Median bias reduction of maximum likelihood estimators can substantially improve estimation and inference. Existing generally applicable methods are, however, implicit, requiring the solution of nonlinear systems of estimating equations for a specified parameterization. Their application to parameter transformations often involves tedious algebra and bespoke implementations. We develop an explicit median bias-corrected estimator for focus parameters that are smooth scalar transformations of a chosen reference parameterization. The estimator results from solving an equation derived from the Cornish-Fisher expansion of the centred and scaled maximum likelihood estimator of the focus parameter, and requires only the ML or an asymptotically equivalent estimator at the reference parameterization, the gradient and Hessian of the transformation, and expectations of products of log-likelihood derivatives. These expectations are available for many models in the bias reduction literature and can also be estimated by Monte Carlo simulation. The resulting estimators are third-order median unbiased and provide one-step approximations to estimators from implicit median bias reduction when the reference parameterization includes the focus parameter. The method can improve standard asymptotic inference and enables hull-based confidence procedures to produce intervals with near nominal finite-sample coverage under median bias control. We illustrate the framework through post-selection inference using the Focused Information Criterion, Mahalanobis distances, quantiles, and scalar focus parameters in regression, stratified, circular, and multiple-mediator models.

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