MODIFIED CLASSICAL AND INVERSE REGRESSION-ESTIMATORS IN CALIBRATION
被引:0
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作者:
DAHIYA, RC
论文数: 0引用数: 0
h-index: 0
机构:OLD DOMINION UNIV, DEPT MATH & STAT, NORLFOK, VA 23529 USA
DAHIYA, RC
MCKEON, JJ
论文数: 0引用数: 0
h-index: 0
机构:OLD DOMINION UNIV, DEPT MATH & STAT, NORLFOK, VA 23529 USA
MCKEON, JJ
机构:
[1] OLD DOMINION UNIV, DEPT MATH & STAT, NORLFOK, VA 23529 USA
[2] UNIV HOUSTON CLEAR LAKE, DEPT MATH, HOUSTON, TX 77058 USA
来源:
SANKHYA-THE INDIAN JOURNAL OF STATISTICS SERIES B
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1991年
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53卷
关键词:
INVERSE REGRESSION;
ESTIMATION;
CALIBRATION;
D O I:
暂无
中图分类号:
O21 [概率论与数理统计];
C8 [统计学];
学科分类号:
020208 ;
070103 ;
0714 ;
摘要:
Classical estimator in linear calibration does not have finite mean for fixed sample size while the inverse regression estimator is not consistent. Here, we investigate two modified estimators which circumvent the above mentioned problems. The various estimators are compared through simulation for small sample size and the asymptotic results are given for large sample size. Asymptotic confidence interval is also considered.