7 days ago · We propose a novel framework for providing a non-parametric dynamic network model--based on a mixture of coupled hierarchical Dirichlet processes-- based on ...

1 day ago · Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society Series B: Statistical Methodology ...

7 days ago · To feed data into our deep learning model, snapshots from MD trajectories are converted into graphs representing the ligand binding structures. Each snapshot is ...

6 days ago · This paper proposes a new joint model by combining the time-series generalized regression neural network (TGRNN) model and the binomially weighted convolutional ...

2 days ago · Gaussian process regression [33] (GPR) is a nonparametric model that applies the Gaussian process prior to regression analysis. Compared with neural network ...

2 days ago · This paper explores the complex dynamics of crowdfunding platforms, particularly focusing on investor behaviour and investment patterns within equity and ...

3 days ago · As we detail below, this refines and weakens existing results that call for both nonparametric ignorability of selection and linearity of the outcome (or ...

5 days ago · All journal articles featured in Journal of the American Statistical Association vol 118 issue 543.

6 days ago · Nonparametric hyperrectangular tolerance and prediction regions for setting multivariate reference regions in laboratory medicine. Statistical Methods in ...

4 days ago · This paper focuses on spatio-temporal (ST) traffic prediction traffic using graph neural networks. Given that ST data consists of non-stationary and complex ...