Results 21 to 30 of about 19,957,777 (200)

Conditional mixture modelling for heavy‐tailed and skewed data

Stat, 2023
Overparameterization is a serious concern for multivariate mixture models as it can lead to model overfitting and, as a result, mixture order underestimation. Parsimonious modelling is one of the most effective remedies in this context. In Gaussian mixture models, the majority of parameters is associated with covariance matrices and parsimonious models
Dong, Aqi, Melnykov, Volodymyr, Wang, Yang, Zhu, Xuwen   +3 more
openaire   +1 more source

Application of the Heavy-tailed Estimation in Financial Data

Journal of Harbin University of Science and Technology, 2019
There exist many marginal distributions of high frequency time series data in the Heavy-tailed distribution which stores a great deal of information in its tail.
CHEN Hai-long, HUANG Fei, XIE Sheng
doaj   +1 more source

Type-I heavy tailed family with applications in medicine, engineering and insurance.

PLoS ONE, 2020
In the present study, a new class of heavy tailed distributions using the T-X family approach is introduced. The proposed family is called type-I heavy tailed family. A special model of the proposed class, named Type-I Heavy Tailed Weibull (TI-HTW) model
Wei Zhao, Saima K Khosa, Zubair Ahmad, Muhammad Aslam, Ahmed Z Afify   +4 more
doaj   +1 more source

Yule–Walker Equations Using a Gini Covariance Matrix for the High-Dimensional Heavy-Tailed PVAR Model

Mathematics, 2021
Gini covariance plays a vital role in analyzing the relationship between random variables with heavy-tailed distributions. In this papaer, with the existence of a finite second moment, we establish the Gini–Yule–Walker equation to estimate the transition
Jin Zou, Dong Han
doaj   +1 more source

Model Selection Test for the Heavy-Tailed Distributions under Censored Samples with Application in Financial Data

International Journal of Financial Studies, 2016
Numerous heavy-tailed distributions are used for modeling financial data and in problems related to the modeling of economics processes. These distributions have higher peaks and heavier tails than normal distributions.
Hanieh Panahi
doaj   +1 more source

Generalized Skew Laplace Random Fields: Bayesian Spatial Prediction for Skew and Heavy Tailed Data

Journal of Statistical Theory and Applications (JSTA), 2021
Earlier works on spatial prediction issue often assume that the spatial data are realization of Gaussian random field. However, this assumption is not applicable to the skewed and kurtosis distributed data.
Mohammad Mehdi Saber, Alireza Nematollahi, Mohsen Mohammadzadeh   +2 more
doaj   +1 more source

On Empirical Risk Minimization with Dependent and Heavy-Tailed Data

, 2021
fixed minor ...
Abhishek Roy 0005, Krishnakumar Balasubramanian 0002, Murat A. Erdogdu   +2 more
openaire   +3 more sources

ℓ1 Major Component Detection and Analysis (ℓ1 MCDA) in Three and Higher Dimensional Spaces

Algorithms, 2014
Based on the recent development of two dimensional ℓ1 major component detection and analysis (ℓ1 MCDA), we develop a scalable ℓ1 MCDA in the n-dimensional space to identify the major directions of star-shaped heavy-tailed statistical distributions with ...
Zhibin Deng, John E. Lavery, Shu-Cherng Fang, Jian Luo   +3 more
doaj   +1 more source

Bayesian Adjustment for Insurance Misrepresentation in Heavy-Tailed Loss Regression

Risks, 2018
In this paper, we study the problem of misrepresentation under heavy-tailed regression models with the presence of both misrepresented and correctly-measured risk factors.
Michelle Xia
doaj   +1 more source

The Arcsine Exponentiated-X Family: Validation and Insurance Application

Complexity, 2020
In this paper, we propose a family of heavy tailed distributions, by incorporating a trigonometric function called the arcsine exponentiated-X family of distributions.
Wenjing He, Zubair Ahmad, Ahmed Z. Afify, Hafida Goual   +3 more
doaj   +1 more source