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Free Access References G. A. F. Seber, G. A. F. Seber University of AucklandSearch for more papers by this author Book Author(s):G. A. F. Seber, G. A. F. Seber University of AucklandSearch for more papers by this author First published: 30 April 1984 https://doi.org/10.1002/9780470316641.refsBook Series:Wiley Series in Probability and Statistics AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat References Abe, O. (1973). A note on the methodology of Knox's tests of “Time and space interaction.” Biometrics, 29, 67– 77. CrossrefCASPubMedWeb of Science®Google Scholar Agresti, A. (1981). Measures of nominal-ordinal association. J. Am. Slat. Assoc., 76, 524– 529. Web of Science®Google Scholar Ahmed, S. W., and Lachenbruch, P. A. (1975). Discriminant analysis when one or both of the initial samples is contaminated: large sample results EDV in Medizin und Biologie, 6, 35– 42. Google Scholar Ahmed, S. W., and Lachenbruch, P. A. (1977). Discriminant analysis when scale contamination is present in the initial sample. In Van Ryziu J. (Ed.), Classification and Clustering, pp. 331– 353. Academic Press: New York. Web of Science®Google Scholar Aitchison, J., and Aitken, C. G. G. (1976). Multivariate binary discrimination by the kernel method. Biometrika, 63, 413– 420. CrossrefWeb of Science®Google Scholar Aitchison, J., and Begg, C. B. (1976). Statistical diagnosis when basic cases are not classified with certainty, Biometrika, 63, 1– 12. CrossrefWeb of Science®Google Scholar Aitchison, J., and Shen, S. M. (1980). Logistic-normal distributions: some properties and uses. Biometrika, 67, 261– 272. CrossrefWeb of Science®Google Scholar Aitchison, J., Habbema, J. D. F., and Kay, J. W. (1977). A critical comparison of two methods of statistical discrimination. Appl. Stat., 26, 15– 25. Wiley Online LibraryGoogle Scholar Aitken, C. G. G. (1978). Methods of discrimination in multivariate binary data. In C. A. Corsten L. and Hermans J. (Eds.), Compstat 1978, pp. 155– 161. Physica-Verlag: Vienna. Google Scholar Aitkin, M. A. (1969). Some tests for correlation matrices. Biometrika, 56, 443– 446. CrossrefWeb of Science®Google Scholar Aitkin, M A. (1971). Correction to “Some tests for correlation matrices.” Biometrika, 58, 245. Web of Science®Google Scholar Aitkin, M. A. (1974). Simultaneous inference and the choice of variable subsets. Technometrics, 16, 221– 227. Web of Science®Google Scholar Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In N. Petrov B. and Czáki F. (Eds.), Second International Symposium on Information Theory, pp. 267– 281. Akademiai Kiadó: Budapest. Google Scholar Alalouf, I. S., and Styan, G. P. H. (1979). Characterizations of estimability in the general linear model. Ann. Stat., 7, 194– 200. CrossrefWeb of Science®Google Scholar Alam, K. (1975). Minimax and admissible minimax estimators of the mean of a multivariate normal distribution for unknown covariance matrix. J. Multivar. Anal., 5, 83– 95. CrossrefGoogle Scholar Albert, A. (1978). Quelques apports nouveaux à l'analyse discriminante. Ph.D. thesis, Faculté des Sciences, Université de Liège, Liège, France. Google Scholar Albert, A., and Anderson, J. A. (1981). Probit and logistic discriminant functions. Commun. Stat. Theor. Methods A, 10, 641– 657. CrossrefWeb of Science®Google Scholar Anderberg, M. R. (1973). Cluster Analysis for Applications. Academic Press: New York. CrossrefWeb of Science®Google Scholar Andersen, E. B. (1982). Latent structure analysis. Scand. J. Stat., 9, 1– 12. CrossrefWeb of Science®Google Scholar Anderson, A. H. (1974). Multidimensional contingency tables. Scand. J. Stat. 1, 115– 127. Google Scholar Anderson, E. (1957). A semi-graphical method for the analysis of complex problems. Proc. Nat. Acad. Sci. U.S.A., 43, 923– 927 CrossrefCASPubMedWeb of Science®Google Scholar [Reprinted, with an appended note, in Technometrics (1960), 2, 387– 391.] CrossrefWeb of Science®Google Scholar Anderson, J. A. (1969). Discrimination between k populations with constraints on the probabilities of misclassification. J. R. Stat. Soc. B, 31, 123– 139. Wiley Online LibraryWeb of Science®Google Scholar Anderson, J. A. (1972). Separate sample logistic discrimination. Biometrika, 59, 19– 35. CrossrefWeb of Science®Google Scholar Anderson, J. A. (1974). Diagnosis by logistic discriminant function: further practical problems and results. Appl. Stat., 23, 397– 404. Wiley Online LibraryWeb of Science®Google Scholar Anderson, J. A. (1975). Quadratic logistic discrimination. Biometrika, 62, 149– 154. CrossrefWeb of Science®Google Scholar Anderson, J. A. (1979). Multivariate logistic compounds. Biometrika, 66, 17– 26. CrossrefWeb of Science®Google Scholar Anderson, J. A. (1982). Logistic discrimination. In R. Krishnaiah P. and Kanal L. (Eds.), Handbook of Statistics. Vol. II. Classification, Pattern Recognition, and Reduction of Dimension, North-Holland: Amsterdam. Google Scholar Anderson, J. A., and Blair, V. (1982). Penalized maximum likelihood estimation in logistic regression and discrimination. Biometrika, 69, 123– 136. CrossrefWeb of Science®Google Scholar Anderson, J. A., and Richardson, S. C. (1979). Logistic discrimination and bias correction in maximum likelihood estimation. Technometrics, 21, 71– 78. CrossrefWeb of Science®Google Scholar Anderson, T. W. (1951). Classification by multivariate analysis. Psychometrika, 16, 31– 50. CrossrefGoogle Scholar Anderson, T. W. (1958). Introduction to Multivariate Statistical Analysis. Wiley: New York. Google Scholar Anderson, T. W. (1963a). A test for equality of means when covariance matrices are unequal. Ann. Math. Stat., 34, 671– 672. CrossrefWeb of Science®Google Scholar Anderson, T. W. (1963b). Asymptotic theory for principal component analysis. Ann. Stat. Math., 34, 122– 148. CrossrefWeb of Science®Google Scholar Anderson, T. W. (1965). Some optimum confidence bounds for roots of determinantal equations. Ann. Stat., 36, 468– 488. CrossrefWeb of Science®Google Scholar Anderson, T. W. (1966). Some nonparametric multivariate procedures based on statistically equivalent blocks. In R. Krishnaiah P. (Ed.), Multivariate Analysis, pp. 5– 27. Academic Press: New York. Web of Science®Google Scholar Anderson, T. W. (1969). Statistical inference for covariance matrices with linear structure. In R. Krishnaiah P. (Ed.), Multivariate Analysis, Vol. II, pp. 55– 66. Academic Press: New York. Google Scholar Anderson, T. W. (1970). Estimation of covariance matrices which are linear combinations or whose inverses are linear combinations of given matrices. In C. Bose R. et al. (Eds.), Essays in Probability and Statistics, pp. 1– 24. University of North Carolina Press: Chapel Hill, N.C. Google Scholar Anderson, T. W. (1973). Asymptotically efficient estimation of covariance matrices with linear structure. Ann. Stat., 1, 135– 141. CrossrefWeb of Science®Google Scholar Anderson, T. W., and Das Gupta, S., (1963). Some inequalities on characteristic roots of matrices. Biometrika, 50, 522– 524. CrossrefWeb of Science®Google Scholar Andrews, D. F. (1971). A note on the selection of data transformations, Biometrika, 58, 249– 254. CrossrefWeb of Science®Google Scholar Andrews, D. F. (1972). Plots of high-dimensional data. Biometrics, 28, 125– 136. CrossrefWeb of Science®Google Scholar Andrews, D. F. (1973). Graphical techniques for high dimensional data. In Cacoullos T. (Ed.), Discriminant Analysis and Applications, pp. 37– 59. Academic Press: New York. CrossrefGoogle Scholar Andrews, D. F., Gnanadesikan, R., and Warner, J. L. (1971). Transformations of multivariate data. Biometrics, 27, 825– 840. CrossrefWeb of Science®Google Scholar Andrews, D. F., Bickel, P. J., Hampel, F. R., Huber, P. J., Rogers, W. H., and Tukey, J. W. (1972a). Robust Estimates of Location—Survey and Advances. Princeton University Press: Princeton, N.J. Google Scholar Andrews, D. F., Gnanadesikan, R., and Warner, J. L. (1972b). Methods for assessing multivariate normality. Bell Laboratories Memorandum. Murray Hill: N.J. Google Scholar Andrews, D. F., Gnanadesikan, R., and Warner, J. L. (1973). Methods for assessing multivariate normality. In R. Krishnaiah P. (Ed.), Multivariate Analysis, Vol. III, pp. 95– 116. Academic Press: New York. Google Scholar Arnold, H. J. (1964). Permutation support for multivariate techniques. Biometrika, 51, 65– 70. CrossrefPubMedWeb of Science®Google Scholar Arnold, S. F. (1979). A coordinate-free approach to finding optimal procedures for repeated measures designs. Ann. Stat., 7, 812– 822. CrossrefWeb of Science®Google Scholar Arnold, S. F. (1981). The Theory of Linear Models and Multivariate Analysis. Wiley: New York. Google Scholar Ashikaga, T., and Chang, P. C. (1981). Robustness of Fisher's linear discriminant function under two-component mixed normal models J. Am. Stat. Assoc., 76, 676– 680. CrossrefWeb of Science®Google Scholar Astrahan, M. M. (1970). Speech Analysis by Clustering, or the Hyperphoneme Method. Stanford Artificial Intelligence Project Memorandum AIM-124, AD 709067. Stanford University, Palo Alto, Calif. Google Scholar Atkinson, A. C. (1973). Testing transformations to normality. J. R. Stat. Soc. B, 35, 473– 479. Wiley Online LibraryWeb of Science®Google Scholar Bahadur, R. R. (1961). A representation of the joint distribution of responses to n dichotomous items. In Solomon H. (Ed.), Studies in Item Analysis and Prediction, pp. 158– 168. Stanford University Press: Palo Alto, Calif. Google Scholar Bailey, B. J. R. (1977). Tables of the Bonferroni t statistic. J. Am. Stat. Assoc., 72, 469– 478. CrossrefWeb of Science®Google Scholar Baker, F. B. (1974). Stability of two hierarchical grouping techniques. Case I: sensitivity to data errors. J. Am. Stat. Assoc., 69, 440– 445. CrossrefWeb of Science®Google Scholar Baker, F. B., and Hubert, L. J. (1975). Measuring the power of hierarchical cluster analysis. J. Am. Stat. Assoc., 70, 31– 38. CrossrefWeb of Science®Google Scholar Baker, R. J., and Nelder, J. A. (1978) The CUM System, Release 3. Numerical Algorithms Group, Oxford. Google Scholar Baksalary, J. K., Corsten, L. C. A., and Kala, R. (1978). Reconciliation of two different views on estimation of growth curve parameters. Biometrika, 65, 662– 665. CrossrefWeb of Science®Google Scholar Balasooriya, U., and Chan, L. K. (1981). Robust estimation of Stigler's data sets by cross validation. Appl. Stat., 30, 170– 177. Wiley Online LibraryGoogle Scholar Ball, G. H., and Hall, D. J. (1965). ISODATA, a Novel Method of Data Analysis and Pattern Classification. Technical Report, Stanford Research Institute: Menlo Park, Calif. Google Scholar Ball, G. H., and Hall, D.J. (1967). A clustering technique for summarizing multivariate data. Behav. Sci., 12, 153– 155. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Banfield, C. F., and Gower, J. C. (1980). A note on the graphical representation of multivariate binary data. Appl. Stat., 29, 238– 245. Wiley Online LibraryGoogle Scholar Barnett, V. (1975). Probability plotting methods and order statistics. Appl. Stat., 24, 95– 108. Wiley Online LibraryWeb of Science®Google Scholar Barnett, V. (1976a). The ordering of multivariate data (with discussion). J. R. Slat. Soc. A, 139, 318– 354. Wiley Online LibraryWeb of Science®Google Scholar Barnett, V. (1976b). Convenient probability plotting positions for the normal distribution. Appl. Stat., 25, 47– 50. Wiley Online LibraryWeb of Science®Google Scholar V. Barnett, (Ed.)., (1981). Interpreting Multivariate Data. Wiley: Chichester. Google Scholar Barnett, V., and Lewis, T. (1978). Outliers in Statistical Data. Wiley: Chicester, England. Google Scholar Bartholomew, D. J. (1980). Factor analysis for categorical data. J. R. Stat. Soc. B. 42, 293– 321. Wiley Online LibraryWeb of Science®Google Scholar Bartholomew, D. J. (1981). Posterior analysis of the factor model. Br. J. Math. Stat. Psychol., 34, 93– 99. Wiley Online LibraryWeb of Science®Google Scholar Bartlett, M S. (1938a). Further aspects of the theory of multiple regression. Proc. Cambridge Philos. Soc., 34, 33– 40. CrossrefWeb of Science®Google Scholar Bartlett, M. S. (1938b). Methods of estimating mental factors. Br. J. Psychol., 28, 97– 104. Google Scholar Bartlett, M. S. (1947). Multivariate analysis. J. R. Stat. Soc. Suppl., 9, 176– 190. Wiley Online LibraryGoogle Scholar Bartlett, M. S. (1951). The effect of standardisation on an approximation in factor analysis. Biometrika, 38, 337– 344. Web of Science®Google Scholar Bartlett, M. S., and Please, N. W., (1963). Discrimination in the case of zero mean differences. Biometrika, 50, 17– 21. CrossrefWeb of Science®Google Scholar Beale, E. M. L. (1969a). Cluster Analysis. Scientific Control Systems: London. Google Scholar Beale, E. M. L. (1969b). Euclidean duster analysis. Bull. Int. Stat. Inst., 43, 92– 94. Google Scholar Beale, E. M. L., and Little, R. J. A. (1975). Missing values in multivariate analysis. J. R. Stat. Soc. B, 37, 129– 145. Wiley Online LibraryWeb of Science®Google Scholar Beauchamp, J. J., Folkert, J. E., and Robson, D. S. (1980). A note on the effect of logarithmic transformation on the probability of misclassfication. Commun. Stat. Theor. Methods A, 9, 777– 794. CrossrefWeb of Science®Google Scholar Bebbington, A. C. (1978). A method of bivariate trimming for robust estimation of the correlation coefficient. Appl. Stat., 27, 221– 226. Wiley Online LibraryGoogle Scholar Beckman, R. J., and Johnson, M. E. (1981). A ranking procedure for partial discriminant analysis. J. Am. Stat. Assoc., 76, 671– 675. CrossrefWeb of Science®Google Scholar Bellmann, R. (1960). Introduction to Matrix Analysis. McGraw-Hill: New York. Google Scholar Bendel, R. B. and Mickey, M. R. (1978). Population correlation matrices for sampling experiments. Commun. Stat. Simul. Comput. B, 7, 163– 182. CrossrefWeb of Science®Google Scholar Bennett, B. M. (1951). Note on a solution of the generalized Behrens-Fisher problem. Ann. Inst. Stat. Math., 2, 87– 90. CrossrefGoogle Scholar Berger, J. O. (1976a). Admissible minimax estimation of a multivariate normal mean with arbitrary quadratic loss. Ann. Stat., 4, 223– 226. CrossrefWeb of Science®Google Scholar Berger, J. O. (1976b). Minimax estimation of a multivariate normal mean under arbitrary quadratic loss. J. Multivar. Anal., 6, 256– 264. CrossrefWeb of Science®Google Scholar Berger, J. O. (1978). Minimax estimation of a multivariate normal mean under polynomial loss. J. Multivar. Anal., 8, 173– 180. CrossrefWeb of Science®Google Scholar Berger, J. O. (1980a). A robust generalized Bayes estimator and confidence region for a multivariate normal mean. Ann. Stat., 8, 716– 761. CrossrefWeb of Science®Google Scholar Berger, J. O. (1980b). Improving on inadmissable estimators in continuous exponential families with applications to simultaneous estimation of gamma scale parameters. Ann. Stat., 8, 545– 571. CrossrefWeb of Science®Google Scholar Berger, J. O. (1982). Selecting a minimax estimator of a multivariate normal mean. Ann. Stat., 10, 81– 92. CrossrefWeb of Science®Google Scholar Berger, J. O., and Bock, M. E. (1976a). Eliminating singularities of Stein-type estimators of location vectors. J. R. Stat. Soc. B, 38, 166– 170. Wiley Online LibraryWeb of Science®Google Scholar Berger, J. O., and Bock, M. E. (1976b). Combining independent normal mean estimation problems with unknown variances. Ann. Stat., 4, 642– 648. CrossrefWeb of Science®Google Scholar Berk, K. N. (1978). Comparing subset regression procedures. Technometrics, 20, 1– 6. CrossrefWeb of Science®Google Scholar Bertin, T. (1967). Semiologie Graphique. Gauthier-Villars: Paris. Google Scholar Bhapkar, V. P., and Patterson, K. W. (1977). On some nonparametric tests for profile analysis of several multivariate samples. J. Multivar. Anal., 7, 265– 277. CrossrefWeb of Science®Google Scholar Bhapkar, V. P., and Patterson, K. W. (1978). A Monte Carlo study of some multivariate nonparametric statistics for profile analysis of several samples. J. Stat. Comput. Simul., 6, 223– 237. CrossrefGoogle Scholar Bhargava, R. P. (1962). Multivariate Tests of Hypotheses with Incomplete Data. Technical Rep. No. 3. Appl. Math, and Labs., Stanford University, Palo Alto, Calif. Google Scholar Bhoj, D. S. (1973a). Percentage points of the statistics for testing hypotheses on mean vectors of multivariate normal distributions with missing observations. J. Stat. Comput. Simul., 2, 211– 224. CrossrefGoogle Scholar Bhoj, D. S. (1973b). On the distribution of a statistic used for testing a multinomial mean vector with incomplete data. J. Stat. Comput. Simul., 2, 309– 316. CrossrefGoogle Scholar Bhoj, D. S. (1978). Testing equality of means of correlated variates with missing observations on both responses. Biometrika, 65, 225– 228. CrossrefWeb of Science®Google Scholar Bhoj, D. S. (1979). Testing equality of variances of correlated vanates with incomplete data on both responses. Biometrika, 66, 681– 683. CrossrefWeb of Science®Google Scholar Bickel, P. J. (1964). On some alternative estimates for shift in the p-variate one-sample problem. Ann. Math. Stat., 35, 1079– 1090. CrossrefWeb of Science®Google Scholar Bickel, P. J. (1976). Another look at robustness: a review of reviews and some new developments. Scand J. Stat., 3, 145– 168. Google Scholar Bickel, P. J., and Doksum, K. A. (1981). An analysis of transformations revisited. J. Am. Stat. Assoc., 76, 296– 311. CrossrefWeb of Science®Google Scholar Binder, D. A. (1978). Bayesian cluster analysis. Biometrika, 65, 31– 38. CrossrefWeb of Science®Google Scholar Binder, D. A. (1981). Approximations to Bayesian cluster analysis. Biometrika, 68, 275– 285. CrossrefWeb of Science®Google Scholar Bishop, Y., Fienberg, S., and Holland, P. (1975). Discrete Multivariate Analysis. MIT Press: Cambridge, Mass. Google Scholar Blackith, R. E., and Reyment, R. A. (1971). Multivariate Morphometries. Academic Press: London. Google Scholar Blashfield, R. K. (1976). Mixture model tests of cluster analysis: accuracy of four agglomerative hierarchical methods. Psychol. Bull., 83, 377– 388. CrossrefWeb of Science®Google Scholar Block, H. W. (1977). Multivariate reliability classes. In R. Krishnaiah P., (Ed.), Applications of Statistics, pp. 79– 88. North-Holland: Amsterdam. Google Scholar Bonner, R. E. (1964). On some clustering techniques IBM J., 22, 22– 32. CrossrefGoogle Scholar Boos, D. D. (1980). A new method for constructing approximate confidence intervals from M estimates. J. Am. Stat. Assoc., 75, 142– 145. Web of Science®Google Scholar Boulton, D. M., and Wallace, C. S. (1970). A program for numerical classification. Comput. J., 13, 63– 69. CrossrefWeb of Science®Google Scholar Bowden, D. C., and Steinhorst, R. K. (1973). Tolerance bands for growth curves. Biometrics, 29, 361– 371. CrossrefWeb of Science®Google Scholar Bowman, K. O., and Shenton, L. R. (1973a). Notes on the distribution of √b1 in sampling from Pearson distributions. Biometrika, 60, 155– 167. Web of Science®Google Scholar Bowman, K. O., and Shenton, L. R. (1973b). Remarks on the distribution of √b1 in sampling from a normal mixture and normal Type A distribution. J. Am. Stat. Assoc., 68, 998– 1003. CrossrefWeb of Science®Google Scholar Bowman, K. O., and Shenton, L. R. (1975). Omnibus test contours for departures from normality based on √ and b2. Biometrika, 62, 243– 249. CrossrefWeb of Science®Google Scholar Box, G. E. P. (1949). A general distribution theory for a class of likelihood criteria. Biometrika, 36, 317– 346. CrossrefCASPubMedWeb of Science®Google Scholar Box, G. E. P. (1953). Non-normality and tests on variances. Biometrika, 40, 318– 335. CrossrefWeb of Science®Google Scholar Box, G. E P. (1979). Robustness in the strategy of scientific model building. In L. Launer R. and N. Wilkinson G. (Eds.), Robustness in Statistics, pp. 201– 236. Academic Press: New York. CrossrefGoogle Scholar Box, G. E. P., and Cox, D. R. (1964). An analysis of transformations. J. R. Stat. Soc. B, 26, 211– 252. Wiley Online LibraryPubMedGoogle Scholar Box, G. E. P., and Cox, D. R. (1982). An analysis of transformations revisited, rebutted. J. Am. Stat. Assoc., 77, 209– 210. CrossrefWeb of Science®Google Scholar Box, G. E. P., and Watson, G. S. (1962). Robustness to non-normality of regression tests. Biometrika, 49, 93– 106. CrossrefWeb of Science®Google Scholar Box, G. E. P., Hunter, W. G., MacGregor, J. F., and Erjavec, J. (1973). Some problems associated with the analysis of multiresponse data. Technometrics, 15, 33– 51. CrossrefWeb of Science®Google Scholar Bradu, D., and Gabriel, K. R. (1978). The biplot as a diagnostic tool for models of two-way tables. Technometrics, 20, 47– 68. CrossrefWeb of Science®Google Scholar Brandwein, A. R. C., and Strawderman, W. E. (1980). Minimax estimation of location parameters for spherically symmetric distributions with concave loss. Ann. Stat., 8, 279– 284. CrossrefWeb of Science®Google Scholar Bray, J. R., and Curtis, J. T. (1957). An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr., 27, 325– 349. Wiley Online LibraryWeb of Science®Google Scholar Breiman, L., Meisel, W., and Purcell, E. (1977). Variable kernel estimates of multivariate densities. Technometrics, 19, 135– 144. CrossrefWeb of Science®Google Scholar Brillinger, D. R. (1975). Time Series: Data Analysis and Theory. Holt, Rinehart, and Winston: New York. Web of Science®Google Scholar Brillouin, L. (1962). Science and Information Theory, 2nd ed. Academic Press: New York. CrossrefWeb of Science®Google Scholar Broffitt, B., Clarke, W. R., and Lachenbruch, P. A. (1980). The effect of Huberizing and trimming on the quadratic discriminant function. Commun. Stat. Theor. Methods A, 9, 13– 25. CrossrefWeb of Science®Google Scholar Broffitt, B., Clarke, W. R., and Lachenbruch, P. A. (1981). Measurement errors—a location contamination problem in discriminant analysis. Commun. Stat. Simul. Comput. B, 10, 129– 141. CrossrefWeb of Science®Google Scholar Broffitt, J. D., Randies, R. H., and Hogg, R. V. (1976). Distribution-free partial discriminant analysis. J. Am. Stat. Assoc., 71, 934– 939. Web of Science®Google Scholar Brown, L. (1966). On the admissibility of estimators of one or more location parameters. Ann. Math. Stat., 37, 1087– 1136. CrossrefWeb of Science®Google Scholar Brown, P. J., and Zidek, J. V. (1980). Adaptive multivariate ridge regression. Ann. Stat., 8, 64– 74. CrossrefWeb of Science®Google Scholar Brunk, H. D., and Pierce, D. A. (1974). Estimation of discrete multivariate densities for computer- aided differential diagnosis of disease. Biometrika, 61, 493– 499. CrossrefWeb of Science®Google Scholar Bruntz, S. M., Cleveland, W. S., Kleiner, B., and Warner, J. L. (1974). The dependence of ambient ozone on solar radiation, wind, temperature, and mixing height. Proc. Symp. Atmos. Diffus. Air Pollution Am. Meterol. Soc., 125– 128. Google Scholar Bruynooghe, M. (1978). Large data set clustering methods using the concept of space contraction. In C. A. Corsten L. and Hermans J. (Eds.), Compstat 1978, pp. 239– 245. Physica-Verlag: Vienna. Google Scholar Bryan, J. K. (1971). Classification and Clustering Using Density Estimation. Ph.D. thesis, University of Missouri, Columbia, Mo. Google Scholar Bryant, P., and Williamson, J. A. (1978). Asymptotic behaviour of classification maximum likelihood estimates. Biometrika, 65, 273– 281. CrossrefWeb of Science®Google Scholar Burr, E. J. (1968). Ouster sorting with mixed character types. I. Standardization of character values. Aust. Comput. J., 1, 97– 99. Google Scholar Burr, E. J. (1970). Cluster sorting with mixed character types. II. Fusion strategies. Aust. Comput. J., 2, 98– 103. Google Scholar Buser, M. W., and Baroni-Urbani, C. (1982). A direct nondimensional clustering method for binary data. Biometrics, 38, 351– 360. CrossrefWeb of Science®Google Scholar Businger, P., and Golub, G. H. (1965). Linear least squares solutions by Householder transformations. Numer. Math., 7, 269– 276. CrossrefGoogle Scholar Byth, K., and McLachlan, G. J. (1980). Logistic regression compared to normal discrimination for non-normal populations. Aust. J. Stat., 22, 188– 196. Wiley Online LibraryGoogle Scholar Cacoullos, T. (1966). Estimation of a multivariate density. Ann. Inst. Stat. Math., 18, 179– 189. CrossrefWeb of Science®Google Scholar T. Cacoullos (Ed.). (1973). Discriminant Analysis and Applications. Academic Press: New York. CrossrefGoogle Scholar Caliński, T., and Harabasz, J. (1974). A dendrite method for cluster analysis. Commun. Stat., 3, 1– 27. Google Scholar Campbell, N. A. (1978). The influence function as an aid in outlier detection in discriminant analysis. Appl. Stat., 27, 251– 258. Wiley Online LibraryWeb of Science®Google Scholar Campbell, N. A. (1980a). Robust procedures in multivariate analysis. I. Robust covariance estimation. Appl. Stat., 29, 231– 237. Wiley Online LibraryWeb of Science®Google Scholar Campbell, N. A. (1980b). Shrunken estimators in discriminant and canonical variate analysis. Appl. Stat., 29, 5– 14. Wiley Online LibraryGoogle Scholar Campbell, N. A. (1981). Graphical comparison of covariance matrices. Aust. J. Stat., 23, 21– 37. Wiley Online LibraryGoogle Scholar Campbell, N. A. (1982). Robust procedures in multivariate analysis. II. Robust canonical variate analysis. Appl. Stat., 31, 1– 8. Wiley Online LibraryWeb of Science®Google Scholar Campbell, N. A., and Atchley, W. R. (1981). The geometry of canonical variate analysis. Syst. Zool., 30, 268– 280. CrossrefWeb of Science®Google Scholar Carmichael, J. W., and Sneath, P. H. A. (1969). Taxometric maps. Syst. Zool., 18, 402– 415. CrossrefWeb of Science®Google Scholar Carmichael, J. W., George, J. A, and Juluis, R. S. (1968). Finding natural clusters. Syst. Zool., 17, 144– 150. CrossrefWeb of Science®Google Scholar Carroll, J. D., and Arable, P. (1980). Multidimensional scaling. Annu Rev. Psychol., 31, 607– 649. CrossrefCASPubMedWeb of Science®Google Scholar Carroll, J. D., and Chang, J. J. (1970). Analysis of individual differences in multidimensional scaling via an N-way generalization of “Eckart-Young” decomposition. Psychometrika, 35, 283– 319. CrossrefWeb of Science®Google Scholar Carroll, R. J. (1978). On the asymptotic distribution of multivariate M-estimates.

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