companies


  Data extracted from Standard & Poor's Compustat PC Plus. This dataset has been used to illustrate some influence diagnostic techniques.


datasets

Routines to perform estimation and inference under the multivariate t-distribution.
Currently, the following methodologies are implemented: multivariate mean and covariance
estimation, hypothesis testing about the mean, equicorrelation and homogeneity of variances,
the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate
generation. Some auxiliary functions are also provided.

Felipe Osorio

Estimation and Testing for the Multivariate t-Distribution

companies function

A data frame with 26 observations on the following 3 variables.
 <dl>
 <dt>book</dt><dd> book value in dollars per share at the end of 1992.</dd> <dt>net</dt><dd> net sales in millions of dollars in 1992.</dd> <dt>ratio</dt><dd> sales to assets ratio in 1992.</dd> </dl>

Format

Hadi, A.S., and Nyquist, H. (1999).
 Frechet distance as a tool for diagnosing multivariate data.
 Linear Algebra and Its Applications 289, 183-201. Hadi, A.S., and Son, M.S. (1997).
 Detection of unusual observations in regression and multivariate data.
 In: A. Ullah, D.E.A. Giles (Eds.) Handbook of Applied Economic Statistics.
 Marcel Dekker, New York. pp. 441-463.

companies: Financial data

Description

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