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Big Data and Information Analytics

2016, Volume 1, Issue 4: 341-348. doi: 10.3934/bdia.2016014
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Increase statistical reliability without losing predictive power by merging classes and adding variables

  • 1.
    School of Mathematics and Information Sciences, Guangzhou University Guangzhou, 510006, China;
  • 2.
    Clearpier Inc., 1300-121 Richmond St. W. Toronto, Ontario Canada M5H 2K1
  • Published: 01 October 2016

  • It is usually true that adding explanatory variables into a probability model increases association degree yet risks losing statistical reliability. In this article, we propose an approach to merge classes within the categorical explanatory variables before the addition so as to keep the statistical reliability while increase the predictive power step by step.

    Citation: Wenxue Huang, Xiaofeng Li, Yuanyi Pan. Increase statistical reliability without losing predictive power by merging classes and adding variables[J]. Big Data and Information Analytics, 2016, 1(4): 341-348. doi: 10.3934/bdia.2016014

    Related Papers:

  • It is usually true that adding explanatory variables into a probability model increases association degree yet risks losing statistical reliability. In this article, we propose an approach to merge classes within the categorical explanatory variables before the addition so as to keep the statistical reliability while increase the predictive power step by step.


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  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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  • 1. 

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