[1]张涛,章溢.纵向数据测量误差模型的2次统计推断[J].江西师范大学学报(自然科学版),2015,(04):360-364.
 ZHANG Tao,ZHANG Yi.The Quadratic Inference Functions in Measurement Error Model for Longitudinal Data[J].,2015,(04):360-364.
点击复制

纵向数据测量误差模型的2次统计推断()
分享到:

《江西师范大学学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2015年04期
页码:
360-364
栏目:
出版日期:
2015-07-01

文章信息/Info

Title:
The Quadratic Inference Functions in Measurement Error Model for Longitudinal Data
作者:
张涛;章溢
中国社会科学院金融研究所,北京 100028; 兴业银行博士后工作站,福建 福州 350001;江西师范大学计算机信息工程学院,江西 南昌,330022
Author(s):
ZHANG Tao;ZHANG Yi
关键词:
纵向数据测量误差QIF方法
Keywords:
longitudinal datameasurement errorQIF method
分类号:
O212
文献标志码:
A
摘要:
考虑纵向数据的线性误差模型,其中协变量含有测量误差。使用2次函数推断方法得到回归参数的估计,证明所得到的估计渐近地服从正态分布;对参数的假设检验问题,证明所得统计量渐近地服从χ2分布,并通过数值模拟讨论方法的有限样本性质。最后,该方法被用于1组艾滋病数据的实证分析中。
Abstract:
A linear model for longitudinal data with continuous responses and error-prone covariates via quadratic in-ference functions methods is considered. Asymptotic normality of the parameter estimators is established by quadratic inference functions. In order to testing interested parameter,the statistic that proposed asymptotically follows a chi-squared distribution. The finite-sample properties of the procedures are studied through Monte Carlo simulations. At last,an application to a longitudinal study is used to illustrate the procedure developed here.

参考文献/References:

[1] Fuller W A.Measurement error models [M].New York:Wiley,1987.
[2] Carroll R J,Ruppert D,Stefanski L A.Measurement error in nonlinear models:A modern perspective [M].New York:Chapman and Hall,2006.
[3] Buonaccorsi J,Demidenko E,Tosteson T.Estimation in longitudinal random effects models with measurement error [J].Statistica Sinica,2000,10(2):885-903.
[4] Wang Naisyin,Lin Xihong,Gutierrez R G,et al.Bias analysis and SIMEX approach in generalized linear mixed measurement error models [J].Journal of the American Statistical Association,1998,93(4):249-261.
[5] Pan Wenqin,Lin Xihong,Zeng Donglin.Structural inference in transition measurement error models for longitudinal data [J].Biometrics,2006,62(6):402-412.
[6] Zhang Tao,Zhu Zhongyi.Efficient inference based on block empirical likelihood for longitudinal partially linear regression models [J].Chinese Journal of Applied Probability and Statistics,2010,26(3):323-335.
[7] Qu Annie,Lindsay B G,Li Bing.Improving generalized estimating equations using quadratic inference functions [J].Biometrika,2000,87(2):823-836.
[8] Lai Peng,Li Gaorong,Lian Hua.Quadratic inference functions for partially linear single-index models with longitudinal data [J].Journal of Multivariate Analysis,2013,118(8):115-127.
[9] Philip M W.A bias-corrected covariance estimator for improved inference when using an unstructured correlation with quadratic inference functions [J].Statistics & Probability Letters,2013,83(6):1553-1558.
[10] Tian Ruiqin,Xue Liugen,Liu Chunling.Penalized quadratic inference functions for semiparametric varying coefficient partially linear models with longitudinal data [J].Journal of Multivariate Analysis,2014,132(5):94-110.
[11] 陈广雷.变系数测量误差模型的B-样条估计 [J].应用数学,2014,27(1):45-51.
[12] 徐修友,黄彬.协变量含测量误差的变系数偏线性模型的变量选择问题研究 [J].北京化工大学学报:自然科学版,2013,40(6):325-333.
[13] Bai Yang,Zhu Zhongyi,Fung Wing.Partial linear models for longitudinal data based on quadratic inference functions [J].Scandinavian Journal of Statistics,2008,35(1):104-118.
[14] Liang Hua,Wang Suojin,Carroll R J.Partially linear models with missing response variables and error-prone covariates [J].Biometrika,2007,94(2):185-198.
[15] Qu Ainne,Li Runze.Quadratic inference functions for varying-coefficient models with longitudinal data [J].Biometrics,2006,62(7):379-391.

备注/Memo

备注/Memo:
国家自然科学基金(71361015);江西省自然科学基金(20142BAB201013);江西师范大学青年成长基金(004796)
更新日期/Last Update: 1900-01-01