modelsel.bib

@article{Merkle+Furr+Rabe-Hesketh:2018,
  title={Bayesian comparison of latent variable models: {C}onditional vs marginal likelihoods},
  author={Merkle, Edgar C and Furr, Daniel and Rabe-Hesketh, Sophia},
  journal={arXiv preprint arXiv:1802.04452},
  year={2018},
  url={https://arxiv.org/abs/1802.04452}
}

@article{betancourt2017conceptual,
  title={A conceptual introduction to {Hamiltonian} {Monte} {Carlo}},
  author={Betancourt, Michael},
  journal={arXiv preprint arXiv:1701.02434},
  year={2017},
  url={https://arxiv.org/abs/1701.02434}
}

@article{Gelfand+etal:1990,
  title={Illustration of {Bayesian} inference in normal data models using {Gibbs} sampling},
  author={Gelfand, Alan E and Hills, Susan E and Racine-Poon, Amy and Smith, Adrian FM},
  journal={Journal of the American Statistical Association},
  volume={85},
  number={412},
  pages={972--985},
  year={1990}
}

@Article{Burkner+Gabry+Vehtari:LFO-CV:2019,
  author = 	 {Paul-Christian B{\"u}rkner and Jonah Gabry and Aki Vehtari},
  title = 	 {Approximate leave-future-out cross-validation for time series models},
  journal = 	 {arXiv preprint arXiv:1902.06281},
  year = 	 {2019},
  url =          {https://arxiv.org/abs/1902.06281}
}

@article{Burkner+Gabry+Vehtari:LFO-CV:2020,
  title={Approximate leave-future-out cross-validation for {Bayesian} time series models},
  author={B{\"u}rkner, Paul-Christian and Gabry, Jonah and Vehtari, Aki},
  journal={Journal of Statistical Computation and
   Simulation},
   volume={90},
   nuber={14},
   pages={2499-2523},
  year={2020}
}

@ARTICLE{Betancourt2013,
  title         = "Hamiltonian Monte Carlo for Hierarchical Models",
  author        = "Betancourt, M J and Girolami, Mark",
  abstract      = "Hierarchical modeling provides a framework for modeling the
                   complex interactions typical of problems in applied
                   statistics. By capturing these relationships, however,
                   hierarchical models also introduce distinctive pathologies
                   that quickly limit the efficiency of most common methods of
                   in- ference. In this paper we explore the use of Hamiltonian
                   Monte Carlo for hierarchical models and demonstrate how the
                   algorithm can overcome those pathologies in practical
                   applications.",
  year          =  2013,
  archivePrefix = "arXiv",
  primaryClass  = "stat.ME",
  eprint        = "1312.0906"
}

@article{Stan:JSS:2017,
   author = {Bob Carpenter and Andrew Gelman and Matthew Hoffman and Daniel Lee and Ben Goodrich and Michael Betancourt and Marcus Brubaker and Jiqiang Guo and Peter Li and Allen Riddell},
   title = {Stan: A Probabilistic Programming Language},
   journal = {Journal of Statistical Software, Articles},
   volume = {76},
   number = {1},
   year = {2017},
   keywords = {probabilistic programming; Bayesian inference; algorithmic differentiation; Stan},
   abstract = {Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.},
   issn = {1548-7660},
   pages = {1--32},
   doi = {10.18637/jss.v076.i01},
   url = {https://www.jstatsoft.org/v076/i01}
}

@article{Kucukelbir+etal:ADVI:2017,
    title={Automatic differentiation variational inference},
    author={Kucukelbir, Alp and Tran, Dustin and Ranganath, Rajesh and Gelman, Andrew and Blei, David M},
    journal={The Journal of Machine Learning Research},
    volume={18},
    pages={430--474},
    year={2017},
    url={http://jmlr.org/papers/v18/16-107.html}
  }


@article{betancourt2017conceptual,
  title={A conceptual introduction to {Hamiltonian Monte Carlo}},
  author={Betancourt, Michael},
  journal={arXiv preprint arXiv:1701.02434},
  year={2017},
  url ={https://arxiv.org/abs/1701.02434}
}

@book{BDA3,
title={Bayesian Data Analysis, third edition},
author={Gelman, Andrew and Carlin, John B. and Stern, Hal S. and Dunson, David B. and Vehtari, Aki and Rubin, Donald B.},
publisher={CRC Press},
year={2013},
note={Book homepage http://www.stat.columbia.edu/~gelman/book/}}

@article{Gelman+Rubin:1992,
  title={Inference from iterative simulation using multiple sequences},
  author={Gelman, Andrew and Rubin, Donald B},
  journal={Statistical science},
  volume={7},
  number={4},
  pages={457--472},
  year={1992}
}

@article{Brooks+Gelman:1998,
author = { Stephen P.   Brooks  and  Andrew   Gelman },
title = {General Methods for Monitoring Convergence of Iterative Simulations},
journal = {Journal of Computational and Graphical Statistics},
volume = {7},
number = {4},
pages = {434-455},
year  = {1998}
}


@Misc{Stan.2.18,
  author = 	 {{Stan Development Team}},
  title = 	 {The {Stan} Core Library Version 2.18.0},
  year = 	 2018,
  url = {http://mc-stan.org}
}

@Misc{RStan.2.17,
  author = 	 {{Stan Development Team}},
  title = 	 {{RStan}: the {R} interface to {Stan}. {R} package Version 2.17.3},
  year = 	 2018,
  url = {http://mc-stan.org}
}

@Misc{RStanARM.2.17,
  author = 	 {{Stan Development Team}},
  title = 	 {{RStanArm}: {Bayesian} applied regression modeling via {Stan}. {R} package Version 2.17.4},
  year = 	 2018,
  url = {http://mc-stan.org}
}

@Misc{StanManual.2.18.0,
  author = 	 {{Stan Development Team}},
  title = 	 {Stan Modeling Language Users Guide and Reference Manual. Version 2.18.0},
  year = 	 2018,
  url = {http://mc-stan.org}
}

@Article{Geyer:1992,
  author = 	 {C J Geyer},
  title = 	 {Practical {Markov} Chain {Monte} {Carlo}},
  journal = 	 {Statistical Science},
  year = 	 {1992},
  volume = 	 {7},
  pages = 	 {473--483}
}

@InCollection{Geyer:2011,
  author = 	 {C J Geyer},
  title = 	 {Introduction to {Markov} chain {Monte} {Carlo}},
  booktitle = 	 {Handbook of Markov Chain Monte Carlo},
  publisher =    {CRC Press},
  year = 	 {2011},
  editor = 	 {S Brooks and A Gelman and G L Jones and X L Meng}
}

@article{Hoffman+Gelman:2014,
  author  = {Matthew D. Hoffman and Andrew Gelman},
  title   = {The {No-U-Turn} {Sampler}: Adaptively Setting Path Lengths in {Hamiltonian} {Monte} {Carlo}},
  journal = {Journal of Machine Learning Research},
  year    = {2014},
  volume  = {15},
  pages   = {1593-1623},
  url     = {http://jmlr.org/papers/v15/hoffman14a.html}
}

@article{Vehtari+etal:PSIS:2017,
  title={Pareto smoothed importance sampling},
  author={Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
  journal={arXiv preprint arXiv:1507.02646},
  year={2017},
  url={https://arxiv.org/abs/1507.02646}
}

@inproceedings{Piironen+Vehtari:GP-projection:2016,
    title = {Projection predictive model selection for {Gaussian} processes},
    pages = {1--6},
    booktitle = {2016 {IEEE} 26th International Workshop on Machine Learning for Signal Processing ({MLSP})},
    author = {Piironen, Juho and Vehtari, Aki},
    year = 2016,
}

@article{Catalina+etal:projpredgamms:2020,
  title={Projection Predictive Inference for Generalized Linear and Additive Multilevel Models},
  author={Alejandro Catalina and Paul-Christian Bürkner and Aki Vehtari},
  year={2020},
  journal={arxiv preprint:2010.06994}
}


@article{Vehtari+etal:PSIS:2019,
  title={Pareto smoothed importance sampling},
  author={Vehtari, Aki and Simpson, Daniel and Gelman, Andrew and Yao, Yuling and Gabry, Jonah},
  journal={arXiv preprint arXiv:1507.02646},
  year={2019},
  url={https://arxiv.org/abs/1507.02646v6}
}

@article{Piironen+etal:projpred:2018,
  title={Projective Inference in High-dimensional Problems: Prediction and Feature Selection},
  author={Piironen, Juho and Paasiniemi, Markus and Vehtari, Aki},
  journal={arXiv preprint arXiv:1810.02406},
  year={2018},
  url={https://arxiv.org/abs/1810.02406}
}

@article{Piironen+etal:projpred:2020,
author = {Piironen, Juho and Paasiniemi, Markus and Vehtari, Aki},
journal = {Electronic Journal of Statistics},
number = 1,
pages = 2155--2197,
title = {Projective inference in high-dimensional problems: Prediction and feature selection},
volume = 14,
year = 2020
}

@Article{Pavone+etal:2020,
title={Using reference models in variable selection},
author={Pavone, Federico and Piironen, Juho and B{\"u}rkner, Paul-Christian and Vehtari, Aki},
journal={arXiv preprint arXiv:2004.13118},
year={2020}
}

@article{Vehtari+etal:PSIS-LOO:2017,
  title={Practical {Bayesian} model evaluation using leave-one-out cross-validation and {WAIC}},
  author={Vehtari, Aki and Gelman, Andrew and Gabry, Jonah},
  journal={Statistics and Computing},
  volume={27},
  number={5},
  pages={1413--1432},
  year={2017},
  doi={10.1007/s11222-016-9696-4},
  url={https://arxiv.org/abs/1507.04544}
}

@article{Piironen+Vehtari:2017a,
	author = {Piironen, Juho and Vehtari, Aki},
	title = {Comparison of {Bayesian} predictive methods for model selection},
	year = {2017},
	journal = {Statistics and Computing},
	volume = {27},
	number = {3},
	pages = {711--735},
	doi = {10.1007/s11222-016-9649-y},
	url = {https://doi.org/10.1007/s11222-016-9649-y}
}

@MANUAL{stanmanual,
  title={Stan Modeling Language Users Guide and Reference Manual},
  author={{Stan Development Team}},
  note={Version 2.17.1},
  address={http://mc-stan.org},
  year={2018}
}

@inproceedings{Piironen+Vehtari:ISPC:2018,
	author = {Piironen, Juho and Vehtari, Aki},
	title = {Iterative supervised principal components},
	booktitle = {Proceedings of the 21st International Conference on Artificial Intelligence and Statistics},
	pages = {106--114},
	year = {2018},
	editor = {Amos Storkey and Fernando Perez-Cruz},
	volume = {84},
	series = {Proceedings of Machine Learning Research},
	url = {http://proceedings.mlr.press/v84/piironen18a.html}
}

@article{Piironen+Vehtari:RHS:2017,
	author = {Piironen, Juho and Vehtari, Aki},
	title = {Sparsity information and regularization in the horseshoe and other shrinkage priors},
	year = {2017},
	journal = {Electronic journal of Statistics},
	volume = {11},
	number = {2},
	pages = {5018--5051},
	doi = {10.1214/17-EJS1337SI},
	url = {https://projecteuclid.org/euclid.ejs/1513306866}
}

@article{Vehtari+Ojanen:2012,
	author = {Vehtari, Aki and Ojanen, Janne},
	title = {A survey of {B}ayesian predictive methods for model assessment, selection and comparison},
	year = {2012},
	journal = {Statistics Surveys},
	volume = {6},
	pages = {142--228},
	doi = {10.1214/12-SS102}
}

@article{Peltola+etal:finite:2012,
  title={Finite adaptation and multistep moves in the Metropolis-Hastings algorithm for variable selection in genome-wide association analysis},
  author={Peltola, Tomi and Marttinen, Pekka and Vehtari, Aki},
  journal={PloS one},
  volume={7},
  number={11},
  pages={e49445},
  year={2012},
  doi={doi.org/10.1371/journal.pone.0049445}
}

@article{Yao+etal:2018,
	author = {Yao, Yuling and Vehtari, Aki and Simpson, Daniel and Gelman, Andrew},
	journal = {Bayesian Analysis},
	number = {3},
	pages = {917--1003},
	title = {Using stacking to average {Bayesian} predictive distributions (with discussion)},
	doi = {10.1214/17-BA1091},
	volume = {13},
	year = {2018}
}

@Article{Vehtari+Lampinen:2002b,
 author	     = {Aki Vehtari and Jouko Lampinen},
 title	     = {Bayesian Model Assessment and Comparison Using
                Cross-Validation Predictive Densities},
 journal     = {Neural Computation},
 year	     = {2002},
 volume      = {14},
 number      = {10},
 pages       = {2439-2468}
}

@Article{Kalliomaki+Vehtari+Lampinen:2005,
 author	     = {Ilkka Kalliom{\"a}ki and Aki Vehtari and Jouko
                Lampinen},
 title	     = {Shape analysis of concrete aggregates for
                statistical quality modeling},
 journal     = {Machine Vision and Applications},
 year	     = {2005},
 volume	     = {16},
 number	     = {3},
 pages	     = {197--201}
}

@article{Navarro:2019:between,
  title={Between the devil and the deep blue sea: Tensions between scientific judgement and statistical model selection},
  author={Navarro, Danielle J},
  journal={Computational Brain \& Behavior},
  volume={2},
  number={1},
  pages={28--34},
  year={2019}
}

@article{Sivula+etal:2020:loo_uncertainty,
  title={Uncertainty in {B}ayesian Leave-One-Out Cross-Validation Based Model Comparison},
  author={Sivula, Tuomas and Magnusson, M{\aa}ns and Vehtari, Aki},
  journal={arXiv:2008.10296},
  year={2020}
}

@article{Gelman+etal:2019:BayesR2,
author = {Andrew Gelman and Ben Goodrich and Jonah Gabry and Aki Vehtari},
title = {R-squared for {Bayesian} Regression Models},
journal = {The American Statistician},
volume = {73},
number = {3},
pages = {307-309},
year  = {2019}
}

@Article{Paananen+etal:2021:implicit,
  author = 	 {Topi Paananen and Juho Piironen and Paul-Christian B{\"u}rkner and Aki Vehtari},
  title = 	 {Implicitly adaptive importance sampling.},
  journal = 	 {Statistics and Computing},
  volume = 31,
  number = 16,
  year = 	 2021
}

@article{Tosh+etal:2021:piranha,
  title={The piranha problem: Large effects swimming in a small pond},
  author={Tosh, Christopher and Greengard, Philip and Goodrich, Ben and Gelman, Andrew and Vehtari, Aki and Hsu, Daniel},
  journal={arXiv preprint arXiv:2105.13445},
  year={2021}
}

@article{Yao:2021:hierstacking,
  title={Bayesian hierarchical stacking: Some models are (somewhere) useful},
  author={Yao, Yuling and Pirš, Gregor and Vehtari, Aki and Gelman, Andrew},
  journal={Bayesian Analysis},
  note = {doi:10.1214/21-BA1287},
  year={2021}
}

@Article{Geisser+Eddy:1979,
 author	     = {Seymour Geisser and William F. Eddy},
 title	     = {A Predictive Approach to Model Selection},
 journal     = {Journal of the American Statistical Association},
 year	     = {1979},
 volume	     = {74},
 number	     = {365},
 pages	     = {153--160}
}

@Book{Burnham+Anderson:2002,
 author	     = {Kenneth P. Burnham and David R. Anderson},
 title	     = {Model Selection and Multi-Model Inference: A
                Practical Information-Theoretic Approach},
 publisher   = {Springer},
 year	     = {2002},
 edition     = {2nd}
}

@article{Oelrich+etal:2020:overconfident,
  title={When are {Bayesian} model probabilities overconfident?},
  author={Oelrich, Oscar and Ding, Shutong and Magnusson, M{\aa}ns and Vehtari, Aki and Villani, Mattias},
  journal={arXiv preprint arXiv:2003.04026},
  year={2020}
}


@article{Gabry+etal:2019:visualization,
author = {Gabry, Jonah and Simpson, Daniel and Vehtari, Aki and Betancourt, Michael and Gelman, Andrew},
title = {Visualization in {Bayesian} workflow},
journal = {Journal of the Royal Statistical Society: Series A (Statistics in Society)},
volume = 182,
number = 2,
pages = {389-402}
year = 2019
}