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Pollutant Dispersion Simulation by Means of a Stochastic Particle Model and a Dynamic Gaussian Plume Model
The pollutant dispersion models of this work fall into two classes: physical and statistical. We propose a large-scale physical particle dispersion...
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Detecting Changes in Dynamic Social Networks Using Multiply-Labeled Movement Data
The social structure of an animal population can often influence movement and inform researchers on a species’ behavioral tendencies. Animal social...
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Robust estimation of functional factor models with functional pairwise spatial signs
Factor model analysis has emerged as a powerful tool to capture the latent dynamic structure of functional data from a dimension-reduction viewpoint....
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Wavelet-L2E Stochastic Volatility Models: an Application to the Water-Energy Nexus
Forecasting commodity markets are difficult due to the time-varying nature and complexity of the financial return series representing these markets....
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A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors
This paper introduces a novel supervised classification method based on dynamic clustering (DC) and K-nearest neighbor (KNN) learning algorithms,...
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Complex Difference System Models for Asymmetric Interaction
Complex difference system models for asymmetric interaction addressed here were first proposed by the author at “The International Conference on...
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Robust density power divergence estimates for panel data models
The panel data regression models have become one of the most widely applied statistical approaches in different fields of research, including social,...
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Specifications tests for count time series models with covariates
We propose a goodness-of-fit test for a class of count time series models with covariates which includes the Poisson autoregressive model with...
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Macroeconomic Forecasting Evaluation of MIDAS Models
We compare the nowcasting and forecasting performance of different variants of MIDAS models (ADL-MIDAS, TF-MIDAS and U-MIDAS) when predicting the GDP...
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Post-processing for Bayesian analysis of reduced rank regression models with orthonormality restrictions
Orthonormality constraints are common in reduced rank models. They imply that matrix-variate parameters are given as orthonormal column vectors....
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Tips and tricks for Bayesian VAR models in
gretl Bayesian Vector Autoregressive models have become the natural response to the dense parametrization often required by multivariate time series...
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Integrated Population Models: Achieving Their Potential
Precise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management....
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Bayesian Hidden Markov Models for Early Warning
We show how Bayesian hidden Markov models may be employed to build early warning systems of particular risky events. The adopted model formulation...
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Rank-based instrumental variable estimation for semiparametric varying coefficient spatial autoregressive models
In this paper, it is aim to propose an instrumental variable rank estimation method for varying coefficient spatial autoregressive models. The newly...
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Robust Forecasting of Multiple Time Series with One-Sided Dynamic Principal Components
Given a high-dimensional vector of time series, we define a class of robust forecasting procedures based on robust one-sided dynamic principal...
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A Hamiltonian Monte Carlo EM algorithm for generalized linear mixed models with spatial skew latent variables
Spatial generalized linear mixed models with skew latent variables are usually used to model discrete spatial responses that have some skewness....
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Dynamic hierarchical Dirichlet processes topic model using the power prior approach
The hierarchical Dirichlet processes (HDP) topic model is a Bayesian nonparametric model that provides a flexible mixed-membership to documents...
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Bayesian Analysis of First-Order Markov Models for Autocorrelated Binary Responses
In many clinical trials, patient outcomes are often binary-valued which are measured asynchronously over time across various dose levels. To account...
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Hidden Markov Models
This chapter introduces hidden Markov models (HMMs), which can be viewed as an extension of mixture models, in which a unit of observation (e.g., a...
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Multi-Level Bayesian Models for Environment Perception
This book deals with selected problems of machine perception, using various 2D and 3D imaging sensors. It proposes several new original methods, and...
