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bibliography.bib
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@article{Martin2021,
author = {Glen P. Martin and Matthew Sperrin and Kym I. E. Snell and Iain Buchan and Richard D. Riley},
doi = {10.1002/sim.8787},
issn = {0277-6715},
issue = {2},
journal = {Statistics in Medicine},
month = {1},
pages = {498-517},
title = {Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches},
volume = {40},
year = {2021},
}
@article{Bai2020,
abstract = {<p>In clinical research, study outcomes usually consist of various patients’ information corresponding to the treatment. To have a better understanding of the effects of different treatments, one often needs to analyze multiple clinical outcomes simultaneously, while the data are usually mixed with both continuous and discrete variables. We propose the multivariate mixed response model to implement statistical inference based on the conditional grouped continuous model through a pairwise composite-likelihood approach. It can simplify the multivariate model by dealing with three types of bivariate models and incorporating the asymptotical properties of the composite likelihood via the Godambe information. We demonstrate the validity and the statistic power of the multivariate mixed response model through simulation studies and clinical applications. This composite-likelihood method is advantageous for statistical inference on correlated multivariate mixed outcomes.</p>},
author = {Hao Bai and Yuan Zhong and Xin Gao and Wei Xu},
doi = {10.3390/stats3030016},
issn = {2571-905X},
issue = {3},
journal = {Stats},
month = {7},
pages = {203-220},
title = {Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method},
volume = {3},
year = {2020},
}
@article{Davenport2018,
author = {Clemontina A. Davenport and Arnab Maity and Patrick F. Sullivan and Jung-Ying Tzeng},
doi = {10.1007/s12561-017-9189-9},
issn = {1867-1764},
issue = {1},
journal = {Statistics in Biosciences},
month = {4},
pages = {117-138},
title = {A Powerful Test for SNP Effects on Multivariate Binary Outcomes Using Kernel Machine Regression},
volume = {10},
year = {2018},
}
@article{CAREY1993,
author = {Vicent Carey and Schott L. Zeger and Peter Diggle},
doi = {10.1093/biomet/80.3.517},
issn = {0006-3444},
issue = {3},
journal = {Biometrika},
pages = {517-526},
title = {Modelling multivariate binary data with alternating logistic regressions},
volume = {80},
year = {1993},
}
@article{TeixeiraPinto2009,
author = {Armando Teixeira-Pinto and Sharon-Lise T. Normand},
doi = {10.1002/sim.3588},
issn = {02776715},
issue = {13},
journal = {Statistics in Medicine},
month = {6},
pages = {1753-1773},
title = {Correlated bivariate continuous and binary outcomes: Issues and applications},
volume = {28},
year = {2009},
}
@article{Holland1981,
author = {Holland, Paul W. and Leinhardt, Samuel},
doi = {10.2307/2287037},
journal = {Journal of the American Statistical Association},
keywords = {generalized iterative scaling,networks,random digraphs,sociome-,try},
number = {373},
pages = {33--50},
title = {{An exponential family of probability distributions for directed graphs}},
volume = {76},
year = {1981}
}
@article{Frank1986,
abstract = {Log-linear statistical models are used to characterize ran- dom graphs with general dependence structure and with Markov dependence. Sufficient statistics for Markov graphs are shown to be given by counts of various triangles and stars. In particular, we show under which assumptions the triad counts are sufficient statistics. We discuss inference methodology for some simple Markov graphs.},
author = {Frank, O and Strauss, David},
doi = {10.2307/2289017},
journal = {Journal of the American Statistical Association},
keywords = {log-linear network model,markov field},
mendeley-groups = {network dependence,ergms},
number = {395},
pages = {832--842},
pmid = {7439394},
title = {{Markov graphs}},
url = {http://amstat.tandfonline.com/doi/abs/10.1080/01621459.1986.10478342},
volume = {81},
year = {1986}
}
@article{Wasserman1996,
author = {Wasserman, Stanley and Pattison, Philippa},
doi = {10.1007/BF02294547},
journal = {Psychometrika},
keywords = {categorical data analysis,random graphs,social network analysis},
number = {3},
pages = {401--425},
pmid = {10613111},
title = {{Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*}},
volume = {61},
year = {1996}
}
@article{Snijders2006,
author = {Snijders, Tom A B and Pattison, Philippa E and Robins, Garry L and Handcock, Mark S},
doi = {10.1111/j.1467-9531.2006.00176.x},
issn = {0081-1750},
journal = {Sociological Methodology},
month = {12},
number = {1},
pages = {99--153},
title = {{New specifications for exponential random graph models}},
url = {http://www.jstor.org/stable/25046693 http://smx.sagepub.com/lookup/doi/10.1111/j.1467-9531.2006.00176.x},
volume = {36},
year = {2006}
}
@article{Robins2007,
author = {Robins, Garry and Pattison, Pip and Kalish, Yuval and Lusher, Dean},
doi = {10.1016/j.socnet.2006.08.002},
journal = {Social Networks},
keywords = {Exponential random graph models,Statistical models for social networks,p* models},
number = {2},
pages = {173--191},
pmid = {18449326},
title = {{An introduction to exponential random graph (p*) models for social networks}},
volume = {29},
year = {2007}
}
@Manual{handcock2023,
author = {Mark S. Handcock and David R. Hunter and Carter T. Butts and Steven M. Goodreau and Pavel N. Krivitsky and Martina Morris},
title = {ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks},
organization = {The Statnet Project (\url{https://statnet.org})},
year = {2023},
note = {R package version 4.5.0},
url = {https://CRAN.R-project.org/package=ergm},
}
@Article{ergmpkg,
title = {{ergm}: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks},
author = {David R. Hunter and Mark S. Handcock and Carter T. Butts and Steven M. Goodreau and Martina Morris},
journal = {Journal of Statistical Software},
year = {2008},
volume = {24},
number = {3},
pages = {1--29},
doi = {10.18637/jss.v024.i03},
}
@Misc{defmarxiv,
title = {{Discrete Exponential-Family Models for Multivariate Binary Outcomes}},
author = {George {Vega Yon} and Thomas Valente and Mary Jo Pugh},
year = {{2022}},
archiveprefix = {{arXiv}},
archiveprefix = {{arXiv}},
primaryclass = {{stat.ME}},
doi = {10.48550/arXiv.2211.00627},
}
@Manual{R,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2023},
url = {https://www.R-project.org/},
}