references.bib

@article{ando2007bayesian,
  title         = {Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models},
  author        = {Ando, Tomohiro},
  journal       = {Biometrika},
  volume        = {94},
  number        = {2},
  pages         = {443--458},
  year          = {2007},
  publisher     = {Oxford University Press},
  doi           = {10.1093/biomet/asm017}
}
@article{baio2010bayesian,
  title         = {Bayesian hierarchical model for the prediction of football results},
  author        = {Baio, Gianluca and Blangiardo, Marta},
  journal       = {Journal of Applied Statistics},
  volume        = {37},
  number        = {2},
  pages         = {253--264},
  year          = {2010},
  publisher     = {Taylor \& Francis}
}
@article{bali2003gev,
  title         = {The generalized extreme value distribution},
  journal       = {Economics Letters},
  volume        = {79},
  number        = {3},
  pages         = {423--427},
  year          = {2003},
  issn          = {0165-1765},
  doi           = {https://doi.org/10.1016/S0165-1765(03)00035-1},
  url           = {https://www.sciencedirect.com/science/article/pii/S0165176503000351},
  author        = {Turan G. Bali}
}
@article{bauer2005probing,
  title         = {Probing interactions in fixed and multilevel regression: Inferential and graphical techniques},
  author        = {Bauer, Daniel J and Curran, Patrick J},
  journal       = {Multivariate behavioral research},
  volume        = {40},
  number        = {3},
  pages         = {373--400},
  year          = {2005},
  publisher     = {Taylor \& Francis}
}
@book{berry1996statistics,
  title         = {Statistics: a Bayesian perspective},
  author        = {Berry, Donald A},
  year          = {1996},
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}
@article{bertrand2004much,
  title         = {How much should we trust differences-in-differences estimates?},
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  journal       = {The Quarterly journal of economics},
  volume        = {119},
  number        = {1},
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}
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}
@inproceedings{caprani2009gev,
  title         = "Estimating extreme highway bridge traffic load effects",
  author        = {Colin C. Caprani and Eugene J. OBrien},
  year          = "2010",
  language      = "English",
  isbn          = "9780415475570",
  pages         = "1 -- 8",
  editor        = "Hitoshi Furuta and Frangopol, {Dan M} and Masanobu Shinozuka",
  booktitle     = "Proceedings of the 10th International Conference on Structural Safety and Reliability (ICOSSAR2009)",
  publisher     = "CRC Press",
  url           = {https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.6789\&rep=rep1\&type=pdf}
}
@article{caprani2010gev,
  title         = {The use of predictive likelihood to estimate the distribution of extreme bridge traffic load effect},
  journal       = {Structural Safety},
  volume        = {32},
  number        = {2},
  pages         = {138--144},
  year          = {2010},
  issn          = {0167-4730},
  doi           = {https://doi.org/10.1016/j.strusafe.2009.09.001},
  url           = {https://www.sciencedirect.com/science/article/pii/S016747300900071X},
  author        = {Colin C. Caprani and Eugene J. OBrien}
}
@misc{card1993minimum,
  title         = {Minimum wages and employment: A case study of the fast food industry in New Jersey and Pennsylvania},
  author        = {Card, David and Krueger, Alan B},
  year          = {1993},
  publisher     = {National Bureau of Economic Research Cambridge, Mass., USA}
}
@misc{carpenter2016hierarchical,
  title         = {Hierarchical partial pooling for repeated binary trials},
  author        = {Carpenter, Bob and Gabry, J and Goodrich, B},
  year          = {2016},
  publisher     = {Technical report. Retrieved from https://mc-stan. org/users/docum entat ion~\ldots{}}
}
@book{coles2001gev,
  title         = "An introduction to statistical modeling of extreme values",
  author        = "Coles, Stuart",
  publisher     = "Springer",
  series        = "Springer Series in Statistics",
  edition       = 2001,
  month         = aug,
  year          = 2001,
  address       = "London, England",
  language      = "en",
  isbn          = {978-1-85233-459-8},
  url           = {https://doi.org/10.1007/978-1-4471-3675-0}
}
@article{collinswilson2019,
  title         = {Ten simple rules for the computational modeling of behavioral data},
  author        = {Wilson, Robert C and Collins, Anne GE},
  editor        = {Behrens, Timothy E},
  volume        = 8,
  year          = 2019,
  month         = {nov},
  pub_date      = {2019-11-26},
  pages         = {e49547},
  citation      = {eLife 2019;8:e49547},
  doi           = {10.7554/eLife.49547},
  abstract      = {Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data.},
  keywords      = {computational modeling, model fitting, validation, reproducibility},
  journal       = {eLife},
  issn          = {2050-084X},
  publisher     = {eLife Sciences Publications, Ltd}
}
@book{cunningham2021causal,
  title         = {Causal inference: The Mixtape},
  author        = {Cunningham, Scott},
  year          = {2021},
  publisher     = {Yale University Press}
}
@article{efron1975data,
  title         = {Data analysis using Stein's estimator and its generalizations},
  author        = {Efron, Bradley and Morris, Carl},
  journal       = {Journal of the American Statistical Association},
  volume        = {70},
  number        = {350},
  pages         = {311--319},
  year          = {1975},
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}
@book{fox2010bayesian,
  title         = {Bayesian item response modeling: Theory and applications},
  author        = {Fox, Jean-Paul},
  year          = {2010},
  publisher     = {Springer}
}
@book{gelman2006data,
  title         = {Data analysis using regression and multilevel/hierarchical models},
  author        = {Gelman, Andrew and Hill, Jennifer},
  year          = {2006},
  publisher     = {Cambridge university press}
}
@article{gelman2006multilevel,
  title         = {Multilevel (hierarchical) modeling: what it can and cannot do},
  author        = {Gelman, Andrew},
  journal       = {Technometrics},
  volume        = {48},
  number        = {3},
  pages         = {432--435},
  year          = {2006},
  publisher     = {Taylor \& Francis}
}
@article{gelman2008scaling,
  title         = {Scaling regression inputs by dividing by two standard deviations},
  author        = {Gelman, Andrew},
  journal       = {Statistics in medicine},
  volume        = {27},
  number        = {15},
  pages         = {2865--2873},
  year          = {2008},
  publisher     = {Wiley Online Library},
  doi           = {10.1002/sim.3107}
}
@book{gelman2013bayesian,
  title         = {Bayesian Data Analysis},
  publisher     = {Chapman and Hall/CRC},
  author        = {Gelman, Andrew and Carlin, John B. and Stern, Hal S. and Dunson, David B. and Vehtari, Aki and Rubin, Donald B.},
  year          = {2013}
}
@article{gelman2020bayesian,
  title         = {Bayesian workflow},
  author        = {Gelman, Andrew and Vehtari, Aki and Simpson, Daniel and Margossian, Charles C and Carpenter, Bob and Yao, Yuling and Kennedy, Lauren and Gabry, Jonah and B{\"u}rkner, Paul-Christian and Modr{\'a}k, Martin},
  journal       = {arXiv preprint arXiv:2011.01808},
  year          = {2020},
  url           = {https://arxiv.org/abs/2011.01808}
}
@book{gelman2020regression,
  title         = {Regression and other stories},
  author        = {Gelman, Andrew and Hill, Jennifer and Vehtari, Aki},
  year          = {2020},
  publisher     = {Cambridge University Press}
}
@article{goldberg2001eigentaste,
  author        = {Ken Goldberg and Theresa Roeder and Chris Perkins},
  title         = {Eigentaste: A Constant Time Collaborative Filtering Algorithm},
  journal       = {Information Retrieval},
  year          = {2001},
  volume        = {4},
  pages         = {133--151}
}
@article{goodman1999,
  doi           = {10.7326/0003-4819-130-12-199906150-00008},
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  year          = {1999},
  month         = jun,
  publisher     = {American College of Physicians},
  volume        = {130},
  number        = {12},
  pages         = {995},
  author        = {Steven N. Goodman},
  title         = {Toward Evidence-Based Medical Statistics. 1: The P Value Fallacy},
  journal       = {Annals of Internal Medicine}
}
@article{google_causal_impact2015,
  title         = {Inferring causal impact using Bayesian structural time-series models},
  author        = {Kay H. Brodersen and Fabian Gallusser and Jim Koehler and Nicolas Remy and Steven L. Scott},
  year          = {2015},
  journal       = {Annals of Applied Statistics},
  pages         = {247--274},
  volume        = {9}
}
@misc{harper2015movielens,
  title         = {The MovieLens Datasets: History and Context},
  author        = {Harper, F. Maxwell and Konstan, Joseph A.},
  journal       = {ACM Transactions on Interactive Intelligent Systems},
  volumne       = {5},
  number        = {4},
  pages         = {1--19},
  year          = {2016},
  month         = {January},
  url           = {https://doi.org/10.1145/2827872}
}
@book{hayes2017introduction,
  title         = {Introduction to mediation, moderation, and conditional process analysis: A regression-based approach},
  author        = {Hayes, Andrew F},
  year          = {2017},
  publisher     = {Guilford publications}
}
@article{hoffman2014nuts,
  title         = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
  author        = {Hoffman, Matthew and Gelman, Andrew},
  year          = {2014},
  journal       = {Journal of Machine Learning Research},
  volume        = {15},
  issue         = {1},
  pages         = {1593--1623},
  url           = {https://dl.acm.org/doi/10.5555/2627435.2638586}
}
@misc{hogg2010data,
  title         = {Data analysis recipes: Fitting a model to data},
  author        = {David W. Hogg and Jo Bovy and Dustin Lang},
  year          = {2010},
  eprint        = {1008.4686},
  archiveprefix = {arXiv},
  primaryclass  = {astro-ph.IM}
}
@book{huntington2021effect,
  title         = {The effect: An introduction to research design and causality},
  author        = {Huntington-Klein, Nick},
  year          = {2021},
  publisher     = {Chapman and Hall/CRC}
}
@article{iacobucci2016mean,
  title         = {Mean centering helps alleviate ``micro'' but not ``macro'' multicollinearity},
  author        = {Iacobucci, Dawn and Schneider, Matthew J and Popovich, Deidre L and Bakamitsos, Georgios A},
  journal       = {Behavior research methods},
  volume        = {48},
  number        = {4},
  pages         = {1308--1317},
  year          = {2016},
  publisher     = {Springer}
}
@article{iacobucci2017mean,
  title         = {Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux},
  author        = {Iacobucci, Dawn and Schneider, Matthew J and Popovich, Deidre L and Bakamitsos, Georgios A},
  journal       = {Behavior research methods},
  volume        = {49},
  number        = {1},
  pages         = {403--404},
  year          = {2017},
  publisher     = {Springer}
}
@book{ivezić2014astroMLtext,
  author        = {\v{Z}eljko Ivezi\'{c} and Andrew J. Connolly and Jacob T. VanderPlas and Alexander Gray},
  doi           = {10.1515/9781400848911},
  title         = {Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data},
  year          = {2014},
  publisher     = {Princeton University Press},
  isbn          = {9781400848911}
}
@book{james2021statisticallearning,
  title         = {An Introduction to Statistical Learning},
  author        = {James, Gareth ad Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
  year          = {2021},
  publisher     = {Springer},
  doi           = {https://doi.org/10.1007/978-1-0716-1418-1},
  issn          = {1431-875X},
  isbn          = {978-1-0716-1420-4}
}
@article{johnson1999,
  doi           = {10.2307/3802789},
  url           = {https://doi.org/10.2307/3802789},
  year          = {1999},
  month         = jul,
  publisher     = {{JSTOR}},
  volume        = {63},
  number        = {3},
  pages         = {763},
  author        = {Douglas H. Johnson},
  title         = {The Insignificance of Statistical Significance Testing},
  journal       = {The Journal of Wildlife Management}
}
@misc{kingma2014autoencoding,
  title         = {Auto-Encoding Variational Bayes},
  author        = {Diederik P Kingma and Max Welling},
  year          = {2014},
  eprint        = {1312.6114},
  archiveprefix = {arXiv},
  primaryclass  = {stat.ML}
}
@article{koren2009matrixfactorization,
  author        = {Koren, Yehuda and Bell, Robert and Volinsky, Chris},
  journal       = {Computer},
  title         = {Matrix Factorization Techniques for Recommender Systems},
  year          = {2009},
  volume        = {42},
  number        = {8},
  pages         = {30--37},
  doi           = {10.1109/MC.2009.263}
}
@article{kruschke2011bayesian,
  title         = {Bayesian assessment of null values via parameter estimation and model comparison},
  author        = {Kruschke, John K},
  journal       = {Perspectives on Psychological Science},
  volume        = {6},
  number        = {3},
  pages         = {299--312},
  year          = {2011},
  publisher     = {Sage Publications Sage CA: Los Angeles, CA}
}
@article{kruschke2013,
  doi           = {10.1037/a0029146},
  url           = {https://doi.org/10.1037/a0029146},
  year          = {2013},
  publisher     = {American Psychological Association ({APA})},
  volume        = {142},
  number        = {2},
  pages         = {573--603},
  author        = {John K. Kruschke},
  title         = {Bayesian estimation supersedes the t test.},
  journal       = {Journal of Experimental Psychology: General}
}
@book{kruschke2014doing,
  title         = {Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan},
  author        = {Kruschke, John},
  year          = {2014},
  publisher     = {Academic Press}
}
@misc{kucukelbir2015automatic,
  title         = {Automatic Variational Inference in Stan},
  author        = {Alp Kucukelbir and Rajesh Ranganath and Andrew Gelman and David M. Blei},
  year          = {2015},
  eprint        = {1506.03431},
  archiveprefix = {arXiv},
  primaryclass  = {stat.ML}
}
@article{lewandowski2009generating,
  title         = {Generating random correlation matrices based on vines and extended onion method},
  author        = {Lewandowski, Daniel and Kurowicka, Dorota and Joe, Harry},
  journal       = {Journal of multivariate analysis},
  volume        = {100},
  number        = {9},
  pages         = {1989--2001},
  year          = {2009},
  publisher     = {Elsevier}
}
@book{martin2018bayesian,
  title         = {Bayesian analysis with Python: introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ},
  author        = {Martin, Osvaldo},
  year          = {2018},
  publisher     = {Packt Publishing Ltd}
}
@book{martin2021bayesian,
  title         = {Bayesian Modeling and Computation in Python},
  author        = {Martin, Osvaldo A and Kumar, Ravin and Lao, Junpeng},
  year          = {2021},
  publisher     = {Chapman and Hall/CRC},
  doi           = {10.1201/9781003019169}
}
@article{mcclelland2017multicollinearity,
  title         = {Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016)},
  author        = {McClelland, Gary H and Irwin, Julie R and Disatnik, David and Sivan, Liron},
  journal       = {Behavior research methods},
  volume        = {49},
  number        = {1},
  pages         = {394--402},
  year          = {2017},
  publisher     = {Springer}
}
@book{mcelreath2018statistical,
  title         = {Statistical rethinking: A Bayesian course with examples in R and Stan},
  author        = {McElreath, Richard},
  year          = {2018},
  publisher     = {Chapman and Hall/CRC}
}
@inproceedings{mnih2008advances,
  title         = {Probabilistic Matrix Factorization},
  author        = {Mnih, Andriy and Salakhutdinov, Russ R},
  booktitle     = {Advances in Neural Information Processing Systems},
  editor        = {J. Platt and D. Koller and Y. Singer and S. Roweis},
  publisher     = {Curran Associates, Inc.},
  url           = {https://proceedings.neurips.cc/paper/2007/file/d7322ed717dedf1eb4e6e52a37ea7bcd-Paper.pdf},
  volume        = {20},
  year          = {2008}
}
@misc{mnih2013playing,
  title         = {Playing Atari with Deep Reinforcement Learning},
  author        = {Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Alex Graves and Ioannis Antonoglou and Daan Wierstra and Martin Riedmiller},
  year          = {2013},
  eprint        = {1312.5602},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG}
}
@book{molnar2019,
  title         = {Interpretable Machine Learning},
  author        = {Christoph Molnar},
  year          = {2019},
  subtitle      = {A Guide for Making Black Box Models Explainable},
  url           = {https://christophm.github.io/interpretable-ml-book/},
  publisher     = {Christoph Molnar}
}
@article{nowlan1992simplifying,
  title         = {Simplifying Neural Networks By Soft Weight-Sharing},
  author        = {Nowlan, Steven J and Hinton, Geoffrey E},
  journal       = {Neural computation},
  volume        = {4},
  number        = {4},
  pages         = {473--493},
  year          = {1992},
  publisher     = {MIT Press}
}
@article{nuijten2015default,
  title         = {A default Bayesian hypothesis test for mediation},
  author        = {Nuijten, Mich{\`e}le B and Wetzels, Ruud and Matzke, Dora and Dolan, Conor V and Wagenmakers, Eric-Jan},
  journal       = {Behavior research methods},
  volume        = {47},
  number        = {1},
  pages         = {85--97},
  year          = {2015},
  publisher     = {Springer}
}
@misc{quiroga2022bart,
  title         = {Bayesian additive regression trees for probabilistic programming},
  author        = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
  publisher     = {arXiv},
  doi           = {10.48550/ARXIV.2206.03619},
  url           = {https://arxiv.org/abs/2206.03619},
  keywords      = {Computation (stat.CO), FOS: Computer and information sciences, FOS: Computer and information sciences},
  year          = {2022},
  copyright     = {Creative Commons Attribution Share Alike 4.0 International}
}
@book{rasmussen2003gaussian,
  title         = {Gaussian Processes for Machine Learning},
  author        = {Rasmussen, Carl Edward and Williams, Christopher K. I.},
  year          = {2006},
  isbn          = {026218253X},
  publisher     = {The MIT Press},
  url           = {https://gaussianprocess.org/gpml/}
}
@book{roback2021beyond,
  title         = {Beyond multiple linear regression: Applied generalized linear models and multilevel models in R},
  author        = {Roback, P., and Legler, J.},
  year          = {2021},
  publisher     = {CRC Press},
  url           = {https://bookdown.org/roback/bookdown-BeyondMLR/}
}
@inproceedings{salakhutdinov2008bayesian,
  title         = {Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo},
  author        = {Salakhutdinov, Ruslan and Mnih, Andriy},
  booktitle     = {Proceedings of the 25th international conference on Machine learning},
  pages         = {880--887},
  year          = {2008},
  volume        = {25}
}
@article{silver2016masteringgo,
  title         = {Mastering the game of Go with deep neural networks and tree search},
  author        = {D. Silver, A. Huang, C. Maddison et al.},
  journal       = {Nature},
  volume        = {529},
  pages         = {484--489},
  year          = {2016},
  url           = {https://doi.org/10.1038/nature16961}
}
@article{spiller2013spotlights,
  title         = {Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression},
  author        = {Spiller, Stephen A and Fitzsimons, Gavan J and Lynch Jr, John G and McClelland, Gary H},
  journal       = {Journal of marketing research},
  volume        = {50},
  number        = {2},
  pages         = {277--288},
  year          = {2013},
  publisher     = {SAGE Publications Sage CA: Los Angeles, CA}
}
@online{stucchio2015bayesian,
  title         = {Bayesian A/B Testing at VWO},
  author        = {Stucchio, Chris},
  year          = {2015},
  url           = {https://vwo.com/downloads/VWO\_SmartStats\_technical\_whitepaper.pdf}
}
@misc{szegedy2014going,
  title         = {Going Deeper with Convolutions},
  author        = {Christian Szegedy and Wei Liu and Yangqing Jia and Pierre Sermanet and Scott Reed and Dragomir Anguelov and Dumitru Erhan and Vincent Vanhoucke and Andrew Rabinovich},
  year          = {2014},
  eprint        = {1409.4842},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV}
}
@article{taylor2018forecasting,
  title         = {Forecasting at scale},
  author        = {Taylor, Sean J and Letham, Benjamin},
  journal       = {The American Statistician},
  volume        = {72},
  number        = {1},
  pages         = {37--45},
  year          = {2018},
  publisher     = {Taylor \& Francis},
  url           = {https://peerj.com/preprints/3190/}
}
@online{vandenbergSPSS,
  author        = {van den Berg, R. G},
  title         = {SPSS Moderation Regression Tutorial},
  url           = {https://www.spss-tutorials.com/spss-regression-with-moderation-interaction-effect/},
  urldate       = {2022-03-20}
}
@inproceedings{vanderplas2012astroML,
  author        = {{Vanderplas}, J.T. and {Connolly}, A.J. and {Ivezi{\'c}}, {\v Z}. and {Gray}, A.},
  booktitle     = {Conference on Intelligent Data Understanding (CIDU)},
  title         = {Introduction to astroML: Machine learning for astrophysics},
  month         = {oct.},
  pages         = {47--54},
  doi           = {10.1109/CIDU.2012.6382200},
  year          = {2012}
}
@book{wilkinson2005grammar,
  title         = {The Grammar of Graphics},
  author        = {Wilkinson, Leland},
  year          = {2005},
  publisher     = {Springer},
  doi           = {https://doi.org/10.1007/0-387-28695-0},
  issn          = {1431-8784},
  isbn          = {978-0-387-24544-7}
}
@article{wing2018designing,
  title         = {Designing difference in difference studies: best practices for public health policy research},
  author        = {Wing, Coady and Simon, Kosali and Bello-Gomez, Ricardo A},
  journal       = {Annu Rev Public Health},
  volume        = {39},
  number        = {1},
  pages         = {453--469},
  year          = {2018}
}
@article{Yao_2018,
  doi           = {10.1214/17-ba1091},
  url           = {https://doi.org/10.1214\%2F17-ba1091},
  year          = 2018,
  month         = {sep},
  publisher     = {Institute of Mathematical Statistics},
  volume        = {13},
  number        = {3},
  author        = {Yuling Yao and Aki Vehtari and Daniel Simpson and Andrew Gelman},
  title         = {Using Stacking to Average Bayesian Predictive Distributions (with Discussion)},
  journal       = {Bayesian Analysis}
}
@article{yuan2009bayesian,
  title         = {Bayesian mediation analysis.},
  author        = {Yuan, Ying and MacKinnon, David P},
  journal       = {Psychological methods},
  volume        = {14},
  number        = {4},
  pages         = {301},
  year          = {2009},
  publisher     = {American Psychological Association}
}
@article{zhang2017moderation,
  title         = {Moderation analysis with missing data in the predictors.},
  author        = {Zhang, Qian and Wang, Lijuan},
  journal       = {Psychological methods},
  volume        = {22},
  number        = {4},
  pages         = {649},
  year          = {2017},
  publisher     = {American Psychological Association}
}