Add to ORKGThe occurrence of extreme phenomena and their devastating impact have been on the agenda, especially in areas of environmental and economic-financial sciences, extending to insurance activity. The theory of extreme values allows an adequate approach in the statistical study of data associated with this type of phenomena. Heavy tail models thus play an important role and are increasingly a resource. In this work we will revisit some max/min-autoregressive and maximum-moving models and contribute to their characterization by deriving their autocorrelation structure based on the Spearman and Kendall coefficients, both useful tools in the identification of models in real data applications.The author was financed by Portuguese Funds through FCT...
We propose a dynamic semi-parametric framework to study time variation in tail parameters. The frame...
In this paper, we consider first-orderMARMAorARMAXprocesses and amodified version of these involvin...
Extreme data points are important in environmental, financial, and insurance settings. In this work,...
summary:In what concerns extreme values modeling, heavy tailed autoregressive processes defined with...
Time series analysis has emerged as one of the most important statistical discipline and it has been...
Extreme-value theory is the branch of statistics concerned with modelling the joint tail of a multiv...
Pareto processes are suitable to model stationary heavy-tailed data. Here, we consider the auto-reg...
This paper addresses the problem of estimating the tail index Α of distributions with heavy, Pareto-...
In this thesis we deal with statistical inference related to extreme value phenomena.\ud Specificall...
Max-autoregressive models for time series data are useful when we want to make inference about rare...
Pareto processes are more suitable for time series with heavy tailed marginals than the classical ga...
The need to model rare events of univariate time series has led to many recent advances in theory an...
Pareto processes are suitable to model stationary heavy-tailed data. Here, we consider the auto-reg...
2022 Summer.Includes bibliographical references.In order to capture the dependence in the upper tail...
Heavy-tailed autoregressive processes defined with minimum or maximum operator are good alternative...
We propose a dynamic semi-parametric framework to study time variation in tail parameters. The frame...
In this paper, we consider first-orderMARMAorARMAXprocesses and amodified version of these involvin...
Extreme data points are important in environmental, financial, and insurance settings. In this work,...
summary:In what concerns extreme values modeling, heavy tailed autoregressive processes defined with...
Time series analysis has emerged as one of the most important statistical discipline and it has been...
Extreme-value theory is the branch of statistics concerned with modelling the joint tail of a multiv...
Pareto processes are suitable to model stationary heavy-tailed data. Here, we consider the auto-reg...
This paper addresses the problem of estimating the tail index Α of distributions with heavy, Pareto-...
In this thesis we deal with statistical inference related to extreme value phenomena.\ud Specificall...
Max-autoregressive models for time series data are useful when we want to make inference about rare...
Pareto processes are more suitable for time series with heavy tailed marginals than the classical ga...
The need to model rare events of univariate time series has led to many recent advances in theory an...
Pareto processes are suitable to model stationary heavy-tailed data. Here, we consider the auto-reg...
2022 Summer.Includes bibliographical references.In order to capture the dependence in the upper tail...
Heavy-tailed autoregressive processes defined with minimum or maximum operator are good alternative...
We propose a dynamic semi-parametric framework to study time variation in tail parameters. The frame...
In this paper, we consider first-orderMARMAorARMAXprocesses and amodified version of these involvin...
Extreme data points are important in environmental, financial, and insurance settings. In this work,...