book.bib

@article{huber50,
author = {J. Huber, Peter},
year = {2011},
month = {01},
pages = {},
title = {Data Analysis: What Can be Learned from the Past 50 Years},
doi = {10.1002/9781118018255}
}
@article{tukeyda,
author = "Tukey, John W.",
doi = "10.1214/aoms/1177704711",
fjournal = "The Annals of Mathematical Statistics",
journal = "Ann. Math. Statist.",
month = "03",
number = "1",
pages = "1--67",
publisher = "The Institute of Mathematical Statistics",
title = "The Future of Data Analysis",
url = "https://doi.org/10.1214/aoms/1177704711",
volume = "33",
year = "1962"
}
@article{donoho50,
author = {David Donoho},
title = {50 Years of Data Science},
journal = {Journal of Computational and Graphical Statistics},
volume = {26},
number = {4},
pages = {745-766},
year  = {2017},
publisher = {Taylor & Francis},
doi = {10.1080/10618600.2017.1384734},
URL = {https://doi.org/10.1080/10618600.2017.1384734},
eprint = {https://doi.org/10.1080/10618600.2017.1384734}
}
@book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.name/knitr/},
}
@book{wasserman,
 author = {Wasserman, Larry},
 title = {All of Statistics: A Concise Course in Statistical Inference},
 year = {2010},
 isbn = {1441923225, 9781441923226},
 publisher = {Springer Publishing Company, Incorporated},
} 
@book{ross,
  added-at = {2015-09-11T13:23:43.000+0200},
  address = {Upper Saddle River, N.J.},
  author = {Ross, Sheldon M.},
  biburl = {https://www.bibsonomy.org/bibtex/29325f1b3acbc545c7ef14d7fe58c8e28/ytyoun},
  edition = {Fifth},
  interhash = {38276e30c93b62798d658c41f33e037c},
  intrahash = {9325f1b3acbc545c7ef14d7fe58c8e28},
  isbn = {0137463146 9780137463145 013895772X 9780138957728},
  keywords = {probability ross textbook},
  publisher = {Prentice Hall},
  refid = {36900824},
  timestamp = {2016-06-12T12:45:30.000+0200},
  title = {A First Course in Probability},
  year = 1998
}
@Book{efron,
  Title                    = {An Introduction to the Bootstrap},
  Author                   = {Bradley Efron and Robert J. Tibshirani},
  Publisher                = {Chapman \& Hall/CRC},
  Year                     = {1993},

  Address                  = {Boca Raton, Florida, USA},
  Number                   = {57},
  Series                   = {Monographs on Statistics and Applied Probability}
}
@book{advr,
  title={Advanced R},
  author={Wickham, H.},
  isbn={0815384572},
  series={Chapman \& Hall/CRC The R Series},
  url={https://adv-r.hadley.nz},
  year={2019},
  edition = {2nd},
  publisher={Chapman and Hall/CRC}
}
@book{r4ds,
 author = {Wickham, Hadley and Grolemund, Garrett},
 title = {R for Data Science: Import, Tidy, Transform, Visualize, and Model Data},
 year = {2017},
 isbn = {1491910399, 9781491910399},
 edition = {1st},
 publisher = {O'Reilly Media, Inc.},
} 
@book{gelman-hill,
  added-at = {2010-03-02T17:25:53.000+0100},
  address = {New York},
  author = {Gelman, Andrew and Hill, Jennifer},
  biburl = {https://www.bibsonomy.org/bibtex/2977dbf8708e1f5ad2a321eb00ec08724/jrennstich},
  booktitle = {Data Analysis Using Regression and Multilevel/Hierarchical Models},
  date-modified = {2010-02-28 21:03:32 -0500},
  interhash = {51719b25389e0e96757c89f059207b1b},
  intrahash = {977dbf8708e1f5ad2a321eb00ec08724},
  keywords = {data methodology},
  pages = {xxii, 625 p},
  publisher = {Cambridge University Press},
  timestamp = {2010-03-07T08:28:08.000+0100},
  title = {Data analysis using regression and multilevel/hierarchical models},
  volume = {Analytical methods for social research},
  year = 2007
}
@book{gelman-bayesian,
  title={Bayesian Data Analysis, Third Edition},
  author={Gelman, A. and Carlin, J.B. and Stern, H.S. and Dunson, D.B. and Vehtari, A. and Rubin, D.B.},
  isbn={9781439840955},
  lccn={2013039507},
  series={Chapman \& Hall/CRC Texts in Statistical Science},
  url={https://books.google.com.mx/books?id=ZXL6AQAAQBAJ},
  year={2013},
  publisher={Taylor \& Francis}
}
@book{kruschke,
  added-at = {2016-12-29T09:25:25.000+0100},
  address = {Boston},
  author = {Kruschke, John},
  biburl = {https://www.bibsonomy.org/bibtex/277f97f6f84d077790b702e30ed86be5f/becker},
  interhash = {5e4043e24f7f58a8de076d7956ca08ea},
  intrahash = {77f97f6f84d077790b702e30ed86be5f},
  keywords = {diss imported inthesis mixedtrails},
  publisher = {Academic Press},
  timestamp = {2017-06-19T10:12:05.000+0200},
  title = {Doing Bayesian Data Analysis (Second Edition)},
  year = 2015
}
=======
>>>>>>> 8114df6e5186291d11fcdf43dfeb7bd2ff236f10
@book{tufte06,
  added-at = {2017-06-17T22:10:30.000+0200},
  address = {Cheshire, CT},
 author = {Tufte, Edward R.},
 biburl = {https://www.bibsonomy.org/bibtex/268b03ee21911d109ddab08424df2357a/flint63},
  file = {Graphics Press Product page:http\://www.edwardtufte.com/tufte/books_be:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/0961392177/:URL},
  groups = {public},
  interhash = {ce895fcb0083acbded29017b98331187},
  intrahash = {68b03ee21911d109ddab08424df2357a},
  isbn = {978-0-9613921-7-8},
  keywords = {01624 101 book shelf information graphics design cognitive science},
  publisher = {Graphics Press},
  timestamp = {2017-07-13T17:15:29.000+0200},
  title = {Beautiful Evidence},
  username = {flint63},
  year = 2006
}
@book{tufte86,
 author = {Tufte, Edward R.},
 title = {The Visual Display of Quantitative Information},
 year = {1986},
 isbn = {0-9613921-0-X},
 publisher = {Graphics Press},
 address = {Cheshire, CT, USA},
} 
@book{cleveland94,
  author={Cleveland, W.S.},
  title={The Elements of Graphing Data},
  isbn={9780963488411},
  lccn={94075052},
  url={https://books.google.com.mx/books?id=KMsZAQAAIAAJ},
  year={1994},
  publisher={AT\&T Bell Laboratories}
}
@book{cleveland93,
  title={Visualizing Data},
  author={Cleveland, W.S.},
  isbn={9780963488404},
  lccn={92075077},
  url={https://books.google.com.mx/books?id=V-dQAAAAMAAJ},
  year={1993},
  publisher={At\&T Bell Laboratories}
}
@book{tukey77,
  title={Exploratory Data Analysis},
  author={Tukey, J.W.},
  isbn={9780201076165},
  lccn={76005080},
  series={Addison-Wesley series in behavioral science},
  url={https://books.google.com.mx/books?id=UT9dAAAAIAAJ},
  year={1977},
  publisher={Addison-Wesley Publishing Company}
}
@article{cleveland84,
  abstract = {The subject of graphical methods for data analysis and for data presentation needs a scientific foundation. In this article we take a few steps in the direction of establishing such a foundation. Our approach is based on graphical perception-the visual decoding of information encoded on graphs-and it includes both theory and experimentation to test the theory. The theory deals with a small but important piece of the whole process of graphical perception. The first part is an identification of a set of elementary perceptual tasks that are carried out when people extract quantitative information from graphs. The second part is an ordering of the tasks on the basis of how accurately people perform them. Elements of the theory are tested by experimentation in which subjects record their judgments of the quantitative information on graphs. The experiments validate these elements but also suggest that the set of elementary tasks should be expanded. The theory provides a guideline for graph construction: Graphs should employ elementary tasks as high in the ordering as possible. This principle is applied to a variety of graphs, including bar charts, divided bar charts, pie charts, and statistical maps with shading. The conclusion is that radical surgery on these popular graphs is needed, and as replacements we offer alternative graphical forms-dot charts, dot charts with grouping, and framed-rectangle charts.},
  added-at = {2012-05-05T17:00:53.000+0200},
  author = {Cleveland, William S. and McGill, Robert},
  biburl = {https://www.bibsonomy.org/bibtex/25234fbbabcf2ac0bfab4f335af2f18ee/procomun},
  copyright = {Copyright © 1984 American Statistical Association},
  file = {Cleveland.McGill1984.pdf:Cleveland.McGill1984.pdf:PDF},
  groups = {public},
  interhash = {8f85e9b8f15a85291d8c1d47c1c42e84},
  intrahash = {5234fbbabcf2ac0bfab4f335af2f18ee},
  issn = {01621459},
  journal = {Journal of the American Statistical Association},
  jstor_articletype = {research-article},
  jstor_formatteddate = {Sep., 1984},
  keywords = {},
  language = {English},
  number = 387,
  pages = {pp. 531-554},
  publisher = {American Statistical Association},
  timestamp = {2012-05-05T17:00:53.000+0200},
  title = {Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods},
  url = {http://www.jstor.org/stable/2288400},
  username = {procomun},
  volume = 79,
  year = 1984
}
@book{wilkinson2005,
  title={The Grammar of Graphics},
  author={Wilkinson, L. and Wills, D. and Rope, D. and Norton, A. and Dubbs, R.},
  isbn={9780387245447},
  lccn={2005043230},
  series={Statistics and Computing},
  url={https://books.google.com.mx/books?id=\_kRX4LoFfGQC},
  year={2005},
  publisher={Springer New York}
}
@book{wickham2009,
 author = {Wickham, Hadley},
 title = {Ggplot2: Elegant Graphics for Data Analysis},
 year = {2009},
 isbn = {0387981403, 9780387981406},
 edition = {2nd},
 publisher = {Springer Publishing Company, Incorporated},
}
@Article{plyr,
  author = {Hadley Wickham},
  journal = {Journal of Statistical Software},
  number = {1},
  pages = {1––29},
  selected = {TRUE},
  title = {The split-apply-combine strategy for data analysis},
  url = {http://www.jstatsoft.org/v40/i01/},
  volume = {40},
  year = {2011},
  bdsk-url-1 = {http://www.jstatsoft.org/v40/i01/},
}
@article{tidy,
   author = {Hadley  Wickham},
   title = {Tidy Data},
   journal = {Journal of Statistical Software, Articles},
   volume = {59},
   number = {10},
   year = {2014},
   keywords = {},
   abstract = {A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.},
   issn = {1548-7660},
   pages = {1--23},
   doi = {10.18637/jss.v059.i10},
   url = {https://www.jstatsoft.org/v059/i10}
}
@book{burns2012r,
  title={The R Inferno},
  author={Burns, P.},
  journal = {The American Statistician},
  year    = 2015,
  number  = 69,
  pages   = {371-386},
  month   = 7,
  note    = {An optional note}, 
  volume  = 4
}
@article{tim,
    author = {Tim C. Hesterberg},
    title = {What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics     Curriculum},
    journal = {The American Statistician},
    volume = {69},
    number = {4},
    pages = {371-386},
    year  = {2015},
    publisher = {Taylor & Francis},
    doi = {10.1080/00031305.2015.1089789},
    note ={PMID: 27019512},
    URL = { 
        https://doi.org/10.1080/00031305.2015.1089789
    
    },
    eprint = { 
        https://doi.org/10.1080/00031305.2015.1089789
    }
}
@article{RaoWu,
    author = { J. N. K.   Rao  and  C. F. J.   Wu },
    title = {Resampling Inference with Complex Survey Data},
    journal = {Journal of the American Statistical Association},
    volume = {83},
    number = {401},
    pages = {231-241},
    year  = {1988},
    publisher = {Taylor & Francis},
    doi = {10.1080/01621459.1988.10478591},
    URL = { 
        https://amstat.tandfonline.com/doi/abs/10.1080/01621459.1988.10478591
    },
    eprint = { 
        https://amstat.tandfonline.com/doi/pdf/10.1080/01621459.1988.10478591
    }
}
@Article{enigh, 
    author = {INEGI}, 
    year = {2018},
    title = {Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH-2018). Diseño muestral},
    url = {http://internet.contenidos.inegi.org.mx/contenidos/Productos/prod_serv/contenidos/espanol/bvinegi/productos/nueva_estruc/702825070359.pdf},
}
}
@book{knuth,
  added-at = {2015-06-04T07:16:19.000+0200},
  address = {Boston},
  author = {Knuth, Donald E.},
  biburl = {https://www.bibsonomy.org/bibtex/25dbc415549a1bb86bff7a3842765c31f/ytyoun},
  edition = {Third},
  interhash = {b825ccd550f92a93eefbacd1bec78704},
  intrahash = {5dbc415549a1bb86bff7a3842765c31f},
  isbn = {0201896842 9780201896848},
  keywords = {algorithm knuth no.pdf taocp textbook},
  publisher = {Addison-Wesley},
  refid = {174763889},
  timestamp = {2015-07-29T09:31:05.000+0200},
  title = {The Art of Computer Programming, Volume 2: Seminumerical Algorithms},
  year = 1997
}

@book{pitman-prob,
  added-at = {2008-01-13T17:55:21.000+0100},
  asin = {0387979743},
  author = {Pitman, Jim},
  biburl = {https://www.bibsonomy.org/bibtex/27adb21de98e2f1120bcecb707d14da10/pitman},
  interhash = {fde5cbdb47ba43fda4aa396771e118fc},
  intrahash = {7adb21de98e2f1120bcecb707d14da10},
  keywords = {author:Pitman__Jim probability textbook undergraduate},
  timestamp = {2008-01-13T17:55:21.000+0100},
  title = {Probability},
  typesource = {Simple CitationSource},
  url = {http://www.amazon.ca/Probability-Jim-Pitman/dp/0387979743/ref=sr_1_1?ie=UTF8&s=books&qid=1200243180&sr=1-1},
  year = 1992
}
@book{ross-sim,
 author = {Ross, Sheldon M.},
 title = {Simulation, Fourth Edition},
 year = {2006},
 isbn = {0125980639},
 publisher = {Academic Press, Inc.},
 address = {Orlando, FL, USA},
} 
@book{wasserman,
  abstract = {This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required.},
  added-at = {2016-12-02T12:27:40.000+0100},
  address = {New York},
  author = {Wasserman, Larry},
  biburl = {https://www.bibsonomy.org/bibtex/26eeeed65f018eb61d267c530f549340a/flint63},
  doi = {10.1007/978-0-387-21736-9},
  file = {SpringerLink:2000-04/Wassermann04.pdf:PDF;Springer Product page:http\://www.springer.com/978-1-4419-2322-6:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/0387402721/:URL},
  groups = {public},
  interhash = {4d24dccf9dc08a9b3bf43e2782bd9244},
  intrahash = {6eeeed65f018eb61d267c530f549340a},
  isbn = {978-1-4419-2322-6},
  issn = {1431-875X},
  keywords = {01624 103 springer book theory engineering science},
  publisher = {Springer},
  series = {Springer Texts in Statistics},
  timestamp = {2017-07-13T18:18:19.000+0200},
  title = {All of Statistics: A Concise Course in Statistical Inference},
  username = {flint63},
  year = 2004
}
@article{matsumoto,
 author = {Matsumoto, Makoto and Nishimura, Takuji},
 title = {Mersenne Twister: A 623-dimensionally Equidistributed Uniform Pseudo-random Number Generator},
 journal = {ACM Trans. Model. Comput. Simul.},
 issue_date = {Jan. 1998},
 volume = {8},
 number = {1},
 month = jan,
 year = {1998},
 issn = {1049-3301},
 pages = {3--30},
 numpages = {28},
 url = {http://doi.acm.org/10.1145/272991.272995},
 doi = {10.1145/272991.272995},
 acmid = {272995},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {k-distribution, m-sequences, GFSR, MT19937, Mersenne primes, Mersenne twister, TGFSR, finite fields, incomplete array, inversive-decimation method, multiple-recursive matrix method, primitive polynomials, random number generation, tempering},
} 
@Book{davison,
    title = {Bootstrap Methods and Their Applications},
    author = {A. C. Davison and D. V. Hinkley},
    publisher = {Cambridge University Press},
    address = {Cambridge},
    year = {1997},
    note = {ISBN 0-521-57391-2},
    url = {http://statwww.epfl.ch/davison/BMA/},
}
@article{graphical-inference,
    author = {Wickham, Hadley and Cook, Dianne and Hofmann, Heike and Buja, Andreas},
    title = {Graphical Inference for Infovis},
    journal = {IEEE Transactions on Visualization and Computer Graphics},
    issue_date = {November 2010},
    volume = {16},
    number = {6},
    month = nov,
    year = {2010},
    issn = {1077-2626},
    pages = {973--979},
    numpages = {7},
    url = {https://doi.org/10.1109/TVCG.2010.161},
    doi = {10.1109/TVCG.2010.161},
    acmid = {1907980},
    publisher = {IEEE Educational Activities Department},
    address = {Piscataway, NJ, USA},
    keywords = {Statistics, Statistics, visual testing, permutation tests, null hypotheses, data     plot, data plot, null hypotheses, permutation tests, visual testing},
}
@article{graphical-tests,
    author = {D. Cook and M. Majumder and L. Follett and H. Hofmann},
    journal = {IEEE Transactions on Visualization & Computer Graphics},
    title = {Graphical Tests for Power Comparison of Competing Designs},
    year = {2012},
    volume = {18},
    number = {},
    pages = {2441-2448},
    keywords={Observers;Accuracy;Data models;Visual analytics;Statistical analysis;Inference     mechanisms;Efficiency of displays;Lineups;Visual inference;Power comparison},
    doi = {10.1109/TVCG.2012.230},
    url = {doi.ieeecomputersociety.org/10.1109/TVCG.2012.230},
    ISSN = {1077-2626},
    month={12}
}
@Manual{R-rstan,
  title = {rstan: R Interface to Stan},
  author = {Jiqiang Guo and Jonah Gabry and Ben Goodrich},
  year = {2019},
  note = {R package version 2.19.2},
  url = {https://CRAN.R-project.org/package=rstan},
}
@article{infovis,
author = {H. Hofmann and H. Wickham and D. Cook and A. Buja},
journal = {IEEE Transactions on Visualization & Computer Graphics},
title = {Graphical inference for infovis},
year = {2010},
volume = {16},
number = {},
pages = {973-979},
keywords={Statistics; visual testing; permutation tests; null hypotheses; data plot},
doi = {10.1109/TVCG.2010.161},
url = {doi.ieeecomputersociety.org/10.1109/TVCG.2010.161},
ISSN = {1077-2626},
month={09}
}

@article{graphical-tests, 
author={H. Hofmann and L. Follett and M. Majumder and D. Cook}, 
journal={IEEE Transactions on Visualization and Computer Graphics}, 
title={Graphical Tests for Power Comparison of Competing Designs}, 
year={2012}, 
volume={18}, 
number={12}, 
pages={2441-2448}, 
keywords={computer graphics;graphical tests;power comparison;visual testing;standard statistical inference tests;graphical findings;distributional assumptions;lineups;graphical designs;coordinate system;polar coordinates;cartesian coordinates;spotting patterns;plot designs;Amazon Mechanical Turk;MTurk;Observers;Accuracy;Data models;Visual analytics;Statistical analysis;Inference mechanisms;Lineups;Visual inference;Power comparison;Efficiency of displays}, 
doi={10.1109/TVCG.2012.230}, 
ISSN={1077-2626}, 
month={Dec},}

@article {buja,
	author = {Buja, Andreas and Cook, Dianne and Hofmann, Heike and Lawrence, Michael and Lee, Eun-Kyung and Swayne, Deborah F. and Wickham, Hadley},
	title = {Statistical inference for exploratory data analysis and model diagnostics},
	volume = {367},
	number = {1906},
	pages = {4361--4383},
	year = {2009},
	doi = {10.1098/rsta.2009.0120},
	publisher = {The Royal Society},
	issn = {1364-503X},
	URL = {http://rsta.royalsocietypublishing.org/content/367/1906/4361},
	eprint = {http://rsta.royalsocietypublishing.org/content/367/1906/4361.full.pdf},
	journal = {Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences}
}

@article{majumder,
    author = { Mahbubul   Majumder  and  Heike   Hofmann  and  Dianne   Cook },
    title = {Validation of Visual Statistical Inference, Applied to Linear Models},
    journal = {Journal of the American Statistical Association},
    volume = {108},
    number = {503},
    pages = {942-956},
    year  = {2013},
    publisher = {Taylor & Francis},
    doi = {10.1080/01621459.2013.808157},
    URL = { 
            https://doi.org/10.1080/01621459.2013.808157
        
    },
    eprint = { 
        https://doi.org/10.1080/01621459.2013.808157
    }

}

@book{hastie,
  added-at = {2008-05-16T16:17:42.000+0200},
  address = {New York, NY, USA},
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
  biburl = {https://www.bibsonomy.org/bibtex/2f58afc5c9793fcc8ad8389824e57984c/sb3000},
  interhash = {d585aea274f2b9b228fc1629bc273644},
  intrahash = {f58afc5c9793fcc8ad8389824e57984c},
  keywords = {ml statistics},
  publisher = {Springer New York Inc.},
  series = {Springer Series in Statistics},
  timestamp = {2008-05-16T16:17:43.000+0200},
  title = {The Elements of Statistical Learning},
  year = 2001
}

@book{bolstad,
  title={Understanding Computational Bayesian Statistics},
  author={Bolstad, W.M.},
  isbn={9780470046098},
  lccn={2009025219},
  series={Cram101 textbook key facts},
  url={https://books.google.com.mx/books?id=igbrBgAAQBAJ},
  year={2010},
  publisher={Wiley}
}
@misc{betancourt2017,
  abstract = {Hamiltonian Monte Carlo has proven a remarkable empirical success, but only
recently have we begun to develop a rigorous understanding of why it performs
so well on difficult problems and how it is best applied in practice.
Unfortunately, that understanding is confined within the mathematics of
differential geometry which has limited its dissemination, especially to the
applied communities for which it is particularly important. In this review I
provide a comprehensive conceptual account of these theoretical foundations,
focusing on developing a principled intuition behind the method and its optimal
implementations rather of any exhaustive rigor. Whether a practitioner or a
statistician, the dedicated reader will acquire a solid grasp of how
Hamiltonian Monte Carlo works, when it succeeds, and, perhaps most importantly,
when it fails.},
  added-at = {2018-07-30T18:41:13.000+0200},
  author = {Betancourt, Michael},
  biburl = {https://www.bibsonomy.org/bibtex/20ec2b7706245822e493920256fe82203/peter.ralph},
  interhash = {ff53d1978dff557f7b1866080fb89fc7},
  intrahash = {0ec2b7706245822e493920256fe82203},
  keywords = {HMC MCMC hamiltonian_monte_carlo methods},
  note = {arxiv:1701.02434},
  timestamp = {2018-07-30T18:41:13.000+0200},
  title = {A Conceptual Introduction to Hamiltonian Monte Carlo},
  url = {http://arxiv.org/abs/1701.02434},
  year = 2017
}
@Misc{stan2018,
    title = {Stan Modeling Language Users Guide and Reference Manual, Version 2.18.0},
    author = {{Stan Development Team}},
    year = {2018},
    url = {http://mc-stan.org/},
  }
@article{little2012,
title = "Calibrated Bayes, an Alternative Inferential Paradigm for Official Statistics: Discussion",
keywords = "112 Statistics and probability",
author = "Risto Lehtonen",
year = "2012",
language = "English",
volume = "28",
pages = "353–357",
journal = "Journal of Official Statistics",
issn = "0282-423X",
publisher = "SCB",
number = "3",
}
@article{mendoza2016,
	abstract = {In all democracies, anticipating the final results of a national election the same day the voters go to the polling stations is a matter of interest, for television stations and some civil rights organizations, for example. The most reliable option is a quick count, a statistical procedure that consists in selecting a random sample of polling stations and analysing their final counts to forecast the election results. In Mexico, a particularly important quick count is organized by the electoral authority. The importance of its results requires this exercise to be designed and executed with specially high standards far beyond those used in commercial studies of this type. In this paper, the model and the Bayesian analysis of the quick counts conducted by the Mexican authority, during the presidential elections in 2006 and 2012, are discussed.},
	affiliation = {Department of Statistics, ITAM, Mexico},
	author = {Mendoza, Manuel and Nieto-Barajas, Luis E.},
	doi = {10.1016/j.electstud.2016.06.007},
	journal = {Electoral Studies},
	keywords = {Quick count; Bayesian approach; Noninformative prior; Normal model},
	language = {English},
	number = {C},
	pages = {124-132},
	title = {Quick counts in the Mexican presidential elections: A Bayesian approach},
	volume = {43},
	year = {2016},
}
@ARTICLE{parkgelmanbafumi,
    title = {Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls},
    author = {Park, David K. and Gelman, Andrew and Bafumi, Joseph},
    year = {2004},
    journal = {Political Analysis},
    volume = {12},
    number = {04},
    pages = {375-385},
    url = {https://EconPapers.repec.org/RePEc:cup:polals:v:12:y:2004:i:04:p:375-385_00}
}
@book{albert,
  title={Bayesian Computation with R},
  author={Albert, J.},
  isbn={9780387922980},
  lccn={2009926660},
  series={Use R!},
  url={https://books.google.com.mx/books?id=AALhk\_mt7SYC},
  year={2009},
  publisher={Springer New York}
}
@Book{chihara,
    title = {Mathematical Statistics with Resampling and R},
    author = {Laura M. Chihara and Tim C. Hesterberg},
    year = {2018},
    edition = {2},
    publisher = {John Wiley & Sons},
    address = {Hoboken, NJ},
    url = {https://sites.google.com/site/chiharahesterberg/home},
    isbn = {978-1-119-41653-1},
}
@book{sleuth,
  added-at = {2011-08-22T09:44:59.000+0200},
  asin = {0534386709},
  author = {Ramsey, Fred and Schafer, Daniel},
  biburl = {https://www.bibsonomy.org/bibtex/2315e0b6de77ccf942fda05256885d642/vivion},
  description = {Amazon.com: The Statistical Sleuth: A Course in Methods of Data Analysis (9780534386702): Fred Ramsey, Daniel Schafer: Books},
  dewey = {519.5},
  ean = {9780534386702},
  edition = 2,
  interhash = {9e51e06e423bc344a003023f30b6417e},
  intrahash = {315e0b6de77ccf942fda05256885d642},
  isbn = {0534386709},
  keywords = {analysis data methods statistical},
  publisher = {Duxbury Press},
  timestamp = {2011-08-22T09:45:00.000+0200},
  title = {The Statistical Sleuth: A Course in Methods of Data Analysis},
  url = {http://www.amazon.com/Statistical-Sleuth-Course-Methods-Analysis/dp/0534386709},
  year = 2002
}
@article{Guber2011,
author = {Guber, Deborah Lynn},
year = {1999},
month = {01},
pages = {},
title = {Getting What You Pay For: The Debate Over Equity in Public School Expenditures},
volume = {7},
journal = {Journal of Statistics Education}
}
@article{chambers83,
  added-at = {2011-08-10T15:21:49.000+0200},
  author = {Chambers, J.M. and Cleveland, W.S. and Kleiner, B. and Tukey, P.A.},
  biburl = {https://www.bibsonomy.org/bibtex/2fcaa7d509ee361fe687769b40d07100f/lutzm},
  date-added = {2007-09-03 22:45:16 -0500},
  date-modified = {2007-09-03 22:45:16 -0500},
  interhash = {7834ca99c9054de232d723a476be2827},
  intrahash = {fcaa7d509ee361fe687769b40d07100f},
  journal = {The Wadsworth Statistics/Probability Series. Boston, MA: Duxury},
  keywords = {diplom},
  timestamp = {2011-08-10T15:21:49.000+0200},
  title = {{Graphical Methods for Data Analysis}},
  year = 1983
}
@article{timboot14,
author = {Hesterberg, Tim},
year = {2014},
month = {11},
pages = {},
title = {What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum},
volume = {69},
journal = {The American Statistician},
doi = {10.1080/00031305.2015.1089789}
}
@book{box78,
  title={Statistics for experimenters: an introduction to design, data analysis, and model building},
  author={Box, G.E.P. and Hunter, W.G. and Hunter, J.S.},
  isbn={9780471093152},
  series={Wiley series in probability and mathematical statistics: Applied probability and statistics},
  year={1978},
  publisher={Wiley}
}
@article{cookwasps,
title = "Using visual statistical inference to better understand random class separations in high dimension, low sample size data",
keywords = "Data mining, Lineup, Projection pursuit, Statistical graphics, Visualization",
author = "{Roy Chowdhury}, Niladri and Dianne Cook and Heike Hofmann and Mahbubul Majumder and Lee, {Eun Kyung} and Toth, {Amy L.}",
year = "2014",
month = "11",
day = "2",
doi = "10.1007/s00180-014-0534-x",
language = "English (US)",
volume = "30",
pages = "293--316",
journal = "Computational Statistics",
issn = "0943-4062",
publisher = "Springer Verlag",
number = "2",
}
@article{falsefindings,
    author = {Ioannidis, John P. A.},
    journal = {PLOS Medicine},
    publisher = {Public Library of Science},
    title = {Why Most Published Research Findings Are False},
    year = {2005},
    month = {08},
    volume = {2},
    url = {https://doi.org/10.1371/journal.pmed.0020124},
    pages = {},
    abstract = {Published research findings are sometimes refuted by subsequent evidence, says Ioannidis, with ensuing confusion and disappointment.},
    number = {8},
    doi = {10.1371/journal.pmed.0020124}
}
@Book{ESL,
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
  biburl = {https://www.bibsonomy.org/bibtex/2f58afc5c9793fcc8ad8389824e57984c/sb3000},
  publisher = {Springer New York Inc.},
  series = {Springer Series in Statistics},
  url = {http://web.stanford.edu/~hastie/ElemStatLearn/},
  title = {The Elements of Statistical Learning},
  year = 2017
}
@misc{bootefron,
  added-at = {2010-07-23T07:14:55.000+0200},
  author = {Efron, B. and Tibshirani, R.},
  biburl = {https://www.bibsonomy.org/bibtex/25e453c8ff829437e8b0010281b07eab7/richterek},
  interhash = {ea5821249b52ce553868b8151c980623},
  intrahash = {5e453c8ff829437e8b0010281b07eab7},
  keywords = {special\_relativity},
  mendeley-tags = {special\_relativity},
  publisher = {Macmillan Publishers Limited. All rights reserved},
  timestamp = {2010-07-23T07:14:59.000+0200},
  title = {{An Introduction to the Bootstrap}},
  type = {Miscellaneous},
  year = 1993
}
@book{ClevelandVis,
 author = {Cleveland, William S.},
 title = {Visualizing Data},
 year = {1993},
 isbn = {0963488406},
 publisher = {Hobart Press},
} 
@article{lineup,
 author = {Wickham, Hadley and Cook, Dianne and Hofmann, Heike and Buja, Andreas},
 title = {Graphical Inference for Infovis},
 journal = {IEEE Transactions on Visualization and Computer Graphics},
 issue_date = {November 2010},
 volume = {16},
 number = {6},
 month = nov,
 year = {2010},
 issn = {1077-2626},
 pages = {973--979},
 numpages = {7},
 url = {https://doi.org/10.1109/TVCG.2010.161},
 doi = {10.1109/TVCG.2010.161},
 acmid = {1907980},
 publisher = {IEEE Educational Activities Department},
 address = {Piscataway, NJ, USA},
 keywords = {Statistics, Statistics, visual testing, permutation tests, null hypotheses, data plot, data plot, null hypotheses, permutation tests, visual testing},
} 
@Manual{nullabor,
    title = {nullabor: Tools for Graphical Inference},
    author = {Hadley Wickham and Niladri Roy Chowdhury and Di Cook and Heike Hofmann},
    year = {2018},
    note = {R package version 0.3.5},
    url = {https://CRAN.R-project.org/package=nullabor},
  }
@Manual{tidyverse,
    title = {tidyverse: Easily Install and Load the 'Tidyverse'},
    author = {Hadley Wickham},
    year = {2017},
    note = {R package version 1.2.1},
    url = {https://CRAN.R-project.org/package=tidyverse},
  }
@Manual{bookdown,
    title = {bookdown: Authoring Books and Technical Documents with R Markdown},
    author = {Yihui Xie},
    year = {2019},
    note = {R package version 0.13},
    url = {https://github.com/rstudio/bookdown},
  }
@article{hundred,
 author = {Fern\'{a}ndez-Delgado, Manuel and Cernadas, Eva and Barro, Sen{\'e}n and Amorim, Dinani},
 title = {Do We Need Hundreds of Classifiers to Solve Real World Classification Problems?},
 journal = {J. Mach. Learn. Res.},
 issue_date = {January 2014},
 volume = {15},
 number = {1},
 month = jan,
 year = {2014},
 issn = {1532-4435},
 pages = {3133--3181},
 numpages = {49},
 url = {http://dl.acm.org/citation.cfm?id=2627435.2697065},
 acmid = {2697065},
 publisher = {JMLR.org},
 keywords = {Bayesian classifiers, UCI data base, classification, decision trees, discriminant analysis, ensembles, generalized linear models, logistic and multinomial regression, multiple adaptive regression splines, nearest-neighbors, neural networks, partial least squares and principal component regression, random forest, rule-based classifiers, support vector machine},
}
@book{GelmanHill,
  added-at = {2011-08-15T12:47:13.000+0200},
  asin = {052168689X},
  author = {Gelman, Andrew and Hill, Jennifer},
  biburl = {https://www.bibsonomy.org/bibtex/201c2497e4ffea441a9835d0f05160dd7/vivion},
  description = {Amazon.com: Data Analysis Using Regression and Multilevel/Hierarchical Models (9780521686891): Andrew Gelman, Jennifer Hill: Books},
  dewey = {519.536},
  ean = {9780521686891},
  edition = 1,
  interhash = {3a8313c400b72653645c195a19c1eb02},
  intrahash = {01c2497e4ffea441a9835d0f05160dd7},
  isbn = {052168689X},
  keywords = {statistics},
  publisher = {Cambridge University Press},
  timestamp = {2011-08-15T12:47:13.000+0200},
  title = {Data Analysis Using Regression and Multilevel/Hierarchical Models},
  url = {http://www.amazon.com/Analysis-Regression-Multilevel-Hierarchical-Models/dp/052168689X/ref=sr_1_1?s=books&ie=UTF8&qid=1313405184&sr=1-1},
  year = 2006
}
@book{izenman,
 author = {Izenman, Alan Julian},
 title = {Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning},
 year = {2008},
 isbn = {0387781889, 9780387781884},
 edition = {1},
 publisher = {Springer Publishing Company, Incorporated},
} 
@article{rfintervals,
author = {Haozhe Zhang and Joshua Zimmerman and Dan Nettleton and Daniel J. Nordman},
title = {Random Forest Prediction Intervals},
journal = {The American Statistician},
volume = {0},
number = {0},
pages = {1-15},
year  = {2019},
publisher = {Taylor & Francis},
doi = {10.1080/00031305.2019.1585288}
}
@Manual{rfintervalpkg,
    title = {rfinterval: Predictive Inference for Random Forests},
    author = {Haozhe Zhang},
    year = {2019},
    note = {R package version 1.0.0},
    url = {https://CRAN.R-project.org/package=rfinterval},
}
@book{Bishop,
 author = {Bishop, Christopher M.},
 title = {Pattern Recognition and Machine Learning (Information Science and Statistics)},
 year = {2006},
 isbn = {0387310738},
 publisher = {Springer-Verlag},
 address = {Berlin, Heidelberg},
} 
@Article{glmnet,
    title = {Regularization Paths for Generalized Linear Models via Coordinate Descent},
    author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    journal = {Journal of Statistical Software},
    year = {2010},
    volume = {33},
    number = {1},
    pages = {1--22},
    url = {http://www.jstatsoft.org/v33/i01/},
  }
@book{chitim,
  title={Mathematical Statistics with Resampling and R},
  author={Chihara, L.M. and Hesterberg, T.C.},
  isbn={9781118029855},
  lccn={2011026144},
  url={https://books.google.com.mx/books?id=9KRHFDKDV84C},
  year={2011},
  publisher={Wiley}
}
@Article{factominer,
    title = {{FactoMineR}: A Package for Multivariate Analysis},
    author = {S\'ebastien L\^e and Julie Josse and Fran\c{c}ois Husson},
    journal = {Journal of Statistical Software},
    year = {2008},
    volume = {25},
    number = {1},
    pages = {1--18},
    doi = {10.18637/jss.v025.i01},
}
@Manual{R-rstanarm,
  title = {rstanarm: Bayesian Applied Regression Modeling via Stan},
  author = {Jonah Gabry and Ben Goodrich},
  year = {2019},
  note = {R package version 2.19.2},
  url = {https://CRAN.R-project.org/package=rstanarm},
}
@Manual{R-bayesplot,
  title = {bayesplot: Plotting for Bayesian Models},
  author = {Jonah Gabry and Tristan Mahr},
  year = {2019},
  note = {R package version 1.7.0},
  url = {https://CRAN.R-project.org/package=bayesplot},
}
@Manual{R-shinystan,
  title = {shinystan: Interactive Visual and Numerical Diagnostics and Posterior
Analysis for Bayesian Models},
  author = {Jonah Gabry},
  year = {2018},
  note = {R package version 2.5.0},
  url = {https://CRAN.R-project.org/package=shinystan},
}
@Manual{R-ggplot2,
  title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics},
  author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo and Hiroaki Yutani},
  year = {2019},
  note = {R package version 3.2.1},
  url = {https://CRAN.R-project.org/package=ggplot2},
}
@Manual{R-tidyr,
  title = {tidyr: Tidy Messy Data},
  author = {Hadley Wickham and Lionel Henry},
  year = {2019},
  note = {R package version 1.0.0},
  url = {https://CRAN.R-project.org/package=tidyr},
}
@Manual{R-purrr,
  title = {purrr: Functional Programming Tools},
  author = {Lionel Henry and Hadley Wickham},
  year = {2019},
  note = {R package version 0.3.3},
  url = {https://CRAN.R-project.org/package=purrr},
}
@Manual{R-ggplot2,
  title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics},
  author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo and Hiroaki Yutani},
  year = {2019},
  note = {R package version 3.2.1},
  url = {https://CRAN.R-project.org/package=ggplot2},
}
@Manual{R-dplyr,
  title = {dplyr: A Grammar of Data Manipulation},
  author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller},
  year = {2019},
  note = {R package version 0.8.3},
  url = {https://CRAN.R-project.org/package=dplyr},
}
=======
  }
@book{missingrubin,
  added-at = {2012-09-09T11:27:10.000+0200},
  author = {Little, R.J.A. and Rubin, D.B.},
  biburl = {https://www.bibsonomy.org/bibtex/2b2fb20df470898e82317fb855c088703/peter.ralph},
  interhash = {3afe77a956d4d25ef971f22ee3172776},
  intrahash = {b2fb20df470898e82317fb855c088703},
  isbn = {9780471183860},
  keywords = {missing_data statistics},
  lccn = {2002027006},
  publisher = {Wiley},
  series = {Wiley series in probability and mathematical statistics. Probability and mathematical statistics},
  timestamp = {2012-09-09T11:27:10.000+0200},
  title = {Statistical analysis with missing data},
  url = {http://books.google.com/books?id=aYPwAAAAMAAJ},
  year = 2002
}
@book{puppies,
 author = {Kruschke, John K.},
 title = {Doing Bayesian Data Analysis: A Tutorial with R and BUGS},
 year = {2010},
 isbn = {0123814855, 9780123814852},
 edition = {1st},
 publisher = {Academic Press, Inc.},
 address = {Orlando, FL, USA},
} 
@book{deepbook,
 author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
 title = {Deep Learning},
 year = {2016},
 isbn = {0262035618, 9780262035613},
 publisher = {The MIT Press},
}
@Article{mice,
    title = {{mice}: Multivariate Imputation by Chained Equations in R},
    author = {Stef {van Buuren} and Karin Groothuis-Oudshoorn},
    journal = {Journal of Statistical Software},
    year = {2011},
    volume = {45},
    number = {3},
    pages = {1-67},
    url = {https://www.jstatsoft.org/v45/i03/},
  }