Daniele Durante

Daniele Durante
Università commerciale Luigi Bocconi | Bocconi · Department of Decision Sciences

PhD

About

39
Publications
4,098
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484
Citations
Additional affiliations
September 2017 - present
Università commerciale Luigi Bocconi
Position
  • Professor

Publications

Publications (39)
Article
Full-text available
A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams continue to grow, the complexity associated with these highly multidimensional connectivity data presents new challenges. In this paper, we focus on time-varying interconn...
Article
Replicated network data are increasingly available in many research fields. In connectomic applications, inter-connections among brain regions are collected for each patient under study, motivating statistical models which can flexibly characterize the probabilistic generative mechanism underlying these network-valued data. Available models for a s...
Article
Full-text available
Network data are increasingly measured along with other variables of interest. Our motivation is drawn from neurophysiology studies measuring a brain connectivity network for each individual along with their membership in an low or high creative reasoning group. It is of paramount importance to develop statistical methods for testing of global and...
Article
Full-text available
There is considerable interest in studying how the distribution of an outcome varies with a predictor. We are motivated by environmental applications in which the predictor is the dose of an exposure and the response is a health outcome. A fundamental focus in these studies is inference on dose levels associated with a particular increase in risk r...
Article
Regression models for dichotomous data are ubiquitous in statistics. Besides being useful for inference on binary responses, these methods serve also as building blocks in more complex formulations, such as density regression, nonparametric classification and graphical models. Within the Bayesian framework, inference proceeds by updating the priors...
Preprint
A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including basic Gaussian response settings, this class also encompasses probit, multinomial probit and tobit regression, among others, thereby yielding to one of the most widely-implemented families...
Article
Modern methods for Bayesian regression beyond the Gaussian response setting are often computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent literature, bypassing such a trade-off is still an open problem even in routine binary regression models, and there is limited theory on the quality of variational approx...
Article
Full-text available
Non-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian state-space models-where filtering, predictive and smoothing distributions are available in closed form-binary state-space models require approximations or sequential Monte...
Preprint
Full-text available
Reliably learning group structure among the nodes in network data is challenging in several modern applications. We are particularly motivated by studying covert networks that encode relationships among criminals. These data are subject to measurement errors and often exhibit a complex combination of an unknown number of core-periphery, assortative...
Article
Full-text available
Network data often exhibit block structures characterized by clusters of nodes with similar patterns of edge formation. When such relational data are complemented by additional information on exogenous node partitions, these sources of knowledge are typically included in the model to supervise the cluster assignment mechanism or to improve inferenc...
Preprint
Full-text available
Network data often exhibit block structures characterized by clusters of nodes with similar patterns of edge formation. When such relational data are complemented by additional information on exogenous node partitions, these sources of knowledge are typically included in the model to supervise the cluster assignment mechanism or to improve inferenc...
Preprint
Predictive models for binary data are fundamental in various fields, ranging from spatial statistics to machine learning. In such settings, the growing complexity of the phenomena to be analyzed has motivated a variety of flexible specifications that avoid strong parametric assumptions when defining the relationship between the observed predictors...
Article
The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approa...
Preprint
Stochastic block models (SBM) are widely used in network science due to their interpretable structure that allows inference on groups of nodes having common connectivity patterns. Although providing a well established model-based approach for community detection, such formulations are still the object of intense research to address the key problem...
Preprint
Multinomial probit models are widely-implemented representations which allow both classification and inference by learning changes in vectors of class probabilities with a set of p observed predictors. Although various frequentist methods have been developed for estimation, inference and classification within such a class of models, Bayesian infere...
Chapter
The recent developments in resonance imaging technologies have allowed growing access to a wide variety of complex information on brain functioning. Regardless of the several types of data modalities which are now available, a fundamental interest in the neurosciences is in conducting inference on brain networks. In this article, we discuss statist...
Preprint
State-of-the-art methods for Bayesian inference on regression models with binary responses are either computationally impractical or inaccurate in high dimensions. To cover this gap we propose a novel variational approximation for the posterior distribution of the coefficients in high-dimensional probit regression. Our method leverages a representa...
Article
Brain network data—measuring structural interconnections among brain regions of interest—are increasingly collected for multiple individuals. Moreover, recent analyses provide additional information on the brain regions under study. These predictors typically include the three‐dimensional anatomical coordinates of the regions, and their membership...
Preprint
Non-Gaussian state-space models arise routinely in several applications. Within this framework, the binary time series setting provides a source of constant interest due to its relevance in a variety of studies. However, unlike Gaussian state-space models---where filtering and predictive distributions are available in closed form---binary state-spa...
Preprint
Full-text available
There is a wide variety of models in which the dimension of the parameter space is unknown. For example, in factor analysis the number of latent factors is typically not known and has to be inferred from the observed data. Although classical shrinkage priors are useful in these contexts, increasing shrinkage priors can provide a more effective opti...
Book
This book presents a selection of peer-reviewed contributions to the fourth Bayesian Young Statisticians Meeting, BAYSM 2018, held at the University of Warwick on 2-3 July 2018. The meeting provided a valuable opportunity for young researchers, MSc students, PhD students, and postdocs interested in Bayesian statistics to connect with the broader Ba...
Article
We propose a nested EM routine which guarantees monotone log-likelihood sequences and improved convergence rates in maximum likelihood estimation of latent class models with covariates.
Article
Family planning has been characterized by highly different strategic programmes in India, including method-specific contraceptive targets, coercive sterilization and more recent target-free approaches. These major changes in family planning policies over time have motivated considerable interest towards assessing the effectiveness of the different...
Article
Twenty-eight early-career researchers in statistics, with the support of seven international professors, were given 48 hours to propose methods for state-of-the-art data analysis in neuroscience. Antonio Canale, Daniele Durante, Lucia Paci and Bruno Scarpa report from the scene.
Book
This volume presents a collection of peer-reviewed contributions arising from StartUp Research: a stimulating research experience in which twenty-eight early-career researchers collaborated with seven senior international professors in order to develop novel statistical methods for complex brain imaging data. During this meeting, which was held on...
Article
Full-text available
There is an increasing focus in several fields on learning how the distribution of an outcome changes with a set of covariates. Bayesian nonparametric dependent mixture models provide a useful approach to flexibly address this goal, however many representations are characterized by difficult interpretation and intractable computational methods. Mot...
Article
Full-text available
Our focus is on realistically modeling and forecasting dynamic networks of face-to-face contacts among individuals. Important as- pects of such data that lead to problems with current methods in- clude the tendency of the contacts to move between periods of slow and rapid changes, and the dynamic heterogeneity in the actors' con- nectivity behavior...
Article
Adaptive dimensionality reduction in high-dimensional problems is a key topic in statistics. The multiplicative gamma process prior takes a step in this direction, but improved studies on its properties are required to ease implementation. This note addresses such aim.
Article
Full-text available
Multivariate categorical data are available in many fields of application. We are motivated by election studies assessing evidence of differences in voters' opinions across party affiliation groups. Similar goals arise routinely in several applications but current literature lacks a general methodology which can test for group differences in multiv...
Article
Motivation: There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditiona...
Article
Full-text available
There is growing interest in understanding how the structural interconnections among brain regions change with the occurrence of neurological diseases. Diffusion weighted MRI imaging has allowed researchers to non-invasively estimate a network of structural cortical connections made by white matter tracts, but current statistical methods for relati...
Article
Complex network data problems are increasingly common in many fields of application. Our motivation is drawn from strategic marketing studies monitoring customer preferences for specific products, along with co-subscription networks encoding multi-buying behavior. Data are available for multiple agencies within the same insurance company, and our g...
Article
Full-text available
Symmetric binary matrices representing relations are collected in many areas. Our focus is on dynamically evolving binary relational matrices, with interest being on inference on the relationship structure and prediction. We propose a nonparametric Bayesian dynamic model, which reduces dimensionality in characterizing the binary matrix through a lo...
Article
Full-text available
In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such time-varying smoothness is not accounted for, one can obtain misleading inferences and predictions, with...
Article
We propose a Bayesian nonparametric model including time-varying predictors in dynamic network inference. The model is applied to infer the dependence structure among financial markets during the global financial crisis, estimating effects of verbal and material cooperation efforts. We interestingly learn contagion effects, with increasing influenc...
Article
In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such locally adaptive smoothness is not accounted for, one can obtain misleading inferences and predictions, w...

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