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Bayesian Estimation of Stress–Strength Parameter for Moran–Downton Bivariate Exponential Distribution Under Progressive Type II Censoring
In the stress–strength model, the estimation of the probability...
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Non-linear INAR(1) processes under an alternative geometric thinning operator
We propose a novel class of first-order integer-valued AutoRegressive (INAR(1)) models based on a new operator, the so-called geometric thinning...
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Limit theorems for branching processes with immigration in a random environment
We investigate branching processes with immigration in a random environment. Using Goldie’s implicit renewal theory we prove that under a generalized...
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Tail processes and tail measures: An approach via Palm calculus
Using an intrinsic approach, we study some properties of random fields which appear as tail fields of regularly varying stationary random fields. The...
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Performance and Optimization Analysis of a Queue with Delayed Uninterrupted Multiple Vacation and N-Policy
This paper considers an M / G /1 queue with delayed uninterrupted multiple vacation and N -policy, in which (i) the server remains dormant from vacation...
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A new estimation for INAR(1) process with Poisson distribution
The first-order Poisson autoregressive model may be suitable in situations where the time series data are non-negative integer valued. In this...
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A Comparative Study of Queuing Systems with Variant of Activation Times and Impatience under N Policy
In this paper, we study single server queues under N policy with various server activation strategies and impatience of customers. Customers arrive...
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Analysis of the Mt/M/1 Queueing System with Impatient Customers and Single Vacation
We consider an M t /M/1 queueing system with impatient customers and a single vacation, assuming the customers’ impatience is due to the server’s...
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Sampling large hyperplane-truncated multivariate normal distributions
Generating multivariate normal distributions is widely used in various fields, including engineering, statistics, finance and machine learning. In...
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A new approach for detecting gradual changes in non-stationary time series with seasonal effects
This paper proposes a new method of detecting the gradual changes of time series when the changes in time series are mixed with seasonality. The key...
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Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach
This paper describes the methodology used by the team RedSea in the data competition organized for EVA 2021 conference. We develop a novel two-part...
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Bayesian Latent Gaussian Models for High-Dimensional Spatial Extremes
In this chapter, we show how to efficiently model high-dimensional extreme peaks-over-threshold events over space in complex non-stationary settings,...
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Spectral Representation
In this chapter we will show that every stationary process can be approximated by a sum of harmonic oscillations with random and uncorrelated...
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Time Series Analysis and Prediction
In this chapter, we present essential parts of time series analysis, with the objective of predicting or forecasting its future development....
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Distribution Theory
This chapter presents exercises on distribution theory relevant to linear models, and provides solutions to those exercises.
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Distribution Theory
Much of classical statistical inference for linear models is based on special cases of those models for which the response vector y has a...
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Tail and Quantile Estimation for Real-Valued \(\boldsymbol{\beta}\)-Mixing Spatial Data
AbstractThis paper deals with extreme-value index estimation of a heavy-tailed distribution of a spatial dependent process. We are particularly...
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Spatial extremes and stochastic geometry for Gaussian-based peaks-over-threshold processes
Geometric properties of exceedance regions above a given quantile level provide meaningful theoretical and statistical characterizations for...
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Parameter Estimation of Standard AR(1) and MA(1) Models Driven by a Non-I.I.D. Noise
The use of a non-i.i.d. noise in parametric modeling of stationary time series can lead to unexpected distortions of the standard errors and...