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Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification

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  • Tyler H. McCormick
  • Adrian E. Raftery
  • David Madigan
  • Randall S. Burd

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  • Tyler H. McCormick & Adrian E. Raftery & David Madigan & Randall S. Burd, 2012. "Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification," Biometrics, The International Biometric Society, vol. 68(1), pages 23-30, March.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:1:p:23-30
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01645.x
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    References listed on IDEAS

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    1. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    2. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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    1. Ramírez-Hassan, Andrés & Carvajal-Rendón, Daniela A., 2021. "Specification uncertainty in modeling internet adoption: A developing city case analysis," Utilities Policy, Elsevier, vol. 70(C).
    2. Todd J. Levy & Kevin Coppa & Jinxuan Cang & Douglas P. Barnaby & Marc D. Paradis & Stuart L. Cohen & Alex Makhnevich & David Klaveren & David M. Kent & Karina W. Davidson & Jamie S. Hirsch & Theodoros, 2022. "Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Gary Koop & Lise Tole, 2013. "Forecasting the European carbon market," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 723-741, June.
    4. Miguel Belmonte & Gary Koop, 2014. "Model Switching and Model Averaging in Time-Varying Parameter Regression Models," Advances in Econometrics, in: Bayesian Model Comparison, volume 34, pages 45-69, Emerald Group Publishing Limited.
    5. Xia, Ye-Mao & Tang, Nian-Sheng & Gou, Jian-Wei, 2016. "Generalized linear latent models for multivariate longitudinal measurements mixed with hidden Markov models," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 259-275.
    6. Bork, Lasse & Møller, Stig V., 2015. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection," International Journal of Forecasting, Elsevier, vol. 31(1), pages 63-78.
    7. Lasse Bork & Stig V. Møller & Thomas Q. Pedersen, 2020. "A New Index of Housing Sentiment," Management Science, INFORMS, vol. 66(4), pages 1563-1583, April.
    8. Alisa Yusupova & Nicos G. Pavlidis & Efthymios G. Pavlidis, 2019. "Adaptive Dynamic Model Averaging with an Application to House Price Forecasting," Papers 1912.04661, arXiv.org.
    9. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    10. Andrés Ramírez-Hassan, 2020. "Dynamic variable selection in dynamic logistic regression: an application to Internet subscription," Empirical Economics, Springer, vol. 59(2), pages 909-932, August.
    11. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    12. Jiakun Jiang & Wei Yang & Erin M. Schnellinger & Stephen E. Kimmel & Wensheng Guo, 2023. "Dynamic logistic state space prediction model for clinical decision making," Biometrics, The International Biometric Society, vol. 79(1), pages 73-85, March.
    13. Solomon Shiferaw Beyene & Tianyi Ling & Blagoj Ristevski & Ming Chen, 2020. "A novel riboswitch classification based on imbalanced sequences achieved by machine learning," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-23, July.
    14. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    15. Hanan Naser & Fatema Alaali, 2018. "Can oil prices help predict US stock market returns? Evidence using a dynamic model averaging (DMA) approach," Empirical Economics, Springer, vol. 55(4), pages 1757-1777, December.
    16. Bakerman, Jordan & Pazdernik, Karl & Korkmaz, Gizem & Wilson, Alyson G., 2022. "Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest," International Journal of Forecasting, Elsevier, vol. 38(2), pages 648-661.
    17. Lin, Boqiang & Su, Tong, 2021. "Do China's macro-financial factors determine the Shanghai crude oil futures market?," International Review of Financial Analysis, Elsevier, vol. 78(C).
    18. Mélanie Roschewitz & Galvin Khara & Joe Yearsley & Nisha Sharma & Jonathan J. James & Éva Ambrózay & Adam Heroux & Peter Kecskemethy & Tobias Rijken & Ben Glocker, 2023. "Automatic correction of performance drift under acquisition shift in medical image classification," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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