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A dynamic network model to measure exposure concentration in the Austrian interbank market
Motivated by an original financial network dataset, we develop a statistical methodology to study non-negatively weighted temporal networks. We focus...
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Parametric quantile autoregressive moving average models with exogenous terms
Parametric autoregressive moving average models with exogenous terms (ARMAX) have been widely used in the literature. Usually, these models consider...
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Assessing Ecosystem State Space Models: Identifiability and Estimation
Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and...
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Exploring local explanations of nonlinear models using animated linear projections
The increased predictive power of machine learning models comes at the cost of increased complexity and loss of interpretability, particularly in...
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Multi-step estimators and shrinkage effect in time series models
Many modern statistical models are used for both insight and prediction when applied to data. When models are used for prediction one should optimise...
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Modeling Structural Breaks in Disturbances Precision or Autoregressive Parameter in Dynamic Model: A Bayesian Approach
The focus of this paper is the examination of dynamic models in the presence of structural changes either due to disturbances precision or...
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Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models
Occupancy models are frequently used by ecologists to quantify spatial variation in species distributions while accounting for observational biases...
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Inference for Quasi-reaction Models with Covariate-Dependent Rates
Statistical models of quasi-reaction systems are typically described by constant reaction rates. This assumption is too restrictive in many...
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Dynamic Copulas for Monotonic Dependence Change in Time Series
A particular class of dynamic bivariate copulas, monotonically increasing or decreasing, is studied for modeling dependence in a time series. As...
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A copula formulation for multivariate latent Markov models
We specify a general formulation for multivariate latent Markov models for panel data, where outcomes are possibly of mixed-type (categorical,...
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Identification of canonical models for vectors of time series: a subspace approach
We propose a new method to specify linear models for vectors of time series with some convenient properties. First, it provides a unified modeling...
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Variable Selection for Hidden Markov Models with Continuous Variables and Missing Data
We propose a variable selection method for multivariate hidden Markov models with continuous responses that are partially or completely missing at a...
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Pairwise Markov Models and Hybrid Segmentation Approach
The article studies segmentation problem (also known as classification problem) with pairwise Markov models (PMMs). A PMM is a process where the...
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Dynamic Models
A graph or hypergraphHypergraph is a static representation of all possible interactions between nodes. However, due to these same interactions, the...
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Linear models with time-varying parameters: a comparison of different approaches
Estimation of linear models with time-varying parameters can be accomplished in a variety of ways, each making different assumptions, with varying...
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Poisson degree corrected dynamic stochastic block model
Stochastic Block Model (SBM) provides a statistical tool for modeling and clustering network data. In this paper, we propose an extension of this...
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State Space Models and Markov Switching Models
The state space methods or models provide a unified and flexible methodology and technology for handling a wide range of problems in time series...
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Fair-DSP: Fair Dynamic Survival Prediction on Longitudinal Electronic Health Record
Scarce medical resources and highly transmissible diseases may overwhelm healthcare infrastructure. Fair allocation based on disease progression and...
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Bayesian Computations for Reliability Analysis in Dynamic Environments
In this chapter, we consider systems operating under a dynamic environment that causes changes in the failure characteristics of the system. We...
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