Multiple taxicab correspondence analysis

We compare the statistical analysis of multidimensional contingency tables by multiple correspondence analysis (MCA) and multiple taxicab correspondence analysis (MTCA). We will show in this paper: First, MTCA and MCA can produce different results. Second, taxicab correspondence analysis of a Burt table is equivalent to centroid correspondence analysis of the indicator matrix. Third, along the first principal axis, the projected response patterns in MTCA will be clustered and the number of cluster points is less than or equal to 1+ the number of variables. Fourth, visual maps produced by MTCA seem to be clearer and more readable in the presence of rarely occurring categories of the variables than the graphical displays produced by MCA. Two well known data sets are analyzed.

[1]  Michael Greenacre,et al.  Visualization of Categorical Data , 1998 .

[2]  J. Tukey,et al.  Multiple-Factor Analysis , 1947 .

[3]  P. G. Kevrekidis,et al.  The One-Dimensional Case , 2009 .

[4]  V. Choulakian,et al.  A Statistical Analysis of the Synoptic Gospels , 2006 .

[5]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[6]  L. A. van der Ark,et al.  Graphical display of latent budget analysis and latent class analysis, with special reference to correspondence analysis , 1998 .

[7]  I. Jolliffe Principal Component Analysis , 2002 .

[8]  Analyse factorielle des correspondances de tableaux multiples , 1988 .

[9]  E. B. Andersen,et al.  Modern factor analysis , 1961 .

[10]  西里 静彦,et al.  Elements of dual scaling : an introduction to practical data analysis , 1994 .

[11]  Jan de Leeuw,et al.  Weber Correspondence Analysis: The One-dimensional Case , 2003 .

[12]  S. Zamir,et al.  Lower Rank Approximation of Matrices by Least Squares With Any Choice of Weights , 1979 .

[13]  Vartan Choulakian,et al.  L1-norm projection pursuit principal component analysis , 2006, Comput. Stat. Data Anal..

[14]  V. Choulakian The optimality of the centroid method , 2003 .

[15]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[16]  R. Clarke,et al.  Theory and Applications of Correspondence Analysis , 1985 .

[17]  M. Hill,et al.  Nonlinear Multivariate Analysis. , 1990 .

[18]  C. Eckart,et al.  The approximation of one matrix by another of lower rank , 1936 .

[19]  Paul Horst,et al.  Factor analysis of data matrices , 1965 .

[20]  Jalal Almhana,et al.  Robust centroid method , 2006, Comput. Stat. Data Anal..

[21]  V. Choulakian Taxicab Correspondence Analysis , 2006, Psychometrika.

[22]  A. H. Siddiqi,et al.  Introduction to functional analysis with applications , 2006 .

[23]  V. Choulakian Taxicab Correspondence Analysis of Contingency Tables with One Heavyweight Column , 2008 .

[24]  V. Choulakian Transposition invariant principal component analysis in L1 for long tailed data , 2005 .

[25]  Michel Tenenhaus,et al.  An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling, homogeneity analysis and other methods for quantifying categorical multivariate data , 1985 .

[26]  Heungsun Hwang,et al.  An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents , 2006 .

[27]  I. Jolliffe,et al.  Nonlinear Multivariate Analysis , 1992 .

[28]  J. Leeuw,et al.  The Gifi system of descriptive multivariate analysis , 1998 .