document_biber.bib

@article{dawidMisleadingArgumentsInvolving1979,
  title = {Some {{Misleading Arguments Involving Conditional Independence}}},
  author = {Dawid, A. P.},
  date = {1979},
  journaltitle = {Journal of the Royal Statistical Society. Series B (Methodological)},
  volume = {41},
  number = {2},
  eprint = {2985039},
  eprinttype = {jstor},
  pages = {249--252},
  issn = {0035-9246},
  abstract = {Some misleading arguments in probability theory and statistics are examined, and shown to have a common structure, expressible in terms of conditional independence. Using an informal approach (which can be rigorized) the arguments are corrected by finding what conclusions are justified in general, and thus showing what further condition is needed to make the original arguments valid.},
}

@article{kalbfleischLikelihoodMethodsNonparametric1978,
  title = {Likelihood {{Methods}} and {{Nonparametric Tests}}},
  author = {Kalbfleisch, John D.},
  date = {1978},
  journaltitle = {Journal of the American Statistical Association},
  volume = {73},
  number = {361},
  eprint = {2286539},
  eprinttype = {jstor},
  pages = {167--170},
  issn = {0162-1459},
  doi = {10.2307/2286539},
  abstract = {Standard techniques of nonparametric statistics are related to likelihood procedures. In particular, rank tests and permutation tests in the regression problem are shown to be directly related to score function tests based on marginal and conditional likelihoods, respectively. The problem of estimating a population percentile is also examined from the viewpoint of marginal likelihood. These calculations tend to bring nonparametric procedures more closely in line with procedures adopted in parametric inference.},
}

@article{rubinNoteBayesianLikelihood1978,
  title = {A {{Note}} on {{Bayesian}}, {{Likelihood}}, and {{Sampling Distribution Inferences}}},
  author = {Rubin, Donald B.},
  date = {1978},
  journaltitle = {Journal of Educational Statistics},
  volume = {3},
  number = {2},
  eprint = {1164884},
  eprinttype = {jstor},
  pages = {189--201},
  issn = {0362-9791},
  doi = {10.2307/1164884},
  abstract = {A simple example is presented that illustrates advantages of Bayesian and likelihood methods of inference relative to sampling distribution methods of inference. It is argued that Bayesian and likelihood methods of inference should be utilized more generally to analyze real data. Sampling distributions should be used to evaluate the long term performance of procedures.},
}