15 citations · 38 across the 10 of their papers we have counts for
10 papers
Births are difficult to predict even with rich survey and full-population register data
Elizaveta Sivak, Emily M. Cantrell, Thomas Emery +109
Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child w…
Approximate Bayesian inference for cumulative probit regression models
Emanuele Aliverti
Ordinal categorical data are routinely encountered in many practical applications. When the primary goal is to construct a regression model for ordinal outcomes, cumulative link mo…
Bayesian local clustering of age-period mortality surfaces across multiple countries
Giovanni Romanò, Emanuele Aliverti, Daniele Durante
Although traditional literature on mortality modeling has focused on single countries in isolation, recent contributions have progressively moved toward joint models for multiple c…
Conjugate priors and bias reduction for logistic regression models
Tommaso Rigon, Emanuele Aliverti
Logistic regression models for binomial responses are routinely used in statistical practice. However, the maximum likelihood estimate may not exist due to data separability. We ad…
Dynamic modeling of the Italians' attitude towards Covid-19
Emanuele Aliverti, Massimilano Russo
We analyze repeated cross-sectional survey data collected by the Institute of Global Health Innovation, to characterize the perception and behavior of the Italian population during…
Dynamic modeling of mortality via mixtures of skewed distribution functions
Emanuele Aliverti, Stefano Mazzuco, Bruno Scarpa
There has been growing interest on forecasting mortality. In this article, we propose a novel dynamic Bayesian approach for modeling and forecasting the age-at-death distribution,…
Stratified stochastic variational inference for high-dimensional network factor model
Emanuele Aliverti, Massimiliano Russo
There has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on M…
Composite mixture of log-linear models for categorical data
Emanuele Aliverti, David B. Dunson
Multivariate categorical data are routinely collected in many application areas. As the number of cells in the table grows exponentially with the number of variables, many or even…
Projected -SNE for batch correction
Emanuele Aliverti, Jeff Tilson, Dayne Filer +6
Biomedical research often produces high-dimensional data confounded by batch effects such as systematic experimental variations, different protocols and subject identifiers. Withou…
Removing the influence of a group variable in high-dimensional predictive modelling
Emanuele Aliverti, Kristian Lum, James E. Johndrow +1
In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise unde…