Normal probability plots with confidence for the residuals in linear regression

被引:4
作者
Chantarangsi, W. [1 ]
Liu, W. [2 ,3 ]
Bretz, F. [4 ]
Kiatsupaibul, S. [5 ]
Hayter, A. J. [6 ]
机构
[1] Nakhon Pathom Rajabhat Univ, Fac Sci & Technol, 85 Moo 3,Malaiman Rd, Mueang Dist 73000, Nakhon Pathom, Thailand
[2] Univ Southampton, S3RI, Southampton, Hants, England
[3] Univ Southampton, Sch Math, Southampton, Hants, England
[4] Novartis Pharma AG, Basel, Switzerland
[5] Chulalongkorn Univ, Dept Stat, Bangkok, Thailand
[6] Univ Denver, Dept Business Informat & Analyt, Denver, CO USA
关键词
Graphical tests; Linear regression model; Normal distribution; Normal probability plot; Power; Residuals; Simultaneous inference;
D O I
10.1080/03610918.2016.1165840
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
Normal probability plots for a simple random sample and normal probability plots for residuals from linear regression are not treated differently in statistical text books. In the statistical literature, 1 - simultaneous probability intervals for augmenting a normal probability plot for a simple random sample are available. The first purpose of this article is to demonstrate that the tests associated with the 1 - simultaneous probability intervals for a simple random sample may have a size substantially different from when applied to the residuals from linear regression. This leads to the second purpose of this article: construction of four normal probability plot-based tests for residuals, which have size exactly. We then compare the powers of these four graphical tests and a non-graphical test for residuals in order to assess the power performances of the graphical tests and to identify the ones that have better power. Finally, an example is provided to illustrate the methods.
引用
收藏
页码:367 / 379
页数:13
相关论文
共 15 条
[1]   Normal probability plots with confidence [J].
Chantarangsi, Wanpen ;
Liu, Wei ;
Bretz, Frank ;
Kiatsupaibul, Seksan ;
Hayter, Anthony J. ;
Wan, Fang .
BIOMETRICAL JOURNAL, 2015, 57 (01) :52-63
[2]  
Crawley M.J., 2013, R BOOK
[3]   THE EFFICIENCY OF SIMULATION-BASED MULTIPLE COMPARISONS [J].
EDWARDS, D ;
BERRY, JJ .
BIOMETRICS, 1987, 43 (04) :913-928
[4]  
EPPS TW, 1983, BIOMETRIKA, V70, P723, DOI 10.1093/biomet/70.3.723
[5]  
Faraway J.J., 2015, LINEAR MODELS R
[6]  
Hazen A., 1914, T AM SOC CIV ENG, V77, P1547, DOI DOI 10.1155/2014/326579
[7]   Omnibus tests for the error distribution in the linear regression model [J].
Huskova, Marie ;
Meintanis, Simos G. .
STATISTICS, 2007, 41 (05) :363-376
[8]  
Kolmogorov A.N., 1933, Giorn Del l'inst Ital Degli Att, V4, P83
[10]   Simulation-based simultaneous confidence bands in multiple linear regression with predictor variables constrained in intervals [J].
Liu, W ;
Jamshidian, M ;
Zhang, Y ;
Donnelly, J .
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2005, 14 (02) :459-484