Comparison of Batches

  • Wolfgang Karl HärdleEmail author
  • Léopold Simar


Multivariate statistical analysis is concerned with analyzing and understanding data in high dimensions.


  1. ALLBUS, Germany General Social Survey 1980–2004 (2006)Google Scholar
  2. D.F. Andrews, Plots of high-dimensional data. Biometrics 28, 125–136 (1972)CrossRefGoogle Scholar
  3. H. Chernoff, Using faces to represent points in k-dimensional space graphically. J. Am. Stat. Assoc. 68, 361–368 (1973)CrossRefGoogle Scholar
  4. B. Flury, H. Riedwyl, Multivariate Statistics (Cambridge University Press, A practical Approach, 1988)Google Scholar
  5. M. Graham, J. Kennedy, Using curves to enhance parallel coordinate visualisations, in Information Visualization, 2003. IV 2003. Proceedings. Seventh International Conference on, pp. 10–16 (2003)Google Scholar
  6. W. Härdle, M. Müller, S. Sperlich, A. Werwatz, Non- and Semiparametric Models (Springer, Heidelberg, 2004)Google Scholar
  7. W. Härdle, Smoothing Techniques (With Implementations in S. Springer, New York, 1991)CrossRefGoogle Scholar
  8. W. Härdle, D.W. Scott, Smoothing by weighted averaging of rounded points. Comput. Stat. 7, 97–128 (1992)MathSciNetzbMATHGoogle Scholar
  9. D. Harrison, D.L. Rubinfeld, Hedonic prices and the demand for clean air. J. Environ. Econ. Manag. 5, 81–102 (1978)CrossRefGoogle Scholar
  10. W. Hoaglin, F. Mosteller, J.W. Tukey, Understanding Robust and Exploratory Data Analysis (Whiley, New York, 1983)Google Scholar
  11. A. Inselberg, A goodness of fit test for binary regression models based on smoohting methods. Vis. Comput. 1, 69–91 (1985)CrossRefGoogle Scholar
  12. S. Klinke, Polzehl, Implementation of kernel based indices in XGobi. Discussion paper 47, SFB 373, Humboldt-University of Berlin (1995)Google Scholar
  13. N. Lewin-Koh, Hexagon binnning. Technical Report (2006)Google Scholar
  14. E. Parzen, On estimating of a probability density and mode. Ann. Math. Stat. 35, 1065–1076 (1962)CrossRefGoogle Scholar
  15. M. Rosenblatt, Remarks on some nonparametric estimates of a density function. Ann. Math. Stat. 27, 832–837 (1956)MathSciNetCrossRefGoogle Scholar
  16. D.W. Scott, Averaged shifted histograms: effective nonparametric density estimation in several dimensions. Ann. Stat. 13, 1024–1040 (1985)CrossRefGoogle Scholar
  17. B.W. Silverman, Density Estimation for Statistics and Data Analysis, vol. 26, Monographs on Statistics and Applied Probability (Chapman and Hall, London, 1986)Google Scholar
  18. E.R. Tufte. The Visual Display of Quantitative Information (Graphics Press, 1983)Google Scholar
  19. P. Whittle, On the smoothing of probability density functions. J. Royal Stat. Soc. Ser. B 55, 549–557 (1958)MathSciNetzbMATHGoogle Scholar

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Authors and Affiliations

  1. 1.Ladislaus von Bortkiewicz Chair of StatisticsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Institute of Statistics, Biostatistics and Actuarial SciencesUniversité Catholique de LouvainLouvain-la-NeuveBelgium

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