserman and P. Pattison. Logit models and logistic regression for social networks: I. an introduction to markov graphs and p
Building Inferentially Tractable Models of Complex Social Systems: .. - Butts (2005) (Correct)
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Wasserman, S. and Pattison, P. (1996). Logit models and logistic regressions for social networks: I. an introduction to Markov graphs and p#. Psychometrika, 60:401--426.
Building Inferentially Tractable Models of Complex Social Systems: .. - Butts (2005) (Correct)
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Robins, G., Pattison, P., and Wasserman, S. (1999). Logit models and logistic regressions for social networks, III. valued relations. Psychometrika, 64:371--394.
Building Inferentially Tractable Models of Complex Social Systems: .. - Butts (2005) (Correct)
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Pattison, P. and Wasserman, S. (1999). Logit models and logistic regressions for social networks: II. multivariate relations. British Journal of Mathematical and Statistical Psychology, 52:169--193.
Permutation Models for Relational Data - Butts (2005) (Correct)
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Wasserman, S. and Pattison, P. (1996). Logit models and logistic regressions for social networks: I. an introduction to Markov graphs and p#. Psychometrika, 60:401--426.
Permutation Models for Relational Data - Butts (2005) (Correct)
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Robins, G., Pattison, P., and Wasserman, S. (1999). Logit models and logistic regressions for social networks, III. valued relations. Psychometrika, 64:371--394.
Permutation Models for Relational Data - Butts (2005) (Correct)
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Pattison, P. and Wasserman, S. (1999). Logit models and logistic regressions for social networks: II. multivariate relations. British Journal of Mathematical and Statistical Psychology, 52:169--193.
The Structure and Function of Complex Networks - Newman (2003) (26 citations) (Correct)
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Wasserman, S. and Pattison, P., Logit models and logistic regressions for social networks: I. An introduction to Markov random graphs and p , Psychometrika 61, 401--426 (1996).
cGraph: A Fast Graph-Based Method for Link Analysis and.. - Kubica, Moore, Cohn.. (2003) (1 citation) (Correct)
No context found.
Stanley Wasserman and Philippa Pattison. Logit models and logistic regression for social networks: I. an introduction to markov graphs and p*. Psychometrika, 60:401--425, 1996.
Link Prediction in Relational Data - Taskar, Wong, Abbeel, Koller (2003) (4 citations) (Correct)
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S. Wasserman and P. Pattison. Logit models and logistic regression for social networks. Psychometrika, 61(3):401--425, 1996.
Conditional Maximum Likelihood Estimation under Various.. - Snijders, van Duijn (Correct)
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Wasserman, S., and Pattison, P. (1996). Logit models and logistic regression for social networks: I. An introduction to Markov graphs and p#. Psychometrika, 61, 401 - 425. 18
Conditional Maximum Likelihood Estimation under Various.. - Snijders, van Duijn (Correct)
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Robins, G., Pattison, P. and Wasserman, S. (1999). Logit models and logistic regressions for social networks, III. Valued relations. Psychometrika, 64, 371 - 394.
Conditional Maximum Likelihood Estimation under Various.. - Snijders, van Duijn (Correct)
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Pattison, P., andWasserman, S. (1999). Logit models and logistic regressions for social networks: II. Multivariate relations. British Journal of Mathematical and Statistical Psychology, 52, 169 - 193.
Finding Clusters in Network Link Strength Data - Graves (1998) (1 citation) (Correct)
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Wasserman, S., and Pattison, P. (1996), "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p ," Psychometrika, 60, 401-426.
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