
@Article{	  kolassa97,
  journal	= "Scandinavian Journal of Statistics",
  volume	= "24",
  doi		= "10.1111/1467-9469.00078",
  pages		= "523--530",
  author	= "Kolassa, J.E.",
  year		= "1997",
  title		= "Infinite Parameter Estimates in Logistic Regression"
}

@Article{	  kolassa16,
  journal	= "Advances in Pure Mathematics",
  volume	= "6",
  pages		= "331-341",
  author	= "Kolassa, J.E.",
  year		= "2016",
  title		= "Inference in the Presence of Likelihood Monotonicity for
		  Polytomous and Logistic Regression",
  doi		= "10.4236/apm.2016.65024"
}

@Misc{		  bes,
  title		= {The British Election Study},
  author	= {David J Sanders and Paul F Whiteley and Harold D Clarke
		  and Marianne Stewart and Kristi Winters},
  url		= {https://www.britishelectionstudy.com},
  language	= {English},
  year		= {2007},
  publisher	= {University of Essex}
}

@Article{	  mehtapatel,
  jstor_articletype={research-article},
  title		= {Computing Distributions for Exact Logistic Regression},
  author	= {Hirji, Karim F. and Mehta, Cyrus R. and Patel, Nitin R.},
  journal	= {Journal of the American Statistical Association},
  jstor_issuetitle={},
  volume	= {82},
  number	= {400},
  jstor_formatteddate={Dec., 1987},
  pages		= {pp. 1110-1117},
  issn		= {01621459},
  doi		= {10.2307/2289388},
  abstract	= {Logistic regression is a commonly used technique for the
		  analysis of retrospective and prospective epidemiological
		  and clinical studies with binary response variables.
		  Usually this analysis is performed using large sample
		  approximations. When the sample size is small or the data
		  structure sparse, the accuracy of the asymptotic
		  approximations is in question. On other occasions,
		  singularity of the covariance matrix of parameter estimates
		  precludes asymptotic analysis. Under these circumstances,
		  use of exact inferential procedures would seem to be a
		  prudent alternative. Cox (1970) showed that exact inference
		  on the parameters of a logistic model with binary response
		  requires consideration of the distribution of sufficient
		  statistics for these parameters. To date, however,
		  resorting to the exact method has not been computationally
		  feasible except in a few special situations. This article
		  presents an efficient recursive algorithm that generates
		  the joint and conditional distributions of the sufficient
		  statistics and thus makes it feasible to perform exact
		  inference for a much wider range of situations. Various
		  methods of improving the efficiency of the basic algorithm,
		  such as the application of appropriate criteria to delete
		  infeasible vectors, recording covariates, sorting
		  observations by covariate values, and use of a two-step
		  recursive procedure, are also described. The algorithm
		  given in this article enables the data analyst to perform
		  exact inference for models with or without interaction
		  terms and for matched as well as unmatched designs. Exact
		  analysis proposed by Cox (1970) was restricted to a single
		  parameter. Since our algorithm can be used to generate any
		  combination of joint and conditional distributions of the
		  sufficient statistics, it paves the way for multiparametric
		  exact inference. Further, this algorithm also provides a
		  tool for comparing exact and asymptotic inferential
		  procedures. Such comparisons would, it is hoped, provide
		  statisticians with guidelines stating when each of the
		  procedures should be preferred.},
  language	= {English},
  year		= {1987},
  publisher	= {American Statistical Association},
  copyright	= {Copyright © 1987 American Statistical Association}
}

@Article{	  goorinetal87,
  author	= {Goorin, A.M. and Perez--Atayde, A. and Gebhardt, M. and
		  Andersen, J.},
  year		= {1987},
  doi		= {10.1200/JCO.1987.5.8.1178},
  title		= {Weekly High--Dose Methotrexate and Doxorubicin for
		  Osteosarcoma: The Dana--Farber Cancer Institute/The
		  Children's Hospital -- Study III},
  journal	= {Journal of Clinical Oncology}
}

@Misc{		  kz19,
  author	= "John E. Kolassa and Juan Zhang",
  year		= {2019},
  howpublished	= {\url{https://higherlogicdownload.s3.amazonaws.com/AMSTAT/fa4dd52c-8429-41d0-abdf-0011047bfa19/UploadedImages/NCB_Conference/Presentations/2019/kolassa_toxslides.pdf}},
  note		= {Accessed: 2019-07-14}
}
