[1]龙 兵.基于区间数据Lindley分布的参数估计(英文)[J].南京师范大学学报(自然科学版),2018,41(04):29.[doi:10.3969/j.issn.1001-4616.2018.04.006]
 Long Bing.Estimation of Parameter for Lindley DistributionBased on Interval Data[J].Journal of Nanjing Normal University(Natural Science Edition),2018,41(04):29.[doi:10.3969/j.issn.1001-4616.2018.04.006]
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基于区间数据Lindley分布的参数估计(英文)()
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《南京师范大学学报》(自然科学版)[ISSN:1001-4616/CN:32-1239/N]

卷:
第41卷
期数:
2018年04期
页码:
29
栏目:
·数学与计算机科学·
出版日期:
2018-12-31

文章信息/Info

Title:
Estimation of Parameter for Lindley DistributionBased on Interval Data
文章编号:
1001-4616(2018)04-0029-04
作者:
龙 兵
荆楚理工学院数理学院,湖北 荆门 448000
Author(s):
Long Bing
School of Mathematics and Physics,Jingchu University of Technology,Jingmen 448000,China
关键词:
Lindley分布区间数据EM算法极大似然法
Keywords:
Lindley distributioninterval dataEM algorithmmaximum likelihood method
分类号:
O212.1
DOI:
10.3969/j.issn.1001-4616.2018.04.006
文献标志码:
A
摘要:
首先在区间数据下用极大似然法求Lindley分布中未知参数的估计,然而并不能得到参数的显示表达式; 其次提出用EM算法可以很方便地求出参数估计且该估计具有良好的收敛性; 最后通过随机模拟来说明用EM算法求Lindley分布中未知参数的估计是切实可行的.
Abstract:
Firstly,the maximum likelihood method is used to estimate the unknown parameter in Lindley distribution under Interval data,however,the explicit expression of the parameter can not be obtained. Secondly,it is proposed that EM algorithm can be used to find out estimation of the parameter,and this method has good convergence. Finally,the simulation results show that it is feasible to use EM algorithm to estimate the unknown parameter in Lindley distribution.

参考文献/References:

[1] LINDLEY D. Introduction to probability and statistics fron a Bayesian viewpoint,part Ⅱ:inference[M]. New York:Cambridge University Press,1965.
[2]LINDLEY D. Fiducial distributions and Bayes’ theorem[J]. Journal of the royal statistical society,1958,20(1):102-107.
[3]GHITANY M E,ATIEH B,NADARAJAH S. Lindley distribution and its application[J]. Mathematics and computers in simulation,2008,78(4):493-506.
[4]GHITANY M E,ALMDK,NADARAJAH S. Zero-truncated Poisson-Lindley distribution and its application[J]. Mathematics and computers in simulation,2008,79(3):279-287.
[5]ZAMANI H,ISMAIL N. Negative binomial-Lindley distribution and its application[J]. Journal of mathematics and statistics,2010,6(1):4-9.
[6]HUANG W P,ZHOU J L. Parameter estimation of Lindley distribution with competing risk data[J]. Systems engineering and electronic,2016,38(2):464-469.
[7]JIE M,ARLENE N R. Inferences about the scale parameter of the gamma distribution based on data mixed from censoring and grouping[J]. Statistics & probability letters,2003,62(3):229-243.
[8]GANG L,ZHANG C H. Linear regression with interval censored data[J]. Ann statist,1998,26:1306-1327.
[9]GAETAN C,YAO J F. A multiple-imputation Metropolis version of the EM algorithm[J]. Biometrika,2003,90(3):643-654.
[10]JIAN H,ROSSINI A J. Sieve estimation for the proportional-odds failure-time regression model with interval censoring[J]. JASA,1997,92:960-967.
[11]RABINOWITZ D,TSIATIS A,ARAGON J. Regression with interval censored data[J]. Biometrika,1995,82:501-513.
[12]ZHENG M,ZHANG H,YANG Y. Estimating parameter in some distributions from grouped data[J]. Journal of Fudan university(natural science),2005,44(3):466-470.
[13]ZHENG M,XIANG Y. Estimating regression coefficients in linear models based on grouped data[J]. Mathematica applicata,2006,19(2):296-303.
[14]LAWLESS J F. Statistical models and methods for lifetime data[M]. New York:Wiley,2003.

备注/Memo

备注/Memo:
收稿日期:2017-03-02.
基金项目:National Natural Science Foundation of China(61374080).
通讯联系人:Long Bing,associate professor,major in applied statistics and reliability. E-mail:qh-longbing@163.com
更新日期/Last Update: 2018-12-30