AN ALT PROPORTIONAL HAZARD-PROPORTIONAL ODDS MODEL

被引:0
|
作者
Huang, T. [1 ]
Elsayed, E. A. [1 ]
Jiang, T. [2 ]
机构
[1] Rutgers State Univ, Dept Ind & Syst Engn, Piscataway, NJ 08854 USA
[2] Beihang Univ, Dept Syst Engn & Engn Technol, Beijing 100083, Peoples R China
来源
14TH ISSAT INTERNATIONAL CONFERENCE ON RELIABILITY AND QUALITY IN DESIGN, PROCEEDINGS | 2008年
关键词
Accelerated life testing; Proportional hazard model; Proportional odds model; Parameter family; Transformation parameter;
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Accelerated life testing (ALT) is used to obtain failure data of products in a short time period and extrapolate the reliability of the products under normal operating conditions. Estimation of the reliability is based on models, which can be classified to parametric and non-parametric models. Non-parametric models are commonly used because of the distribution-free property. Proportional hazard model (PHM) and proportional odds model (POM) are two widely used non-parametric models for reliability prediction based on ALT data. These two models perform well if the underlying distributions are Weibull and log-logistic respectively. However, in some situations the test specimens may be obtained from a mixed population and using PHM or POM will result in inaccurate estimates of the reliability at normal operating conditions. In this paper, a proportional hazard-proportional odds (PH-PO) model is developed in order to obtain more accurate estimation for the failure time distributions of Weibull, log-logistic and a mixture of the two. This PH-PO model is based on a parameter family which makes PHM and POM special cases of the model. The performance of the PH-PO model is verified numerically using simulated data. The results show that in general the PH-PO model gives more accurate estimations of reliability.
引用
收藏
页码:39 / +
页数:2
相关论文
共 50 条
  • [1] Proportional Hazard Model and Proportional Odds Model under Dependent Truncated Data
    Hsieh, Jin-Jian
    Chen, Yun-Jhu
    AXIOMS, 2022, 11 (10)
  • [2] PROPORTIONAL ODDS MODEL
    PETERSON, B
    HARRELL, FE
    BIOMETRICS, 1992, 48 (01) : 325 - 326
  • [3] Model Diagnostics for Proportional and Partial Proportional Odds Models
    O'Connell, Ann A.
    Liu, Xing
    JOURNAL OF MODERN APPLIED STATISTICAL METHODS, 2011, 10 (01) : 139 - 175
  • [4] Proportional Odds Hazard Model for Discrete Time-to-Event Data
    Vieira, Maria Gabriella Figueiredo
    Cardial, Marcilio Ramos Pereira
    Matsushita, Raul
    Nakano, Eduardo Yoshio
    AXIOMS, 2023, 12 (12)
  • [5] PROPORTIONAL ODDS MODEL - REPLY
    BRANT, R
    BIOMETRICS, 1992, 48 (01) : 326 - 326
  • [6] On testing proportional odds assumptions for proportional odds models
    Liu, Anqi
    He, Hua
    Tu, Xin M.
    Tang, Wan
    GENERAL PSYCHIATRY, 2023, 36 (03)
  • [7] On the proportional odds model in survival analysis
    Kirmani, SNUA
    Gupta, RC
    ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 2001, 53 (02) : 203 - 216
  • [8] Computing Estimates in the Proportional Odds Model
    David R. Hunter
    Kenneth Lange
    Annals of the Institute of Statistical Mathematics, 2002, 54 : 155 - 168
  • [9] Variable selection for proportional odds model
    Lu, Wenbin
    Zhang, Hao H.
    STATISTICS IN MEDICINE, 2007, 26 (20) : 3771 - 3781
  • [10] Optimal design for the proportional odds model
    Perevozskaya, I
    Rosenberger, WF
    Haines, LM
    CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2003, 31 (02): : 225 - 235