For regular parametric problems, we show how median centring of the maximum likelihood
estimate can be achieved by a simple modification of the score equation. For a scalar …

We showcase some unexplored connections between saddlepoint approximations,
measure transportation, and some key topics in information theory. To bridge these different …

Classical statistical inference relies mostly on parametric models and on optimal procedures
which are mostly justified by their asymptotic properties when the data generating process …

We offer an exposition of modern higher order likelihood inference and introduce software to
implement this in a quite general setting. The aim is to make more accessible an important …

We introduce innovative inference procedures for analyzing time series data. Our
methodology enables density approximation and composite hypothesis testing based on …

Saddlepoint techniques provide numerically accurate, small sample approximations to the
distribution of estimators and test statistics. Except for a few simple models, these …

Scoring rules give rise to methods for statistical inference and are useful tools to achieve
robustness or reduce computations. Scoring rule inference is generally performed through …

Likelihood-based methods of statistical inference provide a useful general methodology that
is appealing, as a straightforward asymptotic theory can be applied for their implementation …

This estimated sampling distribution is then used to construct an accurate confidence set II i
SB I i sb (IY i), with the property, valid assuming only correctness of the model IF i (I yi; I i) …

Inference about a scalar parameter of interest typically relies on the asymptotic normality of
common likelihood pivots, such as the signed likelihood root, the score and Wald statistics …