The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied. Many examples are presented to clarify the use of the techniques and to demonstrate what conclusions can be made.
- Andersen, R. (2008). Modern Methods for Robust Regression. Thousand Oaks: Sage.Google Scholar
- Beaton, A. E. & Tukey, J. W. (1974). The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectoscopic Data. Technometrics, 16, 147–185.CrossRefGoogle Scholar
- Berk, R. A. (1990). A Primer on Robust Regression. In J. Fox & J. S. Long (Hg.), Modern Methods of Data Analysis(S. 292–324). Newbury Park: Sage.Google Scholar
- Buchinsky, M. (1998). The Dynamics of Changes in the Female Wage Distribution in the USA: A Quantile Regression Approach. Journal of Applied Econometrics, 13, 1–30.3.0.CO%3B2-A'>CrossRefGoogle Scholar
- Croux, C., Dhaene, G., & Hoorelbeke, D. (2003). Robust Standard Errors for Robust Estimators. Center for Economic Studies Discussions Paper Series (DPS) 03.16. Letzter Zugriff 30.05.2010: www.econ.kuleuven.be/eng/ew/discussionpapers/Dps03/Dps0316.pdf.
- Evans, M., Hastings, N., & Peacock, B. (2000). Statistical Distributions. New York: Wiley, 3. Auflage.Google Scholar
- Fox, J. (2008). Applied Regression Analysis and Generalized Linear Models. Thousand Oaks: Sage, 2. Auflage.Google Scholar
- Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust Statistics. The Approach Based on Influence Functions. New York: John Wiley & Sons.Google Scholar
- Hao, L. & Naiman, D. Q. (2007). Quantile Regression. Thousand Oaks: Sage.Google Scholar
- Heritier, S., Cantoni, E., Copt, S., & Victoria-Feser, M.-P. (2009). Robust Statistics in Biostatistics. West Sussey: Wiley.Google Scholar
- Huber, P. J. (1964). Robust Estimation of a Location Parameter. The Annals of Mathematical Statistics, 35, 73–101.CrossRefGoogle Scholar
- Huber, P. J. (1972). The 1972 Wald Lecture. Robust statistics: A Review. The Annals of Mathematical Statistics, 43, 1041–1067.Google Scholar
- Huber, P. J. (1973). Robust Regression: Asymptotics, Conjectures and Monte Carlo. The Annals of Mathematical Statistics, 1, 799–821.Google Scholar
- Huber, P. J. (1981). Robust Statistics. New York: Wiley.CrossRefGoogle Scholar
- Jann, B. (2006). Diagnostik von Regressionsschätzungen bei kleinen Stichproben. In A. Diekmann (Hg.), Methoden der Sozialforschung(S. 421–452). Wiesbaden: VS Verlag für Sozialwissenschaften.Google Scholar
- Jann, B. (2010). Robreg: Stata Module for Robust Regression Estimators. Statistical Software Components S457114, Boston College Department of Economics. Letzter Zugriff 29.03.2010: www.ideas.repec.org/c/boc/bocode/s457114.html.
- Jasso, G. (1985). Marital Coital Frequency and the Passage of Time: Estimating the Separate Effects of Spouses' Ages and Marital Duration, Birth and Marriage Cohorts, and Period Influences. American Sociological Review, 50, 224–241.CrossRefGoogle Scholar
- Kahn, J. R. & Udry, J. R. (1986). Marital Coital Frequency: Unnoticed Outliers and Unspecified Interactions Lead to Erroneous Conclusions. American Sociological Review, 51, 734–737.CrossRefGoogle Scholar
- Koenker, R. (2005). Quantile Regression. New York: Cambridge University Press.CrossRefGoogle Scholar
- Krasker, W. S. & Welsch, R. E. (1982). Efficient Bounded-Influence Regression Estimation. Journal of American Statistical Association, 77, 595–604.CrossRefGoogle Scholar
- Li, G. (1985). Robust Regression. In D. C. Hoaglin, F. Mosteller, & J. W. Tukey (Hg.), Exploring Data Tables, Trends, and Shapes(S. 281–343). New York: John Wiley & Sons.Google Scholar
- Marazzi, A., Joss, J., & Randriamiharisoa, A. (1993). Algorithms, Routines, and S Functions for Robust Statistics. Pacific Grove: Wadsworth & Brooks/Cole.Google Scholar
- Maronna, R. A., Martin, D. R., & Yohai, V. J. (2006). Robust Statistics. Theory and Methods. Chichester: Wiley.CrossRefGoogle Scholar
- Maronna, R. A. & Yohai, V. J. (2000). Robust Regression with Both Continuous and Categorical Predictors. Journal of Statistical Planning and Inference, 89, 197–214.CrossRefGoogle Scholar
- Ronchetti, E. M. (1985). Robust Model Selection in Regression. Statistics & Probability Letters, 3, 21–23.CrossRefGoogle Scholar
- Rousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79, 871–880.CrossRefGoogle Scholar
- Rousseeuw, P. J. & Hubert, M. (1997). Recent Developments in PROGRESS. In Y. Dodge (Hg.), L1-Statistical Procedures and Related Topics, Lecture Notes – Monograph Series, Band 31 (S. 201–214). Hayward: Institute of Mathematical Statistics.Google Scholar
- Rousseeuw, P. J. & Leroy, A. M. (1987). Robust Regression and Outlier Detection. New York: John Wiley & Sons.CrossRefGoogle Scholar
- Rousseeuw, P. J. & Van Driessen, K. (2002). Computing LTS Regression for Large Data Sets. Estadistica, 54, 163–190.Google Scholar
- Rousseeuw, P. J. & Yohai, V. (1984). Robust Regression by Means of S-Estimators. In J. Franke, W. Hardle, & D. Martin (Hg.), Robust and Nonlinear Time Series Analysis. Lecture Notes in Statistics, Band 26 (S. 256–272). Berlin: Springer.Google Scholar
- Ruppert, D. (1992). Computing S Estimators for Regression and Multivariate Location/ Dispersion. Journal of Computational and Graphical Statistics, 1, 253–270.CrossRefGoogle Scholar
- Salibian-Barrera, M. & Yohai, V. J. (2006). A Fast Algorithm for S-Regression Estimates. Journal of Computational and Graphical Statistics, 15, 414–427.CrossRefGoogle Scholar
- Staudte, R. G. & Sheather, S. J. (1990). Robust Estimation and Testing. New York: John Wiley & Sons.Google Scholar
- Stefanski, L. A. (1991). A Note on High-Breakdown Estimators. Statistics & Probability Letters, 11, 353–358.CrossRefGoogle Scholar
- Street, J. O., Carroll, R. J., & Ruppert, D. (1988). A Note on Computing Robust Regression Estimates via Iteratively Reweighted Least Squares. The American Statistician, 42, 152–154.CrossRefGoogle Scholar
- Verardi, V. & Croux, C. (2009). Robust Regression in Stata. The Stata Journal, 9, 439–453.Google Scholar
- Western, B. (1995). Concepts and Suggestions for Robust Regression Analysis. American Journal of Political Science, 39, 786–817.CrossRefGoogle Scholar
- White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48, 817–838.CrossRefGoogle Scholar
- Wilcox, R. R. (2005). Introduction to Robust Estimation and Hypothesis Testing. New York: Elsevier Academic Press, 2. Auflage.Google Scholar
- Wu, L. L. (1985). Robust M-Estimation of Location and Regression. Sociological Methodology, 15, 316–388.CrossRefGoogle Scholar
- Yohai, V. J. (1987). High Breakdown-Point and High Efficiency Robust Estimates for Regression. The Annals of Statistics, 152, 642–656.CrossRefGoogle Scholar
- Yohai, V. J., Stahel, W. A., & Zamar, R. H. (1991). A Procedure for Robust Estimation and Inference in Linear Regression. In W. A. Stahel & S. Weisberg (Hg.), Directions in Robust Statistics and Diagnostics, Band II (S. 365–374). New York: Springer.Google Scholar