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Nonparametric estimation of directional highest density regions
Highest density regions (HDRs) are defined as level sets containing sample points of relatively high density. Although Euclidean HDR estimation from...
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Wavelets Based Artificial Neural Network Technique for Forecasting Agricultural Prices
It has been observed that most of the agricultural time series data in general and price data in particular are non-linear, non-stationary,...
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Bridge closure in the road network of Lombardy: a spatio-temporal analysis of the socio-economic impacts
This paper introduces a methodology to evaluate the socio-economic impacts of closure for maintenance of one or more infrastructures of a large and...
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Assessing similarities between spatial point patterns with a Siamese neural network discriminant model
Identifying structural differences among observed point patterns from several populations is of interest in several applications. We use deep...
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Network Science
Network science is a set of techniques, methods, and tools to study patterns in networked structures. The goal of network science is to understand...
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Towards Calculating the Resilience of an Urban Transport Network Under Attack
In this article we present a methodology to calculate the resilience of a simulated cyber-physical Urban Transport Network (UTN) under attack. The...
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The utility of less-common statistical methods for analyzing agricultural systems: focus on kernel density estimation, copula modeling and extreme value theory
A variety of statistical methods have been developed for multivariate analysis of agricultural systems. Some statistical methods are rarely used to...
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Combining assumptions and graphical network into gene expression data analysis
BackgroundAnalyzing gene expression data rigorously requires taking assumptions into consideration but also relies on using information about network...
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Fast estimation of multivariate spatiotemporal Hawkes processes and network reconstruction
We present a fast, accurate estimation method for multivariate Hawkes self-exciting point processes widely used in seismology, criminology, finance...
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Bayesian dynamic network actor models with application to South Korean COVID-19 patient movement data
Motivated by the ongoing COVID-19 pandemic, this article introduces Bayesian dynamic network actor models for the analysis of infected individuals’...
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DBGAN: A Data Balancing Generative Adversarial Network for Mobility Pattern Recognition
Mobility pattern recognition is a central aspect of transportation and data mining research. Despite the development of various machine learning...
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Optimal Liquidation Through a Limit Order Book: A Neural Network and Simulation Approach
We present a learning algorithm based on simulation and neural networks to solve a stochastic optimal control problem with a large state space using...
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A Multiplex Network Approach for Analyzing University Students’ Mobility Flows
This paper proposes a multiplex network approach to analyze the Italian students’ mobility choices from bachelor’s to master’s degrees. We rely upon...
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The Bethe Hessian and Information Theoretic Approaches for Online Change-Point Detection in Network Data
Sequences of networks are currently a common form of network data sets. Identification of structural change-points in a network data sequence is a...
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New Frontiers for Scan Statistics: Network, Trajectory, and Text Data
In this chapter we survey the new theoretical developments and the use of scan statistics in data represented as graphs, trajectories, and text....
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A smooth dynamic network model for patent collaboration data
The development and application of models, which take the evolution of network dynamics into account, are receiving increasing attention. We...
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Asymptotic theory in network models with covariates and a growing number of node parameters
We propose a general model that jointly characterizes degree heterogeneity and homophily in weighted, undirected networks. We present a moment...
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Network vector autoregression with individual effects
In recent years, there has been great interest in using network structure to improve classic statistical models in cases where individuals are...
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Structural learning and estimation of joint causal effects among network-dependent variables
Bayesian networks in the form of Directed Acyclic Graphs (DAGs) represent an effective tool for modeling and inferring dependence relations among...
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A statistical model of neural network learning via the Cramer–Rao lower bound
The neural networks (NN) remain as black boxes, albeit their quite successful stories everywhere. It is mainly because they provide only the complex...
