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Bayesian Semiparametric Isotonic Regression for Count Data


  • Dunson, David B.


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  • Dunson, David B., 2005. "Bayesian Semiparametric Isotonic Regression for Count Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 618-627, June.
  • Handle: RePEc:bes:jnlasa:v:100:y:2005:p:618-627

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    Cited by:

    1. Shively, Thomas S. & Walker, Stephen G. & Damien, Paul, 2011. "Nonparametric function estimation subject to monotonicity, convexity and other shape constraints," Journal of Econometrics, Elsevier, vol. 161(2), pages 166-181, April.
    2. John Haslett & Andrew Parnell, 2008. "A simple monotone process with application to radiocarbon‐dated depth chronologies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(4), pages 399-418, September.
    3. Abel Rodriguez & Enrique ter Horst, 2008. "Measuring expectations in options markets: An application to the SP500 index," Papers 0901.0033,
    4. Abel Rodríguez & Enrique ter Horst, 2011. "Measuring expectations in options markets: an application to the S&P500 index," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1393-1405, July.
    5. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2008. "A Bayesian mixed logit-probit model for multinomial choice," Journal of Econometrics, Elsevier, vol. 147(2), pages 232-246, December.
    6. Shively, Thomas S. & Kockelman, Kara & Damien, Paul, 2010. "A Bayesian semi-parametric model to estimate relationships between crash counts and roadway characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 699-715, June.
    7. Cristina Rueda & Miguel Fernández & Bonifacio Salvador, 2009. "Bayes Discriminant Rules with Ordered Predictors," Journal of Classification, Springer;The Classification Society, vol. 26(2), pages 201-225, August.
    8. Zarepour, Mahmoud & Labadi, Luai Al, 2012. "On a rapid simulation of the Dirichlet process," Statistics & Probability Letters, Elsevier, vol. 82(5), pages 916-924.

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