<filename>book.bib
@Book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {<NAME>},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.org/knitr/},
}

@article{sobolindices,
author = {<NAME> and Sobol’, I.M.},
year = {1995},
month = {01},
pages = {},
title = {Sensitivity analysis for nonlinear mathematical models: Numerical experience},
volume = {7},
journal = {Matematicheskoe Modelirovanie}
}

@article{10.1093/aje/kwt212,
    author = {<NAME>},
    title = "{Selecting an Appropriate Caliper Can Be Essential for Achieving Good Balance With Propensity Score Matching}",
    journal = {American Journal of Epidemiology},
    volume = {179},
    number = {2},
    pages = {226-235},
    year = {2013},
    month = {10},
    abstract = "{Matching on the propensity score is widely used to estimate the effect of an exposure in observational studies. However, the quality of the matches can be affected by decisions made during the matching process, particularly the order in which subjects are selected for matching and the maximum permitted difference between matched subjects (the “caliper”). This study used simulations to explore the effects of these decisions on both the imbalance of covariates and the closeness of matching, while allowing the numbers of potential matches and strengths of association between the confounding variable and the exposure to vary. It was found that, without a caliper, substantial bias was possible, particularly with a relatively small reservoir of potential matches and strong confounder-exposure association. Use of the recommended caliper reduced the bias considerably, but bias remained if subjects were selected by increasing or decreasing propensity score. A tighter caliper led to greatly reduced bias and closer matches, although some subjects could not be matched. This study suggests that a narrow caliper can improve the performance of propensity score matching. In situations where it is impossible to find appropriate matches for all exposed subjects, it is better to select subjects in order of the best available matches, rather than increasing or decreasing the propensity score.}",
    issn = {0002-9262},
    doi = {10.1093/aje/kwt212},
    url = {https://doi.org/10.1093/aje/kwt212},
    eprint = {https://academic.oup.com/aje/article-pdf/179/2/226/17341431/kwt212.pdf},
}


@article{DBLP:journals/corr/NickelK17,
  author    = {<NAME> and
               <NAME>},
  title     = {Poincar{\'{e}} Embeddings for Learning Hierarchical Representations},
  journal   = {CoRR},
  volume    = {abs/1705.08039},
  year      = {2017},
  url       = {http://arxiv.org/abs/1705.08039},
  archivePrefix = {arXiv},
  eprint    = {1705.08039},
  timestamp = {Mon, 13 Aug 2018 16:47:24 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/NickelK17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1806-03417,
  author    = {<NAME> and
               <NAME>},
  title     = {Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic
               Geometry},
  journal   = {CoRR},
  volume    = {abs/1806.03417},
  year      = {2018},
  url       = {http://arxiv.org/abs/1806.03417},
  archivePrefix = {arXiv},
  eprint    = {1806.03417},
  timestamp = {Mon, 13 Aug 2018 16:49:06 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1806-03417.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}