Logistic regression for binary responses
With independent Bernoulli Yᵢ, logistic regression assumes log[pᵢ/(1−pᵢ)]=xᵢᵀβ.
Key knowledge
With independent Bernoulli Yᵢ, logistic regression assumes log[pᵢ/(1−pᵢ)]=xᵢᵀβ. Its coefficients are fitted by maximizing Bernoulli likelihood; inverse-information standard errors rely on correct model specification and regular asymptotics.
Conditions and scope
- 0<pᵢ<1 and a fixed declared design; inference requires estimable coefficients and regularity.
- Classification performance alone does not validate inferential standard errors.
Sources · 1
References
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Evidence and review
Lecture 26 model/likelihood and Remark 26.2 specification warning; no unconditional existence or finite-sample inference guarantee.
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