3 citations · 3 across the 6 of their papers we have counts for
9 papers · 1 filter
Bayesian Multi-Group Functional Factor Models with Parameter-Expanded Cumulative Shrinkage Priors
Xuanye Dai, Anna Gottard, Michele Guindani +1
Functional data consist of trajectories observed over a continuous domain, such as time, space, or wavelength. Here we consider curves observed on different groups of subjects and…
A Bayesian Approach for Inference on Mixed Graphical Models
Mauro Florez, Anna Gottard, Carrie McAdams +2
Mixed data refers to a type of data in which variables can be of multiple types, such as continuous, discrete, or categorical. This data is routinely collected in various fields, i…
Bayesian Controlled FDR Variable Selection via Parameter-Expanded Latent Knockoffs
Lorenzo Focardi-Olmi, Anna Gottard, Michele Guindani +1
In many research fields, researchers aim to identify significant associations between a set of explanatory variables and a response while controlling the FDR. The Knockoff filter h…
Clustering Computer Mouse Tracking Data with Informed Hierarchical Shrinkage Partition Priors
Ziyi Song, Weining Shen, Marina Vannucci +4
Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive process…
Bayesian Bivariate Conway-Maxwell-Poisson Regression Model for Correlated Count Data in Sports
Mauro Florez, Michele Guindani, Marina Vannucci
Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data a…
Local Level Dynamic Random Partition Models for Changepoint Detection
Alice Giampino, Bernardo Nipoti, Marina Vannucci +1
Motivated by an increasing demand for models that can effectively describe features of complex multivariate time series, e.g. from sensor data in biomechanics, motion analysis, and…