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AR(1)模型下F检验的性质 被引量：1

Properties of F statistics with AR(1) model
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摘要 讨论误差服从一阶自回归的线性模型关于回归系数的线性假设检验问题.在方差参数已知的情况下,研究了检验统计量的性质;在方差参数未知时,采用前三阶中心矩相等的方法,提出两种近似检验方法.模拟结果显示,当误差正相关程度较高时,两种近似检验在具有较小的第一类错误的同时,具有较大的功效. The problem of testing linear hypothesis about regression coefficients of linear model with AR（1） errors is discussed. The properties of tests statistic are studied when the variance parameter is known. For unknown variance parameter case, two approximate tests statistics are proposed by“matching the first three moments”. The simulation results show that for high positive errors correlation cases,these two approximate tests statistics have lower type-1 error and larger power.
作者 樊亚莉 王林平
机构地区 上海理工大学理学院 上海师范大学数理信息学院
出处 《上海理工大学学报》 EI CAS 北大核心 2005年第4期295-299,共5页 Journal of University of Shanghai For Science and Technology
关键词 一阶自回归 功效函数 F统计量 autocrooelation power function F statistics
分类号 O212.1 [理学—概率论与数理统计]
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参考文献4

  • 1Wu C F J, Holt D, Homes D J. The effect of two-stage sampling on the F statistic[J]. Journal of the American Statistical Association, 1988,83:150～159.
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同被引文献16

  • 1Miller A. Subset selection in regression[M]. London: Chapman and Hall,2002.
  • 2FungWK, Zhu Z Y, Wei B C, et al. Influence diagnostics and outlier tests for semiparametric mixed models[J]. Journal of Royal Statistical Society, Ser B, 2002,64(2) : 565 - 579.
  • 3Fan J, Li R. Varible selection via nonconcave penalized likelihood and its oracle properties[J]. Journal of the American Statistical Association, 2001,96 (456) : 1348 - 1360.
  • 4Tibshirani R. Regression shrinkage and selection via the lasso [J]. Journal of Royal Statistical Society, 1996,58(1) :267- 288.
  • 5DonohoDL. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994,81(3) : 425 - 455.
  • 6Zou H. Regulariztion and variable selection via the elastic net[J]. Journal of Royal Statistical Society, Ser B,2005,67(2) :301 - 320.
  • 7Zou H. The adaptive lasso and its oracle properties [J]. Journal of American Statistical Association, 2006, 101(475) : 1418 - 1429.
  • 8Fan J. A selective overview of variable selection in high dimensional feature space[J]. Statistica Sinica, 2010,20(1) : 101 - 148.
  • 9Preisser J S. Robust regression for clustered data with application to binary responses[J]. Biometrics, 1999, 55(2) :574 - 579.
  • 10He X. Robust estimation in generalized partial linear models for clustered data [J]. Journal of American Statistical Association,2005,100(472) : 1176 - 1184.

引证文献1

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上海理工大学学报
2005年 第4期

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