arXiv subjects

Giuseppe Vinci

Publications and source records attributed to Giuseppe Vinci.

9 recordsLinked to original sources

arXiv2026

Linked factor analysis

Factor models are widely applied to the analysis of multivariate data across disparate fields of research. However, modern scientific data are often incomplete, and estimating a factor model from partially observed data can be very challenging. In this work, we show that if the data are structurally incomplete, the factor model likelihood function can be decomposed into a product of likelihood functions for multiple factor models relative to different observed data subsets. If these factor models are linked together by common parameters, we can obtain complete maximum likelihood estimates of the full factor model parameters. We call this modeling framework Linked Factor Analysis (LINFA). LINFA can be used for covariance matrix completion, dependence estimation, dimension reduction, and data completion. We compute the maximum likelihood estimator through an efficient Expectation-Maximization algorithm, accelerated by a novel Group Vertex Tessellation algorithm. We establish the conditions for the consistency and asymptotic normality of the estimator. We design confidence regions, hypothesis tests, bootstrap algorithms, and methods for selecting the number of factors. Finally, we illustrate the application of LINFA in an extensive simulation study and in the analysis of neuroscience data.

stat.ME↗

arXiv2026

Probabilistic Graphical Models in Astronomy

The field of astronomy is experiencing a data explosion driven by significant advances in observational instrumentation, and classical methods often fall short of addressing the complexity of modern astronomical datasets. Probabilistic graphical models offer powerful tools for uncovering the dependence structures and data-generating processes underlying a wide array of cosmic variables. By representing variables as nodes in a network, these models allow for the visualization and analysis of the intricate relationships that underpin theories of hierarchical structure formation within the universe. We highlight the value that graphical models bring to astronomical research by demonstrating their practical application to the study of exoplanets and host stars.

astro-ph.IM↗

arXiv2025

Covariance matrix completion via auxiliary information

Covariance matrix estimation is an important task in the analysis of multivariate data in disparate scientific fields. However, modern scientific data are often incomplete due to factors beyond the control of researchers, and traditional methods may only yield incomplete covariance matrix estimates. For example, it is impossible to obtain a complete sample covariance matrix if some variable pairs have no joint observations. We propose a novel approach, AuxCov, which exploits auxiliary variables to produce complete covariance matrix estimates from structurally incomplete data. In neuroscience, an example of an auxiliary variable is the distance between neurons, which is typically inversely related to the strength of the neuronal correlation. AuxCov estimates the relationship between the observed correlations and the auxiliary variables via regression, and uses it to predict the missing correlation estimates and to regularize the observed ones. We implement AuxCov using parametric and nonparametric regression methods, and propose procedures for tuning parameter selection and uncertainty quantification. We evaluate the performance of AuxCov through simulations and in the analysis of large-scale neuroscience data.

stat.ME↗

arXiv2024

Forensic Science and How Statistics Can Help It: Evidence, Hypothesis Testing, and Graphical Models

The persistent issue of wrongful convictions in the United States emphasizes the need for scrutiny and improvement of the criminal justice system. While statistical methods for the evaluation of forensic evidence, including glass, fingerprints, and DNA, have significantly contributed to solving intricate crimes, there is a notable lack of national-level standards to ensure the appropriate application of statistics in forensic investigations. We discuss the obstacles in the application of statistics in court, and emphasize the importance of making statistical interpretation accessible to non-statisticians, especially those who make decisions about potentially innocent individuals. We investigate the use and misuse of statistical methods in crime investigations, in particular the likelihood ratio approach. We further describe the use of graphical models, where hypotheses and evidence can be represented as nodes connected by arrows signifying association or causality. We emphasize the advantages of special graph structures, such as object-oriented Bayesian networks and chain event graphs, which allow for the concurrent examination of evidence of various nature.

stat.AP↗

arXiv2024

Neuronal functional connectivity graph estimation with the R package neurofuncon

Researchers continue exploring neurons' intricate patterns of activity in the cerebral visual cortex in response to visual stimuli. The way neurons communicate and optimize their interactions with each other under different experimental conditions remains a topic of active investigation. Probabilistic Graphical Models are invaluable tools in neuroscience research, as they let us identify the functional connections, or conditional statistical dependencies, between neurons. Graphical models represent these connections as a graph, where nodes represent neurons and edges indicate the presence of functional connections between them. We developed the R package neurofuncon for the computation and visualization of functional connectivity graphs from large-scale data based on the Graphical lasso. We illustrate the use of this package with publicly available two-photon calcium microscopy imaging data from approximately 10000 neurons in a 1mm cubic section of a mouse visual cortex.

q-bio.NC↗

arXiv2023

Spiral-Elliptical automated galaxy morphology classification from telescope images

The classification of galaxy morphologies is an important step in the investigation of theories of hierarchical structure formation. While human expert visual classification remains quite effective and accurate, it cannot keep up with the massive influx of data from emerging sky surveys. A variety of approaches have been proposed to classify large numbers of galaxies; these approaches include crowdsourced visual classification, and automated and computational methods, such as machine learning methods based on designed morphology statistics and deep learning. In this work, we develop two novel galaxy morphology statistics, descent average and descent variance, which can be efficiently extracted from telescope galaxy images. We further propose simplified versions of the existing image statistics concentration, asymmetry, and clumpiness, which have been widely used in the literature of galaxy morphologies. We utilize the galaxy image data from the Sloan Digital Sky Survey to demonstrate the effective performance of our proposed image statistics at accurately detecting spiral and elliptical galaxies when used as features of a random forest classifier.

astro-ph.IM↗

arXiv2023

Graph quilting: graphical model selection from partially observed covariances

Graphical model selection is a seemingly impossible task when many pairs of variables are never jointly observed; this requires inference of conditional dependencies with no observations of corresponding marginal dependencies. This under-explored statistical problem arises in neuroimaging, for example, when different partially overlapping subsets of neurons are recorded in non-simultaneous sessions. We call this statistical challenge the "Graph Quilting" problem. We study this problem in the context of sparse inverse covariance learning, and focus on Gaussian graphical models where we show that missing parts of the covariance matrix yields an unidentifiable precision matrix specifying the graph. Nonetheless, we show that, under mild conditions, it is possible to correctly identify edges connecting the observed pairs of nodes. Additionally, we show that we can recover a minimal superset of edges connecting variables that are never jointly observed. Thus, one can infer conditional relationships even when marginal relationships are unobserved, a surprising result! To accomplish this, we propose an $\ell_1$-regularized partially observed likelihood-based graph estimator and provide performance guarantees in population and in high-dimensional finite-sample settings. We illustrate our approach using synthetic data, as well as for learning functional neural connectivity from calcium imaging data.

math.ST↗

arXiv2017

A Sequential Scheme for Large Scale Bayesian Multiple Testing

The problem of large scale multiple testing arises in many contexts, including testing for pairwise interaction among large numbers of neurons. With advances in technologies, it has become common to record from hundreds of neurons simultaneously, and this number is growing quickly, so that the number of pairwise tests can be very large. It is important to control the rate at which false positives occur. In addition, there is sometimes information that affects the probability of a positive result for any given pair. In the case of neurons, they are more likely to have correlated activity when they are close together, and when they respond similarly to various stimuli. Recently a method was developed to control false positives when covariate information, such as distances between pairs of neurons, is available. This method, however, relies on computationally-intensive Markov Chain Monte Carlo (MCMC). Here we develop an alternative, based on Sequential Monte Carlo, which scales well with the size of the dataset. This scheme considers data items sequentially, with relevant probabilities being updated at each step. Simulation experiments demonstrate that the proposed algorithm delivers results as accurately as the previous MCMC method with only a single pass through the data. We illustrate the method by using it to analyze neural recordings from extrastriate cortex in a macaque monkey.

stat.CO↗

arXiv2014

Estimating the distribution of Galaxy Morphologies on a continuous space

The incredible variety of galaxy shapes cannot be summarized by human defined discrete classes of shapes without causing a possibly large loss of information. Dictionary learning and sparse coding allow us to reduce the high dimensional space of shapes into a manageable low dimensional continuous vector space. Statistical inference can be done in the reduced space via probability distribution estimation and manifold estimation.

astro-ph.GA↗