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Applied Bayesian Statistics pp 25–47Cite as

Introduction to One-Parameter Models: Estimating a Population Proportion

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Part of the book series: Springer Texts in Statistics ((STS,volume 98))

Abstract

On March 15, 2002, the Iowa City Press Citizen carried an article about the intended 19% tuition increase to go into effect at the University of Iowa (UI) for the next academic year. Let’s revisit that time and suppose that you wish to send the regents and the state legislature some arguments against this idea. To support your argument, you would like to tell the regents and legislators what proportion of current UI students are likely to quit school if tuition is raised that much.

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References

  • J. Albert, Bayesian Computation using Minitab (Wadsworth, Belmont, CA, 1997)

    Google Scholar 

  • J. Albert, Bayesian Computation with R (Springer, New York, 2007)

    Book  MATH  Google Scholar 

  • J. Albert, LearnBayes: Functions for Learning Bayesian Inference (2008). R package version 2.0

    Google Scholar 

  • T.W. Anderson, An Introduction to Multivariate Statistical Analysis, 2nd edn. (Wiley, 1984)

    Google Scholar 

  • G. Antelman, Elementary Bayesian Statistics, Edward Elgar Pub (1997)

    Google Scholar 

  • Y. Atchade, J. Rosenthal, Bernoulli 11, 815 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  • R.L. Bauer, J. Sung, K.H.E. Jr., A. Koul, N.B. Castillo, T. Nemoto, Annals of Surgical Oncology 4(4), 316 (1997)

    Google Scholar 

  • J.O. Berger, Statistical decision theory and Bayesian analysis (Springer, 1985)

    Google Scholar 

  • D.A. Berry, Statistics: A Bayesian Perspective (Duxbury Press, Belmont, California, 1996)

    Google Scholar 

  • C.I. Bliss, Annals of Applied Biology 22, 134 (1935)

    Article  Google Scholar 

  • G.E. Box, G.C. Tiao, Bayesian inference in statistical analysis (Addison-Wesley, 1973)

    Google Scholar 

  • S.P. Brooks, A. Gelman, Journal of Computational and Graphical Statistics 7, 434 (1998)

    MathSciNet  Google Scholar 

  • R.L. Burden, D. Faires, Numerical Analysis, 9th edn. (Brooks/Cole, Boston, 2011)

    Google Scholar 

  • B.P. Carlin, S. Chib, Journal of the Royal Statistical Society, Series B 57(3), 473 (1995)

    Google Scholar 

  • B.P. Carlin, T.A. Louis, Bayes and Empirical Bayes Methods for Data Analysis, 2nd edn. (Chapman & Hall/CRC, Boca Raton, 2000)

    Book  MATH  Google Scholar 

  • B.P. Carlin, D.J. Sargent, Statistics in Medicine 15, 1093 (1996)

    Article  Google Scholar 

  • S. Chib, Journal of the American Statistical Association 90(432), 1313 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  • S. Chib, I. Jeliazkov, Journal of the American Statistical Association 96(453), 270 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  • P. Congdon, Bayesian Statistical Modelling (Wiley, 2001)

    Google Scholar 

  • P. Congdon, Applied Bayesian Modelling (Wiley, 2003)

    Google Scholar 

  • M.K. Cowles, Journal of Statistical Planning and Inference 112(1–2) (2003)

    Google Scholar 

  • M.K. Cowles, B.P. Carlin, Journal of the American Statistical Association 91, 883 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  • L. Devroye, Non-uniform Random Variate Generation, Springer, (1986)

    Google Scholar 

  • S. DJ, B. NG, C. BP, V. der Linde A, Journal of the Royal Statistical Society, Series B, 64(4), 583 (2002)

    Google Scholar 

  • A.E. Gelfand, A.F.M. Smith, Journal of the American Statistical Association 85, 398 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  • A. Gelman, Bayesian Analysis 1(3), 515 (2006)

    MathSciNet  Google Scholar 

  • A. Gelman, D.Rubin, Statistical Science 7, 457 (1992)

    Article  Google Scholar 

  • A. Gelman, X.L. Meng, H. Stern, Statistica Sinica 6, 733 (1996)

    MathSciNet  MATH  Google Scholar 

  • A. Gelman, J.B. Carlin, H.S. Stern, D.B. Rubin, Bayesian Data Analysis, 2nd edn. (Chapman & Hall, 2004)

    Google Scholar 

  • S. Geman, D. Geman, IEEE Transaction on Pattern Analysis and Machine Intelligence 6, 721 (1984)

    Article  MATH  Google Scholar 

  • J. Gill, Bayesian Methods for the Social and Behavioral Sciences (Chapman and Hall, 2002)

    Google Scholar 

  • I.J. Good, The Estimation of Probabilities: An Essay on Modern Bayesian Methods, M.I.T. Press, Cambridge, MA, (1965)

    MATH  Google Scholar 

  • J. Greenhouse, L. Waserman, Statistics in Medicine 14, 1379 (1995)

    Article  Google Scholar 

  • W. Hastings, Biometrika 57, 97 (1970)

    Article  MATH  Google Scholar 

  • D.W. Hosmer, S. Lemeshow, Applied logistic regression (Wiley Series in probability and statistics), 2nd edn. (Wiley-Interscience Publication, 2000)

    Google Scholar 

  • H. Jeffreys, Proceedings of the Royal Society of London, Series A, Mathematical and Physical Sciences 186, 453 (1946)

    Article  MathSciNet  MATH  Google Scholar 

  • J. Jensen, Acta Mathematica 30, 175 (1906)

    Article  MathSciNet  MATH  Google Scholar 

  • V. Johnson, J. Albert, Ordinal Data Modeling (Springer, 1999)

    Google Scholar 

  • R.E. Kass, A.E. Raftery, Journal of the American Statistical Association 90(430), 773 (1995)

    Article  MATH  Google Scholar 

  • S.L. Lauritzen, D.J. Spiegelhalter, Journal of the Royal Statistical Society, Series B 50, 205 (1988). Reprinted in Shafer and Pearl (1990)

    Google Scholar 

  • P.M. Lee, Bayesian Statistics: an Introduction, 3rd edn. (Hodder Arnold, 2004)

    Google Scholar 

  • D. Lindley, A. Smith, Journal of the Royal Statistical Society Series B 34, 1 (1972)

    MathSciNet  MATH  Google Scholar 

  • D. Lunn, D. Spiegelhalter, A. Thomas, N. Best, Statistics in Medicine 28, 3049 (2009)

    Article  MathSciNet  Google Scholar 

  • D. Lunn, A. Thomas, N. Best, D. Spiegelhalter, Statistics and Computing 10, 325 (2000)

    Article  Google Scholar 

  • P. McCulloch, J.A. Nelder, Generalized Linear Models, 2nd edn. (CRC Press, 1989)

    Google Scholar 

  • S.B. McGrayne, Scientific American (2011)

    Google Scholar 

  • S.B. McGrayne, The Theory That Would Not Die: How Bayes Rule Cracked the Enigma Code, Hunted Down Russian Submarines and Emerged Triumphant from Two Centuries of Controversy, Yale University Press (2011)

    Google Scholar 

  • N. Metropolis, A. Rosenbluth, A. Rosenbluth, A. Teller, E. Teller, Journal of Chemical Physics 21, 1087 (1953)

    Article  Google Scholar 

  • D.S. Moore, The Basic Practice of Statistics, 4th edn. (W.H. Freeman and Company, New York, 2007)

    Google Scholar 

  • R.M. Neal, Annals of Statistics 31, 705 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  • J.A. Nelder, R.W.M. Wedderburn, Journal of the Royal Statistical Society, Series A, General 135, 370 (1972)

    Google Scholar 

  • M. Novick, P. Jackson, D. Thayer, N. Cole, British Journal of Mathematical and Statistical Psychology 25, 33 (1972)

    Article  MATH  Google Scholar 

  • S.P. Poplack, A.N. Tosteson, M.R. Grove, W.A. Wells, P.A. Carney, Radiology 217, 832 (2000)

    Google Scholar 

  • R Development Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008). URL http://www.R-project.org. ISBN 3-900051-07-0

  • C.P. Robert, G. Casella, Introducing Monte Carlo Methods with R (Springer, 2010)

    Google Scholar 

  • D.J. Spiegelhalter, L.S. Freedman, M. Parmer, Statistics in Medicine 11, 1501 (1993)

    Article  Google Scholar 

  • D. Spiegelhalter, A. Thomas, N. Best, W. Gilks, BUGS Examples, Version 0.5. MRC Biostatistics Unit, Cambridge (1995)

    Google Scholar 

  • C.P. Robert, The Bayesian choice, Springer (2007)

    Google Scholar 

  • G.G. Woodworth, Biostatistics: A Bayesian Introduction (Wiley, Hoboken, New Jersey, 2004)

    MATH  Google Scholar 

  • A. Zellner, An Introduction to Bayesian Inference in Econometrics (Wiley, 1971)

    Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa, USA

    Mary Kathryn Cowles

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  1. Mary Kathryn Cowles
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Cowles, M.K. (2013). Introduction to One-Parameter Models: Estimating a Population Proportion. In: Applied Bayesian Statistics. Springer Texts in Statistics, vol 98. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5696-4_3

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