ANCOVA: A robust omnibus test based on selected design points

Journal of Modern Applied Statistical Methods

Volumn 5, Issue 1, 2006, Pages 13-20

  Wilcox, Rand R ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

ANCOVA; Bootstrap methods; Measures of depth; Smoothers

Indexed keywords

EID: 33748938033     PISSN: 15389472     EISSN: 15389472     Source Type: Journal    
DOI: 10.22237/jmasm/1146456120     Document Type: Article

References (19)

1. Atiqullah, M. (1964). The robustness of the covariance analysis of a one-way classification. Biometrika, 51, 365-372.
2. Bonett, D. G. & Price, R. M. (2002). Statistical inference for a linear function of medians: Confidence intervals, hypothesis testing, and sample size requirements. Psychological Methods, 7, 370-383.
3. Bowman, A. & Young, S. (1996). Graphical comparison of nonparametric curves. Applied Statistics, 45, 83-98.
4. Delgado, M. A. (1993). Testing the equality of nonparametric regression curves. Statistics and Probability Letters, 17, 199-204.
5. Dette, H. (1999). A consistent test for the functional form of a regression based on a difference of variance estimators. Annals of Statistics, 27, 1012-1040.
6. Dette, H. & Neumeyer, N. (2001). Nonparametric analysis of covariance. Annals of Statistics, 29, 1361-1400.
7. Donoho, D. L. & Gasko, M. (1992). Breakdown properties of the location estimates based on halfspace depth and projected outlyingness. Annals of Statistics, 20, 1803-1827.
8. Hall, P., Huber, C., & Speckman, P. L. (1997). Covariate-matched one-sided tests for the difference between functional means. Journal of the American Statistical Association, 92, 1074-1083.
9. Harrell, F. E. & Davis, C. E. (1982). A new distribution-free quantile estimator. Biometrika, 69, 635-640.
10. Hoaglin, D. C. (1985). Summarizing shape numerically: The g-and-h distribution. In D. Hoaglin, F. Mosteller & J. Tukey (Eds.) Exploring Data Tables Trends and Shapes. New York: Wiley.
11. Kulasekera, K. B. (1995). Comparison of regression curves using quasi-residuals. Journal of the American Statistical Association, 90, 1085-1093.
12. Kulasekera, K. B. & Wang, J. (1997). Smoothing parameter selection for power optimality in testing of regression curves. Journal of the American Statistical Association, 92, 500-511.
13. Liu, R. G. & Singh, K. (1997). Notions of limiting P values based on data depth and bootstrap. Journal of the American Statistical Association, 92, 266-277,
14. Munk, A. & Dette, H. (1998). Nonparametric comparison of several regression functions: Exact and asymptotic theory. Annals of Statistics, 26, 2339-2368.
15. Neumeyer, N. & Dette, H. (2003). Nonparametric comparison of regression curves: An empirical process approach. Annals of Statistics, 31, 880-920.
16. Robey, R. R. & Barcikowski, R. S. (1992). Type I error and the number of iterations in Monte Carlo studies of robustness. British Journal of Mathematical and Statistical Psychology, 45, 283-288.
17. Young, S. G. & Bowman, A. W. (1995). Nonparametric analysis of covariance. Biometrics, 57, 920-931.
18. Wilcox, R. R. (2003). Applying Contemporary Statistical Techniques Testing. San Diego CA: Academic Press.
19. Wilcox, R. R. (2005). Introduction to Robust Estimation and Hypothesis Testing (2nd Ed). San Diego CA: Academic Press.