modeling loss data by phase-type distribution

Modeling Leukemia in Children Using Phase-type Distribution

Background: In this study, with the aim of modeling Leukemia in children using Phase-type distribution, three transitional phases including diagnosis, brain metastasis and testis/ovary metastasis, and one absorotion phase of recovery/death have been considered. The distribution was fitted and the probabilities of death or recovery were determined based on the independent variab...

Modeling the Loss Distribution

This paper focuses on modeling and predicting the loss distribution for credit risky assets such as bonds or loans. We directly model the two components of loss — the default probabilities and the recovery rates given default, and capture the dependence between them through shared covariates. Using an extensive default and recovery data set, we demonstrate the limitations of standard metrics of...

2D Data Modeling by Probability Distribution

Applied science and mechanics need mathematical methods for 2D processes modeling using the set of data points. A novel method of Hurwitz-Radon Matrices (MHR) is used in 2D curve modeling. Proposed method is based on the family of Hurwitz-Radon matrices which possess columns composed of orthogonal vectors. Two-dimensional process is modeled via different functions: sine, cosine, tangent, logari...

Inference about the Burr Type III Distribution under Type-II Hybrid Censored Data

This paper presents the statistical inference on the parameters of the Burr type III distribution, when the data are Type-II hybrid censored. The maximum likelihood estimators are developed for the unknown parameters using the EM algorithm method. We provided the observed Fisher information matrix using the missing information principle which is useful for constructing the asymptotic confidence...

Modeling the Distribution of Financial Returns by Functional Data Analysis

In this paper, we use functional data analysis to model a time varying unconditional distribution of financial intraday returns. This is in the spirit of the recent development of realized volatility modeling (e.g. Andersen et al, 2001), where one of the moments of this uncondional distribution, the realized volatility, is assumed to change smoothly over time. In the approach used in this paper...

Modeling Spatial Distribution of Intracellular Organelles by Molecular Motors