On weighted least squares estimation for parameters of the two-parameter Weibull distribution

被引:0
|
作者
Zhang, L. F. [1 ]
Xie, M. [1 ]
Tang, L. C. [1 ]
机构
[1] Natl Univ Singapore, Dept Ind & Syst Engn, Kent Ridge Crescent, Singapore 119260, Singapore
来源
TWELFTH ISSAT INTERNATIONAL CONFERENCE RELIABILITY AND QUALITY IN DESIGN, PROCEEDINGS | 2006年
关键词
Weibull distribution; parameter estimation; weighted least squares estimation; mean square error;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper presents an alternative method for calculating weights to be used in weighted least squares estimation (WLSE) technique for estimating the two Weibull parameters. As a common practice, weights are calculated by the reciprocals of the variances of predictor variable values. The existing WLSE methods including Bergman [101, Faucher and Tyson [11], Hung [12] and Lu et al. [13] use approximated values of the variances to calculate weights. In fact, the exact values of the variances of predictor variable values can be deducted through analytical analysis. The present paper describes the method for deducing the exact values of the variances, and also provides an approximation formula to simplify the calculation. Step-by-step procedures are provided for the proposed WLSE technique. Simulation results show that for estimating the shape parameter, the proposed procedure is more accurate than the existing WLSE methods and always generates smallest mean square error (MSE).
引用
收藏
页码:318 / +
页数:2
相关论文
共 50 条
  • [21] An evaluation of extended vs weighted least squares for parameter estimation in physiological modeling
    Spilker, ME
    Vicini, P
    JOURNAL OF BIOMEDICAL INFORMATICS, 2001, 34 (05) : 348 - 364
  • [22] Bayesian estimation for environmental factor of two parameter of Weibull distribution
    Li, Feng
    Shi, Yi-Min
    Jing, Yuan
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2008, 30 (01): : 186 - 189
  • [23] Statistical analysis of wind speed using two-parameter Weibull distribution in Alacati region
    Ozay, Can
    Celiktas, Melih Soner
    ENERGY CONVERSION AND MANAGEMENT, 2016, 121 : 49 - 54
  • [24] Two-parameter Weibull Distribution Theory Testing Analysis in Fatigue Life of Asphalt Mixture
    Jie, Sun
    Yu, Jiangmiao
    Zhao, Haisheng
    ADVANCED TRANSPORTATION, PTS 1 AND 2, 2011, 97-98 : 45 - +
  • [25] Three-parameter vs. two-parameter Weibull distribution for pultruded composite material properties
    Alqam, M
    Bennett, RM
    Zureick, AH
    COMPOSITE STRUCTURES, 2002, 58 (04) : 497 - 503
  • [26] A Weighted Least-Squares Approach to Parameter Estimation Problems Based on Binary Measurements
    Colinet, Eric
    Juillard, Jerome
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2010, 55 (01) : 148 - 152
  • [27] Nonlinear Least Squares Estimation for Parameters of Mixed Weibull Distributions by Using Particle Swarm Optimization
    Lu, Zhong
    Dong, Li
    Zhou, Jia
    IEEE ACCESS, 2019, 7 : 60545 - 60554
  • [28] Estimating Weibull Parameters Using Least Squares and Multilayer Perceptron vs. Bayes Estimation
    Aydi, Walid
    Alduais, Fuad S.
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 71 (02): : 4033 - 4050
  • [29] The two-parameter Weibull distribution as a universal tool to model the variation in species relative abundances
    Ulrich, Werner
    Nakadai, Ryosuke
    Matthews, Thomas J.
    Kubota, Yasuhiro
    ECOLOGICAL COMPLEXITY, 2018, 36 : 110 - 116
  • [30] A New Rank Estimator for Least Squares Estimation of Weibull Modulus
    Birgoren, Burak
    GAZI UNIVERSITY JOURNAL OF SCIENCE, 2025, 38 (01): : 167 - 179