25 citations · 67 across the 17 of their papers we have counts for
20 papers
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…
Directional testing for one-way MANOVA in divergent dimensions
Caizhu Huang, Claudia Di Caterina, Nicola Sartori
Testing the equality of mean vectors across different groups plays an important role in many scientific fields. In regular frameworks, likelihood-based statistics under the nor…
Directional testing for high-dimensional multivariate normal distributions
Caizhu Huang, Claudia Di Caterina, Nicola Sartori
Thanks to its favorable properties, the multivariate normal distribution is still largely employed for modeling phenomena in various scientific fields. However, when the number of…
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…
Parametric bootstrap inference for stratified models with high-dimensional nuisance specifications
Ruggero Bellio, Ioannis Kosmidis, Alessandra Salvan +1
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 stat…
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…
A New Look at -Tests
Andrew McCormack, Nancy Reid, Nicola Sartori +1
Directional inference for vector parameters based on higher order approximations in likelihood inference has recently been developed in the literature. Here we explore examples of…
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…
Median bias reduction in random-effects meta-analysis and meta-regression
Sophia Kyriakou, Ioannis Kosmidis, Nicola Sartori
Random-effects models are frequently used to synthesise information from different studies in meta-analysis. While likelihood-based inference is attractive both in terms of limitin…
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…
Improved Laplace Approximation for Marginal Likelihoods
Erlis Ruli, Nicola Sartori, Laura Ventura
Statistical applications often involve the calculation of intractable multidimensional integrals. The Laplace formula is widely used to approximate such integrals. However, in high…
Empirical and Simulated Adjustments of Composite Likelihood Ratio Statistics
Manuela Cattelan, Nicola Sartori
Composite likelihood inference has gained much popularity thanks to its computational manageability and its theoretical properties. Unfortunately, performing composite likelihood r…
Approximate Bayesian Computation with composite score functions
Erlis Ruli, Nicola Sartori, Laura Ventura
Both Approximate Bayesian Computation (ABC) and composite likelihood methods are useful for Bayesian and frequentist inference, respectively, when the likelihood function is intrac…
A note on marginal posterior simulation via higher-order tail area approximations
Erlis Ruli, Nicola Sartori, Laura Ventura
We explore the use of higher-order tail area approximations for Bayesian simulation. These approximations give rise to an alternative simulation scheme to MCMC for Bayesian computa…
The Banff Challenge: Statistical Detection of a Noisy Signal
A. C. Davison, N. Sartori
Particle physics experiments such as those run in the Large Hadron Collider result in huge quantities of data, which are boiled down to a few numbers from which it is hoped that a…