References
- Aitken, A.G. (1964). Determinants and Matrices. Edinburgh: Oliver and Boyd.
- Chatterjee, S., Hadi, A.S. (1988). Sensitivity Analysis in Linear Regression. New York: John Wiley & Sons.
- Barrett, B.E., Gray, J.B. (1996). Computation of determinantal subset influence in regression. Statist. Comput. 6:131–138.
- Barrett, B.E., Gray, J.B. (1997). Leverage, residual and interaction diagnostics for subsets of cases in least squares regression. Computat. Statisti. Data Anal. 26:39–52.
- De Jongh, P.J., De Wet, T., Welsh, A.H. (1988). Mallows-type bounded-influence regression trimmed means. J. Amer. Statist. Assoc. 83:805–810.
- Graybill, F.A. (1983). Matrices with Applications in Statistics. Pacific Grove, CA: Wadsworth & Brooks/Cole.
- Gunst, R.F., Mason, R.L. (1980). Regression Analysis and Its Application: A Data Oriented Approach. New York: Marcel Dekker.
- Hadi, A.S., Simonoff, J.S. (1993). Procedures for the identification of multiple outliers in linear models. J. Amer. Statist. Assoc. 88:1264–1272.
- Harville, D.A. (1997). Matrix Algebra From a Statistician's Perspective. New York: Springer.
- Hawkins, D.M., Bradu, D., Kass, G.V. (1984). Location of several outliers in multiple regression data using elemental sets. Technometrics 26:197–208.
- Hawkins, D.M. (1993). The accuracy of elemental set approximations for regression. J. Amer. Statist. Assoc. 88:580–589.
- Hoaglin, D.C., Welsch, R.E. (1978). The hat matrix and Anova. Ameri. Statistician 32:17–22.
- Hocking, R.R., Pendleton, O.J. (1983). The regression dilemma. Commun. Statist. Part A-Theor. Meth. 12:497–527.
- Kempthorne, P.J., Mendel, M.B. (1990). Comment. J. Amer. Statist. Assoc. 85:647–648.
- Koenker, R., Bassett, G. (1978). Regression quantiles. Econometrica 46:33–50.
- Koenker, R. (2005). Quantile regression. Econometric Society Monographs. New York: Cambridge University Press.
- Mayo, M.S., Gray, J.B. (1997). Elemental subsets: the building blocks of regression. Amer. Statistician 51:122–129.
- Montgomery, D.C., Peck, E.A. (1982). Introduction to Linear Regression Analysis. New York: John Wiley & Sons.
- Rousseeuw, P.J. and van Zomeren, B.C. (1990). Unmasking multivariate outliers and leverage points (with discussion). J. Amer. Statist. Assoc. 85:633–651.
- Rousseeuw, P.J., Van Driessen, K. (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics 41:212–223.
- Schott, J.R. (2005). Matrix Analysis for Statistics.2nd ed. Hoboken, NJ: John Wiley & Sons.
- Searle, S.R. (1982). Matrix Algebra Useful for Statistics. New York: John Wiley & Sons.
- Seaver, B., Trantis, K., Reeves, C. (1999). The identification of influential subsets in regression using a fuzzy clustering strategy. Technometrics, Amer. Statist. Assoc. Amer. Soc. Qual. 41:340–351.
- Sheynin, O.B. (1973). R. J. Boscovich's work on probability. Arch. History Exact Sci. 9:306–324.
- Stigler, S.M. (1986). The History of Statistics: The Measurement of Uncertainty Before 1900. Cambridge, MA: Harvard University Press.
- Yang, X.M., Yang, X.Q., Teo, K.L. (2001). A matrix trace inequality. J. Mathemat. Anal. Applic. 263:327–331.
- Yu, K., Lu, Z., Stander, J. (2003). Quantile regression: applications and current research areas. Statistician, 52:331–350.