@article{ObrienHeft,
  author ={R. G. OBrien and M. W. Heft},
title  ={New Discrimination Indexes and Models for Studying Sensory Functioning in Aging},
journal ={Journal of Applied Statistics},
year ={1995},
volume ={22},
pages ={9-27},
}

@incollection{Zellner1986,
 author      = "Arnold Zellner",
  title       = "On Assessing Prior Distributions and Bayesian Regression Analysis with
                  g-Prior Distributions",
  editor      = "P. Goel and Arnold Zellner",
  booktitle   = "Bayesian Inference and Decision Techniques: Essays in Honor of
                       Bruno de Finetti",
  publisher   = "Elsevier Science Publishers, Inc.",
  year        = 1986,
  pages       = "233-243",

@book{tires,
  title       = "The Design and Analysis of Industrial Experiments",
  author      = "O. L. Davies",
  publisher   = {Oliver and Boyd},
  year        = 1954,
  address = "London"
}


@article{OHaganfbfs,
  author    = {Anthony O'Hagan},
  title     = {Fractional Bayes Factors for Model Comparison},
  journal   = {Journal of the Royal Statistical Society. Series B (Methodological)},
  year      = {1995},
  volume    = {57},
  number    = {1},
  pages     = {99-138},
}
  issn      = {00359246},
  abstract  = {Bayesian comparison of models is achieved simply by calculation of posterior probabilities of the models themselves. However, there are difficulties with this approach when prior information about the parameters of the various models is weak. Partial Bayes factors offer a resolution of the problem by setting aside part of the data as a training sample. The training sample is used to obtain an initial informative posterior distribution of the parameters in each model. Model comparison is then based on a Bayes factor calculated from the remaining data. Properties of partial Bayes factors are discussed, particularly in the context of weak prior information, and they are found to have advantages over other proposed methods of model comparison. A new variant of the partial Bayes factor, the fractional Bayes factor, is advocated on grounds of consistency, simplicity, robustness and coherence.},
  publisher = {[Royal Statistical Society, Wiley]},
  url       = {http://www.jstor.org/stable/2346088},
}

@article{torque,
  author ={G.E. Meek and C.O. Ozgur},
title  ={Torque Variation Analysis},
journal ={Journal of the Industrial Mathematics Society},
year ={1991},
volume ={41},
pages ={1-16},
}

@article{franckdogs,
  author  = {Franck, Christopher T. and Nielsen, Dahlia M. and Osborne, Jason A.},
  title   = {A method for detecting hidden additivity in two-factor unreplicated experiments},
  journal = {Computational Statistics \& Data Analysis},
  year    = {2013},
  volume  = {67},
  number  = {Supplement C},
  pages   = {95 - 104},
  issn    = {0167-9473},
  doi     = {https://doi.org/10.1016/j.csda.2013.05.002},
}

@Article{Furry,
  author  ={M. S. Furry},
  title   ={Breaking strength, elongation and folding endurance of films of starches and gelatin used in sizing},
  journal ={Technical Bulletin (United States Department of Agriculture)},
  year    ={1939},
  volume  ={674},
  pages   ={1-36},
}

@article{Liangetal,
  author    = {Feng Liang and Rui Paulo and German Molina and Merlise A Clyde and Jim O Berger},
  title     = {Mixtures of g Priors for Bayesian Variable Selection},
  journal   = {Journal of the American Statistical Association},
  year      = {2008},
  volume    = {103},
  number    = {481},
  pages     = {410-423},
  doi       = {10.1198/016214507000001337},
  eprint    = {https://doi.org/10.1198/016214507000001337},
  publisher = {Taylor \& Francis},
  url       = {
        https://doi.org/10.1198/016214507000001337
    },
}

@Article{Zellner1980,
author="Zellner, A.
and Siow, A.",
title="Posterior odds ratios for selected regression hypotheses",
journal="Trabajos de Estadistica y de Investigacion Operativa",
year="1980",
month="Feb",
day="01",
volume="31",
number="1",
pages="585--603",
abstract="Bayesian posterior odds ratios for frequently encountered hypotheses about parameters of the normal linear multiple regression model are derived and discussed. For the particular prior distributions utilized, it is found that the posterior odds ratios can be well approximated by functions that are monotonic in usual sampling theoryF statistics. Some implications of this finding and the relation of our work to the pioneering work of Jeffreys and others are considered. Tabulations of odds ratios are provided and discussed.",
issn="0041-0241",
doi="10.1007/BF02888369",
url="https://doi.org/10.1007/BF02888369"
}

@ARTICLE{metzger2019,
       author = {{Metzger}, Thomas A. and {Franck}, Christopher T.},
        title = "{Detection of latent heteroscedasticity and group-based regression effects in linear models via Bayesian model selection}",
      journal = {arXiv e-prints},
     keywords = {Statistics - Methodology, Statistics - Applications},
         year = "2019",
        month = "Mar",
}

@Manual{Rlanguage,
   title = {R: A Language and Environment for Statistical Computing},
   author = {{R Core Team}},
   organization = {R Foundation for Statistical Computing},
   address = {Vienna, Austria},
   year = {2013},
   url = {http://www.R-project.org/},
 }

@Manual{slgf,
   title = {slgf: Bayesian Model Selection in the Presence of Latent Grouping Factors},
   author = {Thomas A. Metzger},
   year = {2019},
   note = {R package version 0.0.1},
   url = {https://CRAN.R-project.org/package=slgf},
  }

@article{francktechnometrics,
author = {Christopher T. Franck},
title = {Detection of Hidden Additivity and Inference Under Model Uncertainty for Unreplicated Factorial Studies via Bayesian Model Selection and Averaging},
journal = {Technometrics},
volume = {0},
number = {0},
pages = {1-21},
year  = {2019},
publisher = {Taylor & Francis},
doi = {10.1080/00401706.2018.1518791},

URL = {
        https://doi.org/10.1080/00401706.2018.1518791

},
eprint = {
        https://doi.org/10.1080/00401706.2018.1518791
}
}

@article{FranckRJ,
  author  = {Christopher T. Franck and Jason A. Osborne},
  title   = {{Exploring Interaction Effects in Two-Factor Studies using the hiddenf Package in R.}},
  journal = {{The R Journal}},
  year    = {2016},
  volume  = {8},
  number  = {1},
  pages   = {159--172},
  url     = {https://journal.r-project.org/archive/2016/RJ-2016-011/index.html},
}


@article{bottles,
  author  = {E. R. Ott and R. D. Snee},
  title   = {{Identifying useful differences in a multiple-head machine}},
  journal = {{Journal of Qualitym Technology}},
  year    = {1973},
  volume  = {5},
  number  = {2},
  pages   = {2--13},
}
