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Local Versus Global Models for Classification Problems: Fitting Models Where it Matters

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  • Hand D.J.
  • Vinciotti V.

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  • Hand D.J. & Vinciotti V., 2003. "Local Versus Global Models for Classification Problems: Fitting Models Where it Matters," The American Statistician, American Statistical Association, vol. 57, pages 124-131, May.
    • Handle: RePEc:bes:amstat:v:57:y:2003:m:may:p:124-131
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    Citations

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    Cited by:

    1. Hofer, Vera, 2015. "Adapting a classification rule to local and global shift when only unlabelled data are available," European Journal of Operational Research, Elsevier, vol. 243(1), pages 177-189.
    2. Hofer, Vera & Krempl, Georg, 2013. "Drift mining in data: A framework for addressing drift in classification," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 377-391.
    3. Bernd Bischl & Julia Schiffner & Claus Weihs, 2013. "Benchmarking local classification methods," Computational Statistics, Springer, vol. 28(6), pages 2599-2619, December.
    4. Terrén-Serrano, G. & Martínez-Ramón, M., 2023. "Kernel learning for intra-hour solar forecasting with infrared sky images and cloud dynamic feature extraction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    5. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2015. "Forecasting National Recessions Using State‐Level Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(5), pages 847-866, August.
    6. Edgar C. Merkle & Mark Steyvers, 2013. "Choosing a Strictly Proper Scoring Rule," Decision Analysis, INFORMS, vol. 10(4), pages 292-304, December.
    7. Travis J. Berge, 2015. "Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(6), pages 455-471, September.
    8. Raffaella Calabrese, 2012. "Improving Classifier Performance Assessment of Credit Scoring Models," Working Papers 201204, Geary Institute, University College Dublin.
    9. Hand, David J., 2009. "Mining the past to determine the future: Problems and possibilities," International Journal of Forecasting, Elsevier, vol. 25(3), pages 441-451, July.
    10. Rybizki, Lydia, 2014. "Learning cost sensitive binary classification rules accounting for uncertain and unequal misclassification costs," FAU Discussion Papers in Economics 01/2014, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    11. Jordà, Òscar & Taylor, Alan M., 2012. "The carry trade and fundamentals: Nothing to fear but FEER itself," Journal of International Economics, Elsevier, vol. 88(1), pages 74-90.
    12. Xinyu Zhang & Alan T. K. Wan & Sherry Z. Zhou, 2011. "Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 132-142, June.
    13. Werner Ehm & Tilmann Gneiting & Alexander Jordan & Fabian Krüger, 2016. "Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 505-562, June.
    14. Travis J. Berge, 2014. "Forecasting Disconnected Exchange Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 713-735, August.
    15. Czogiel, Irina & Luebke, Karsten & Zentgraf, Marc & Weihs, Claus, 2006. "Localized Linear Discriminant Analysis," Technical Reports 2006,10, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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