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Estimation and bootstrap for stochastically monotone Markov processes
The Markov property is shared by several popular models for time series such as autoregressive or integer-valued autoregressive processes as well as...
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Discrete-Time Markov Chain
Markov chains serve as one of the most important methods in the application of probability theory to real-world models involving uncertainty. Markov...
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Matrix-Variate Hidden Markov Regression Models: Fixed and Random Covariates
Two families of matrix-variate hidden Markov regression models (MV-HMRMs) are here introduced. The distinction between them relies on the role of the...
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High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields
The conditional extremes framework allows for event-based stochastic modeling of dependent extremes, and has recently been extended to spatial and...
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Optimal Stock Portfolio Selection with a Multivariate Hidden Markov Model
The underlying market trends that drive stock price fluctuations are often referred to in terms of bull and bear markets. Optimal stock portfolio...
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Mean convergence theorems for arrays of dependent random variables with applications to dependent bootstrap and non-homogeneous Markov chains
This paper provides sets of sufficient conditions for mean convergence theorems for arrays of dependent random variables. We expand and improve a...
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A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models
Multi-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the...
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Strong Approximation of Bessel Processes
We consider the path approximation of Bessel processes and develop a new and efficient algorithm. This study is based on a recent work by the...
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Gerber-Shiu Function for a Class of Markov-Modulated Lévy Risk Processes with Two-Sided Jumps
We investigate the Gerber-Shiu discounted penalty function for Markov-modulated Lévy risk processes with random incomes. Firstly, we consider the...
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A synthetic likelihood approach for intractable markov random fields
We propose a new scalable method to approximate the intractable likelihood of the Potts model. The method decomposes the original likelihood into...
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Jump Markov chains and rejection-free Metropolis algorithms
We consider versions of the Metropolis algorithm which avoid the inefficiency of rejections. We first illustrate that a natural Uniform Selection...
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A copula-based Markov chain model for serially dependent event times with a dependent terminal event
Copula modeling for serial dependence has been extensively discussed in a time series context. However, fitting copula-based Markov models for...
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On the Convergence Time of Some Non-Reversible Markov Chain Monte Carlo Methods
It is commonly admitted that non-reversible Markov chain Monte Carlo (MCMC) algorithms usually yield more accurate MCMC estimators than their...
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Copula and Markov Models
This chapter introduces the basic concepts on copulas and Markov models. We review the formal definition of copulas with its fundamental properties....
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Semiparametric predictive inference for failure data using first-hitting-time threshold regression
The progression of disease for an individual can be described mathematically as a stochastic process. The individual experiences a failure event when...
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Markov Models for Time Series Analysis
A time series is a series of observations of a quantity of interest. Markov models are commonly used in applications to take into account the...
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Statistical Analysis of Generalized Jackson Network with Unreliable Servers via Strong Approximation
We consider a Jackson network with regenerative input flows in which every server is subject to a random environment influence generating breakdowns...
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Unbiased Simulation of Rare Events in Continuous Time
For rare events described in terms of Markov processes, truly unbiased estimation of the rare event probability generally requires the avoidance of...
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Ranks, copulas, and permutons
We review a recent development at the interface between discrete mathematics on one hand and probability theory and statistics on the other,...
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The Markovian Shot-noise Risk Model: A Numerical Method for Gerber-Shiu Functions
In this paper, we consider discounted penalty functions, also called Gerber-Shiu functions, in a Markovian shot-noise environment. At first, we...
