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Ruin under Light-Tailed or Moderately Heavy-Tailed Insurance Risks Interplayed with Financial Risks
Consider an insurer who makes risk-free or risky investments and hence is exposed to both insurance and financial risks. We investigate the interplay...
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Estimating the Conditional Tail Expectation of Randomly Right-Censored Heavy-Tailed Data
The conditional tail expectation (CTE) is a very useful tool in risk management and one of the best-known risk measures in the realm of insurance....
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Mixtures of regressions using matrix-variate heavy-tailed distributions
Finite mixtures of regressions (FMRs) are powerful clustering devices used in many regression-type analyses. Unfortunately, real data often present...
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Heavy-tailed phase-type distributions: a unified approach
A phase-type distribution is the distribution of the time until absorption in a finite state-space time-homogeneous Markov jump process, with one...
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Alternative skew Laplace scale mixtures for modeling data exhibiting high-peaked and heavy-tailed traits
The search and construction of appropriate and flexible models for describing and modelling empirical data sets incongruent with normality retains a...
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Asymptotic Behavior of Eigenvalues of Variance-Covariance Matrix of a High-Dimensional Heavy-Tailed Lévy Process
In this paper, we study the limiting behavior of eigenvalues of the variance-covariance matrix of a random sample from a multivariate subordinator...
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Speeding up the Zig-Zag Process
The Zig-Zag process is a Piecewise Deterministic Markov Process (PDMP), efficiently used for simulation in an MCMC setting. A generalisation of this...
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Semiparametric Mixed-Effects Ordinary Differential Equation Models with Heavy-Tailed Distributions
Ordinary differential equation (ODE) models are popularly used to describe complex dynamical systems. When estimating ODE parameters from noisy data,...
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Extreme values of linear processes with heavy-tailed innovations and missing observations
We investigate maxima in incomplete samples from strictly stationary random sequences defined as linear models of i.i.d. random variables with...
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Matrix Mittag–Leffler distributions and modeling heavy-tailed risks
In this paper we define the class of matrix Mittag-Leffler distributions and study some of its properties. We show that it can be interpreted as a...
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On the Ruin Probabilities in a Discrete Time Insurance Risk Process with Capital Injections and Reinsurance
The discrete time insurance risk process with a constant interest force is an interesting stochastic model in risk theory. This paper considers the...
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A robust Birnbaum–Saunders regression model based on asymmetric heavy-tailed distributions
Skew-normal/independent distributions provide an attractive class of asymmetric heavy-tailed distributions to the usual symmetric normal...
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Student’s-t process with spatial deformation for spatio-temporal data
Many models for environmental data that are observed in time and space have been proposed in the literature. The main objective of these models is...
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Ornstein - Uhlenbeck Process Driven By \(\alpha\)-stable Process and Its Gamma Subordination
The variety and diversity of phenomena surrounding us and easy access to empirical data require either new and more complicated models that allow to...
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Logistic Quantile Regression for Bounded Outcomes Using a Family of Heavy-Tailed Distributions
Mean regression model could be inadequate if the probability distribution of the observed responses is not symmetric. Under such situation, the...
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Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland
The credibility of microsimulation modeling with the research community and policymakers depends on high-quality baseline surveys. Quality problems...
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Robust semiparametric modeling of mean and covariance in longitudinal data
Longitudinal data often suffer from heavy-tailed errors and outliers, which can significantly reduce efficiency and lead to invalid inferences....
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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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Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions
Despite recent methodological advances in hidden Markov regression models and a rapid increase in their application in a wide range of empirical...
