Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s)

434 ReferencesHasselmann, K. (1988). PIPs and POPs: The reduction of complex dynamicalsystems using principal interaction and oscillation patterns. J.Geophys. Res., 93, 11,015–11,021.Hastie, T. and Stuetzle, W. (1989). Principal curves. J. Amer. Statist.Assoc., 84, 502–516.Hastie, T., Tibshirani, R., Eisen, M.B., Alizadeh, A., Levy, R., Staudt,L., Chan, W.C., Botstein, D. and Brown, P. (2000). ‘Gene shaving’ asa method for identifying distinct sets of genes with similar expressionpatterns. Genome Biol., 1, research 0003.1–003.21.Hausmann, R. (1982). Constrained multivariate analysis. In Optimisationin Statistics, eds. S.H. Zanckis and J.S. Rustagi, 137–151. Amsterdam:North-Holland.Hawkins, D.M. (1973). On the investigation of alternative regressions byprincipal component analysis. Appl. Statist., 22, 275–286.Hawkins, D.M. (1974). The detection of errors in multivariate data usingprincipal components. J. Amer. Statist. Assoc., 69, 340–344.Hawkins, D.M. (1980). Identification of Outliers. London: Chapman andHall.Hawkins, D.M. and Eplett, W.J.R. (1982). The Cholesky factorizationof the inverse correlation or covariance matrix in multiple regression.Technometrics, 24, 191–198.Hawkins, D.M. and Fatti, L.P. (1984). Exploring multivariate data usingthe minor principal components. Statistician, 33, 325–338.Helland, I.S. (1988). On the structure of partial least squares regression.Commun. Statist.—Simul., 17, 581–607.Helland, I.S. (1990). Partial least squares regression and statistical models.Scand. J. Statist., 17, 97–114.Helland, I.S. and Almøy, T. (1994). Comparison of prediction methodswhen only a few components are relevant. J. Amer. Statist. Assoc., 89,583–591 (correction 90, 399).Heo, M. and Gabriel, K.R. (2001). The fit of graphical displays to patternsof expectations. Computat. Statist. Data Anal., 36, 47-67.Hill, R.C., Fomby, T.B. and Johnson, S.R. (1977). Component selectionnorms for principal components regression. Commun. Statist., A6, 309–334.Hills, M. (1982). Allometry. In Encyclopedia of Statistical Sciences Vol 1,eds. S. Kotz and N.L. Johnson, 48–54. New York: Wiley.Hoaglin, D.C., Mosteller, F. and Tukey, J.W. (1983). Understanding Robustand Exploratory Data Analysis. New York: Wiley.Hocking, R.R. (1976). The analysis and selection of variables in linearregression. Biometrics, 32, 1–49.Hocking, R.R. (1984). Discussion of ‘K-clustering as a detection toolfor influential subsets in regression’ by J.B. Gray and R.F. Ling.Technometrics, 26, 321–323.