Semi-parametric Modelling of Excesses above High Multivariate Thresholds with Censored Data

نویسنده

  • ANNE SABOURIN
چکیده

One commonly encountered problem in statistical analysis of extreme events is that very few data are available for inference. This issue is all the more important in multivariate problems that the dependence structure among extremes has to be inferred. In some cases, e.g. in environmental applications, it is sometimes possible to increase the sample size by taking into account historical or incomplete series with partial censoring. In this work, a semi-parametric Dirichlet mixture model for multivariate extremes is adapted to the context of censored data and missing components. The censored likelihood, which is needed for Bayesian inference, has no analytic expression. A data augmentation scheme is introduced, which avoids multivariate integration of the Poisson process intensity over both the censored intervals and the failure region above threshold. Multivariate extremes; censored data; semi parametric Bayesian inference; mixture models; Dirichlet mixture; reversible-jump algorithm.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Semi-parametric modeling of excesses above high multivariate thresholds with censored data

How to include censored data in a statistical analysis is a recurrent issue in statistics. In multivariate extremes, the dependence structure of large observations can be characterized in terms of a non parametric angular measure, while marginal excesses above asymptotically large thresholds have a parametric distribution. In this work, a flexible semi-parametric Dirichlet mixture model for ang...

متن کامل

Meta-heuristic algorithms for parameter estimation of semi-parametric linear regression models

Consider the semi-parametric linear regression model Y = ′X+ , where has an unknown distribution F0. The semi-parametric MLE ̃ of under this set-up is called the generalized semi-parametric MLE(GSMLE).Although the GSML estimation of the linear regression model is statistically appealing, it has never been attempted due to difficulties with obtaining the GSML estimates of and F until recent work...

متن کامل

Assessment of affecting risk factor on relapse and death in patients with thyroid cancer in Khorasan Razavi 2005-2015

Background and Aim: Thyroid cancer is one of the most common endocrine system cancer and rare types of cancers. However, despite to low death rate, the prevalence of this disease is high. Different factors affected on incident, recurrence and death in thyroid cancer patients. This study aimed to identify these factors. Method: In this historical cohort study, 631 cases of thyroid cancer referr...

متن کامل

icenReg: Regression Models for Interval Censored Data in R

The non-parametric maximum likelihood estimator and semi-parametric regression models are fundamental estimators for interval censored data, along with standard fullyparametric regression models. The R-package icenReg is introduced which contains fast, reliable algorithms for fitting these models. In addition, the package contains functions for imputation of the censored response variables and ...

متن کامل

Second-Order Refined Peaks-Over-Threshold Modelling for Heavy-Tailed Distributions

Modelling excesses over a high threshold using the Pareto or generalized Pareto distribution (PD/GPD) is the most popular approach in extreme value statistics. This method typically requires high thresholds in order for the (G)PD to fit well and in such a case applies only to a small upper fraction of the data. The extension of the (G)PD proposed in this paper is able to describe the excess dis...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013