CONFIDENCE DENSITY METHOD FOR PARAMETER ESTIMATION

LI TI-PEI

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2024年10月30日

Chinese Physics C> 1978, Vol. 2> Issue(2) : 131-142

CONFIDENCE DENSITY METHOD FOR PARAMETER ESTIMATION

LI TI-PEI

Institute of High Energy Physics, Academia Sinica

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Abstract：
Based on an analysis of the Bayesian posteriori density method and the Non-Bayesian confidence interval method for parameter estimation, the concept and a computation formula for the confidence density are introduced from the confidence interval. It is shown that the confidence density distribution is a complete description of the result of the Non-Bayesian parameter estimation and possesses a definite probability meaning. As an example to show its application, a statistical inference to the mass of a charged particle is made.

References

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李惕暗，《实验的数学处理》，科学出版社(将出版).[2] M.费史，《概率论及数理统计》，王福保译，上海科学技术出版社(1962).[3] A. Mood and F, Graybill,《Introduction to the Theory of Statistics》, McGraw-Hill BookCompany,Inc., NewYork (1963). [4]中国科学院原子能研究所云南站，物理，1(1972)，57. [5]李惕陪，16580事例c粒子质量的统计推断，(1972)，未发表. [6]李惕陪，游离测量的数学拟合，(1973)，未发表. [7]中国科学院高能物理研究所三室，μ介子在氦氩混合气体中的游离能损曲线，(1974)，未发表.

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LI TI-PEI. CONFIDENCE DENSITY METHOD FOR PARAMETER ESTIMATION[J]. Chinese Physics C, 1978, 2(2): 131-142.

LI TI-PEI. CONFIDENCE DENSITY METHOD FOR PARAMETER ESTIMATION[J]. Chinese Physics C, 1978, 2(2): 131-142. shu

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Received: 1977-07-18
Revised: 1900-01-01

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沈阳化工大学材料科学与工程学院沈阳 110142

CONFIDENCE DENSITY METHOD FOR PARAMETER ESTIMATION

LI TI-PEI

Institute of High Energy Physics, Academia Sinica

Received Date: 1977-07-18
Accepted Date: 1900-01-01
Available Online: 1978-04-05

Abstract: Based on an analysis of the Bayesian posteriori density method and the Non-Bayesian confidence interval method for parameter estimation, the concept and a computation formula for the confidence density are introduced from the confidence interval. It is shown that the confidence density distribution is a complete description of the result of the Non-Bayesian parameter estimation and possesses a definite probability meaning. As an example to show its application, a statistical inference to the mass of a charged particle is made.