25 citations · 67 across the 17 of their papers we have counts for
10 papers · 1 filter
Efficient computation of mixture confidence sequences in generalized linear models
Claudia Di Caterina, Luigi Pace, Alessandra Salvan +1
Classical confidence intervals, when repeatedly obtained on accumulating data at different sample sizes, produce contradictory inferences with high probability. We propose a simple…
Focused median bias reduction
Davide Benussi, Ioannis Kosmidis, Alessandra Salvan +1
Median bias reduction of maximum likelihood estimators can substantially improve estimation and inference. Existing generally applicable methods are, however, implicit, requiring t…
Adjusted composite likelihood for robust Bayesian meta-analysis
Michele Lambardi di San Miniato, Nicola Sartori
A composite likelihood is a non-genuine likelihood function that allows to make inference on limited aspects of a model, such as marginal or conditional distributions. Composite li…
Directional tests in Gaussian graphical models
Claudia Di Caterina, Nancy Reid, Nicola Sartori
Directional tests to compare incomplete undirected graphs are developed in the general context of covariance selection for Gaussian graphical models. The exactness of the underlyin…
Accurate inference in negative binomial regression
Euloge Clovis Kenne Pagui, Alessandra Salvan, Nicola Sartori
Negative binomial regression is commonly employed to analyze overdispersed count data. With small to moderate sample sizes, the maximum likelihood estimator of the dispersion param…
Efficient implementation of median bias reduction with applications to general regression models
Euloge Clovis Kenne Pagui, Alessandra Salvan, Nicola Sartori
In numerous regular statistical models, median bias reduction (Kenne Pagui et al., 2017) has proven to be a noteworthy improvement over maximum likelihood, alternative to mean bias…
Mean and median bias reduction in generalized linear models
Ioannis Kosmidis, Euloge Clovis Kenne Pagui, Nicola Sartori
This paper presents an integrated framework for estimation and inference from generalized linear models using adjusted score equations that result in mean and median bias reduction…
Monte Carlo modified profile likelihood in models for clustered data
Claudia Di Caterina, Giuliana Cortese, Nicola Sartori
The main focus of the analysts who deal with clustered data is usually not on the clustering variables, and hence the group-specific parameters are treated as nuisance. If a fixed…
Robust approximate Bayesian inference
Erlis Ruli, Nicola Sartori, Laura Ventura
We discuss an approach for deriving robust posterior distributions from -estimating functions using Approximate Bayesian Computation (ABC) methods. In particular, we use -est…
Median bias reduction of maximum likelihood estimates
Euloge Clovis, Kenne Pagui, Alessandra Salvan +1
For regular parametric problems, we show how median centering of the maximum likelihood estimate can be achieved by a simple modification of the score equation. For a scalar parame…