IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v216y2020i1p284-304.html
   My bibliography  Save this article

Pairwise local Fisher and naive Bayes: Improving two standard discriminants

Author

Listed:
  • Otneim, Håkon
  • Jullum, Martin
  • Tjøstheim, Dag

Abstract

The Fisher discriminant is probably the best known likelihood discriminant for continuous data. Another benchmark discriminant is the naive Bayes, which is based on marginals only. In this paper we extend both discriminants by modeling dependence between pairs of variables. In the continuous case this is done by local Gaussian versions of the Fisher discriminant. In the discrete case the naive Bayes is extended by taking geometric averages of pairwise joint probabilities. We also indicate how the two approaches can be combined for mixed continuous and discrete data. The new discriminants show promising results in a number of simulation experiments and real data illustrations.

Suggested Citation

  • Otneim, Håkon & Jullum, Martin & Tjøstheim, Dag, 2020. "Pairwise local Fisher and naive Bayes: Improving two standard discriminants," Journal of Econometrics, Elsevier, vol. 216(1), pages 284-304.
    • Handle: RePEc:eee:econom:v:216:y:2020:i:1:p:284-304
    DOI: 10.1016/j.jeconom.2020.01.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407620300257
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2020.01.019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    2. Rong Zheng & Jiexun Li & Hsinchun Chen & Zan Huang, 2006. "A framework for authorship identification of online messages: Writing‐style features and classification techniques," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 378-393, February.
    3. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    4. Roopesh Ranjan & Tilmann Gneiting, 2010. "Combining probability forecasts," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 71-91, January.
    5. Li, Qi & Racine, Jeffrey S, 2008. "Nonparametric Estimation of Conditional CDF and Quantile Functions With Mixed Categorical and Continuous Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 423-434.
    6. Berentsen, Geir Drage & Kleppe, Tore Selland & Tjøstheim, Dag Bjarne, 2014. "Introducing localgauss, an R Package for Estimating and Visualizing Local Gaussian Correlation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i12).
    7. Tjøstheim, Dag & Hufthammer, Karl Ove, 2013. "Local Gaussian correlation: A new measure of dependence," Journal of Econometrics, Elsevier, vol. 172(1), pages 33-48.
    8. Virginia Lacal & Dag Tjøstheim, 2019. "Estimating and Testing Nonlinear Local Dependence Between Two Time Series," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 648-660, October.
    9. Virginia Lacal & Dag TjØstheim, 2017. "Local Gaussian Autocorrelation and Tests for Serial Independence," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 51-71, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. José-Luis Velázquez-Rodríguez & Yenny Villuendas-Rey & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez, 2020. "A Novel and Simple Mathematical Transform Improves the Perfomance of Lernmatrix in Pattern Classification," Mathematics, MDPI, vol. 8(5), pages 1-46, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Dongxin & Zhang, Feipeng & Yuan, Di & Cai, Yuan, 2024. "Does COVID-19 impact the dependence between oil and stock markets? Evidence from RCEP countries," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 909-939.
    2. Sleire, Anders D. & Støve, Bård & Otneim, Håkon & Berentsen, Geir Drage & Tjøstheim, Dag & Haugen, Sverre Hauso, 2022. "Portfolio allocation under asymmetric dependence in asset returns using local Gaussian correlations," Finance Research Letters, Elsevier, vol. 46(PB).
    3. Bampinas, Georgios & Panagiotidis, Theodore & Politsidis, Panagiotis N., 2023. "Sovereign bond and CDS market contagion: A story from the Eurozone crisis," Journal of International Money and Finance, Elsevier, vol. 137(C).
    4. Nguyen, Quynh Nga & Aboura, Sofiane & Chevallier, Julien & Zhang, Lyuyuan & Zhu, Bangzhu, 2020. "Local Gaussian correlations in financial and commodity markets," European Journal of Operational Research, Elsevier, vol. 285(1), pages 306-323.
    5. Qi Li & Juan Lin & Jeffrey S. Racine, 2013. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 57-65, January.
    6. Michael S. Delgado & Daniel J. Henderson & Christopher F. Parmeter, 2014. "Does Education Matter for Economic Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 334-359, June.
    7. Jeffrey Racine, 2015. "Mixed data kernel copulas," Empirical Economics, Springer, vol. 48(1), pages 37-59, February.
    8. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    9. Das, Sonali & Racine, Jeffrey S., 2018. "Interactive nonparametric analysis of nonlinear systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 290-301.
    10. Georgios Bampinas & Theodore Panagiotidis, 2017. "Oil and stock markets before and after financial crises: A local Gaussian correlation approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(12), pages 1179-1204, December.
    11. Haupt, Harry & Schnurbus, Joachim & Semmler, Willi, 2018. "Estimation of grouped, time-varying convergence in economic growth," Econometrics and Statistics, Elsevier, vol. 8(C), pages 141-158.
    12. Elie Bouri & Rangan Gupta & Shixuan Wang, 2019. "Contagion between Stock and Real Estate Markets: International Evidence from a Local Gaussian Correlation Approach," Working Papers 201917, University of Pretoria, Department of Economics.
    13. Norets, Andriy & Pelenis, Justinas, 2022. "Adaptive Bayesian estimation of conditional discrete-continuous distributions with an application to stock market trading activity," Journal of Econometrics, Elsevier, vol. 230(1), pages 62-82.
    14. Maasoumi, Esfandiar & Racine, Jeffrey S., 2016. "A solution to aggregation and an application to multidimensional ‘well-being’ frontiers," Journal of Econometrics, Elsevier, vol. 191(2), pages 374-383.
    15. Kraus, Daniel & Czado, Claudia, 2017. "D-vine copula based quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 1-18.
    16. Sonali Das & Jeffrey S. Racine, 2016. "Nonparametric Analysis of Complex Nonlinear Systems," Department of Economics Working Papers 2016-07, McMaster University.
    17. Nuno Baetas da Silva & João Sousa Andrade, 2016. "The relationship between social transfers and poverty reduction: A nonparametric approach for the EU-27," GEMF Working Papers 2016-09, GEMF, Faculty of Economics, University of Coimbra.
    18. Simar, Léopold & Vanhems, Anne & Van Keilegom, Ingrid, 2016. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," Journal of Econometrics, Elsevier, vol. 190(2), pages 360-373.
    19. Haupt, Harry & Meier, Verena, 2016. "Dealing with heterogeneity, nonlinearity and club misclassification in growth convergence: A nonparametric two-step approach," Center for Mathematical Economics Working Papers 455, Center for Mathematical Economics, Bielefeld University.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:216:y:2020:i:1:p:284-304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.