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Journals / Hacettepe Journal of Mathematics and Statistics / 2004 / Cilt: 33 - Sayı:

Stochastic covariates in binary regression

Süleyman GÜNAY
Evrim ORAL
Pages
97–109
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

We show that our solutions are much more precise than those obtained by treating X as non-stochastic when, in fact, it is stochastic.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Binary regression has many medical applications.
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Key Findings & Data:In applying the technique, the tradition is to assume the risk factor X as a non-stochastic variable.
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Conclusion & Impact:We show that our solutions are much more precise than those obtained by treating X as non-stochastic when, in fact, it is stochastic.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Binary regression has many medical applications. In applying the technique, the tradition is to assume the risk factor X as a non-stochastic variable. In most situations, however, X is stochastic. In this study, we discuss the case when X is stochastic in nature, which is more realistic from a practical point of view than X being non-stochastic. We show that our solutions are much more precise than those obtained by treating X as non-stochastic when, in fact, it is stochastic.

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Cite This Article

GÜNAY, S., ORAL, E. (2022). Stochastic covariates in binary regression. Hacettepe Journal of Mathematics and Statistics, Cilt: 33 - Sayı:, 97–109. https://ajindex.com/articles/530813

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