Least squares and minimum distance estimation in the three-parameter Weibull and Frechet models with applications to river drain data
被引:0
作者:
Offinger, R
论文数: 0引用数: 0
h-index: 0
机构:
Otto Von Guericke Univ, Fak Math, Inst Math Stochast, D-39016 Magdeburg, GermanyOtto Von Guericke Univ, Fak Math, Inst Math Stochast, D-39016 Magdeburg, Germany
Offinger, R
[1
]
机构:
[1] Otto Von Guericke Univ, Fak Math, Inst Math Stochast, D-39016 Magdeburg, Germany
来源:
ADVANCES IN STOCHASTIC MODELS FOR RELIABILITY, QUALITY AND SAFETY
|
1998年
关键词:
point estimation;
censored samples;
Gumbel distribution;
maximum likelihood method;
univariate optimization by hybrid method;
simulation;
bias;
mean square error;
EDF tests;
Cramer-von Mises statistic;
fitting empirical data;
skewness;
kernel estimation;
D O I:
暂无
中图分类号:
T [工业技术];
学科分类号:
08 ;
摘要:
After introducing three-Parameter Weibull and Frechet models we de:fine various least squares and minimum distance estimation methods in general. We then show how these methods can be applied to the Weibull and Frechet models and examine the quality of two special estimators in a small simulation study. These studies show that the estimators are a good alternative to already known estimators. Finally we discuss the application of the models to river drain data and some involved problems.