= Conditional logistic model for longitudinal binary data
{title2=$\operatorname{logit}P(Y_t=1\mid\mathcal H_{t-1},x)=\eta_t$}
= History-dependent logistic regression
{synonym}
A <logistic regression> can specify each binary response conditionally on recorded past outcomes and baseline <covariates>. A <chain rule for probabilities> gives the path <likelihood> as the product of the conditional <Bernoulli distribution> masses. This permits dependence within a person's history without treating their outcomes as unconditionally independent. The model must specify the initial history and which past features enter its conditional mean.
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