Page 199 - Computational Statistics Handbook with MATLAB

186 Computational Statistics Handbook with MATLAB

are collected they are plotted as a sequence of connected dots and
a stem-and-leaf is created at the same time.
• Discrete Quantile Plots: Hoaglin and Tukey [1985] provide similar
plots for other discrete distributions. These include the negative
binomial, the geometric and the logarithmic series. They also dis-
cuss graphical techniques for plotting confidence intervals instead
of points. This has the advantage of showing the confidence one
has for each count.
• Box plots: Other variations of the box plot have been described in
the literature. See McGill, Tukey and Larsen [1978] for a discussion
of the variable width box plot. With this type of display, the width
of the box represents the number of observations in each sample.
• Scatterplots: Scatterplot techniques are discussed in Carr, et al.
[1987]. The methods presented in this paper are especially pertinent
to the situation facing analysts today, where the typical data set
that must be analyzed is often very large n =( 10 10 …, 6 , ) . They
3
recommend various forms of binning (including hexagonal bin-
ning) and representation of the value by gray scale or symbol area.
• PPEDA: Jones and Sibson [1987] describe a steepest-ascent algo-
rithm that starts from either principal components or random
starts. Friedman [1987] combines steepest-ascent with a stepping
search to look for a region of interest. Crawford [1991] uses genetic
algorithms to optimize the projection index.
• Projection Pursuit: Other uses for projection pursuit have been
proposed. These include projection pursuit probability density esti-
mation [Friedman, Stuetzle, and Schroeder, 1984], projection pur-
suit regression [Friedman and Stuetzle, 1981], robust estimation [Li
and Chen, 1985], and projection pursuit for pattern recognition
[Flick, et al., 1990]. A 3-D projection pursuit algorithm is given in
Nason [1995]. For a theoretical and comprehensive description of
projection pursuit, the reader is directed to Huber [1985]. This
invited paper with discussion also presents applications of projec-
tion pursuit to computer tomography and to the deconvolution of
time series. Another paper that provides applications of projection
pursuit is Jones and Sibson [1987]. Not surprisingly, projection
pursuit has been combined with the grand tour by Cook, et al.
[1995]. Montanari and Lizzani [2001] apply projection pursuit to
the variable selection problem. Bolton and Krzanowski [1999]
describe the connection between projection pursuit and principal
component analysis.

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