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August 11, 2025Statistical Analysis and Data Mining The ASA Data Science Journal

Testing for the Important Components of Predictive Variance

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Authors

DDDean DustinUniversity of Nebraska–LincolnSGSouparno GhoshUniversity of Nebraska–LincolnBCBertrand ClarkeUniversity of Nebraska–Lincoln

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Implication

Analysis reveals key factors influencing predictive variance through random variables, indicating model importance.

Key Points

  • Identify which discrete random variables significantly contribute to predictive variance, enabling better modeling decisions.
  • The decomposition method uses the law of total variance to assess the influence of random variables on predictions.
  • Assessment highlights connections to Cochran's theorem, facilitating insights into variance across different factors.
  • This approach can improve understanding of predictive models and assists in simplifying complex modeling aspects.

Cite This Study

Dustin et al. (2025) studied this question.

synapsesocial.com/papers/68a35ef30a429f7973328289https://doi.org/10.1002/sam.70029
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Also Consider

Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification1954 · 1,528 citations
  2. 2High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking2018 · 33 citations