Results 81 to 90 of about 19,957,777 (200)

Modeling Heavy Tail Data With Bayesian Nonparametric Mixtures

Statistical Analysis and Data Mining: An ASA Data Science Journal
ABSTRACT In the study of heavy tail data, several models have been introduced. If the interest is in the tail of the distribution, block maxima or excess over thresholds are the typical approaches, wasting relevant information in the bulk of the data. To avoid this, mixture models for the body (below the threshold) and the tail (above
openaire   +2 more sources

Portfolio Diversification Under Local, Moderate and Global Deviations From Power Laws [PDF]

This paper focuses on the analysis of portfolio diversification for a wide class of nonlinear transformations of heavy-tailed risks. We show that diversification of a portfolio of nonlinear transformations of thick-tailed risks increases riskiness if ...
Johan Walden, Rustam Ibragimov
core  

Shifting paradigms: on the robustness of economic models to heavy-tailedness assumptions [PDF]

The structure of many models in economics and finance depends on majorization properties of convolutions of distributions. In this paper, we analyze robustness of these properties and the models based on them to heavy-tailedness assumptions.
Rustam Ibragimov
core  

Evaluating Agreement Among Multiple Methods With Replicated Measurements Using Scale Mixtures of Skew-Normal Measurement Error Models

Journal of Probability and Statistics
This paper introduces a robust parametric approach for assessing the agreement among multiple measurement methods when dealing with replicated data from a continuous variable.
Jeevana Duwarahan, Lakshika S. Nawarathna   +1 more
doaj   +1 more source

On Tail Index Estimation for Dependent, Heterogenous Data [PDF]

In this paper we analyze the asymptotic properties of the popular distribution tail index estimator by B. Hill (1975) for possibly heavy- tailed, heterogenous, dependent processes.
Jonathan B. Hill
core  

The Limits of Diversification When Losses May Be Large [PDF]

Recent results in value at risk analysis show that, for extremely heavy-tailed risks with unbounded distribution support, diversification may increase value at risk, and that, generally, it is difficult to construct an appropriate risk measure for such ...
Johan Walden, Rustam Ibragimov
core  

Estimating Skewness and Kurtosis for Asymmetric Heavy-Tailed Data: A Regression Approach

Mathematics
Estimating skewness and kurtosis from real-world data remains a long-standing challenge in actuarial science and financial risk management, where these higher-order moments are critical for capturing asymmetry and tail risk.
Joseph H. T. Kim, Heejin Kim
doaj   +1 more source

Inference for double Pareto lognormal queues with applications [PDF]

In this article we describe a method for carrying out Bayesian inference for the double Pareto lognormal (dPlN) distribution which has recently been proposed as a model for heavy-tailed phenomena. We apply our approach to inference for the dPlN/M/1 and M/
Michael P. Wiper, Rosa E. Lillo, Pepa Ramirez, Simon P. Wilson   +3 more
core  

Predicting the Remaining Useful Life of Turbofan Engines Using Fractional Lévy Stable Motion with Long-Range Dependence

Fractal and Fractional
Remaining useful life prediction guarantees a reliable and safe operation of turbofan engines. Long-range dependence (LRD) and heavy-tailed characteristics of degradation modeling make this method advantageous for the prediction of RUL. In this study, we
Deyu Qi, Zijiang Zhu, Fengmin Yao, Wanqing Song, Aleksey Kudreyko, Piercarlo Cattani, Francesco Villecco   +6 more
doaj   +1 more source

The Lambert Way to Gaussianize Heavy-Tailed Data with the Inverse of Tukey's h Transformation as a Special Case. [PDF]

ScientificWorldJournal, 2015
Goerg GM.
europepmc   +1 more source