stock611

This data set consists of 611 observations with 12 variables.

datasets

Outliers virtually exist in any datasets of any application field.
To avoid the impact of outliers, we need to use robust estimators.
Classical estimators of multivariate mean and covariance matrix are
the sample mean and the sample covariance matrix. Outliers will
affect the sample mean and the sample covariance matrix, and thus
they will affect the classical factor analysis which depends on
the classical estimators (Pison, G., Rousseeuw, P.J., Filzmoser,
P. and Croux, C. (2003) <doi:10.1016/S0047-259X(02)00007-6>).
So it is necessary to use the robust estimators of the sample
mean and the sample covariance matrix. There are several robust
estimators in the literature: Minimum Covariance Determinant estimator,
Orthogonalized Gnanadesikan-Kettenring, Minimum Volume Ellipsoid,
M, S, and Stahel-Donoho.
The most direct way to make multivariate analysis more robust is to replace
the sample mean and the sample covariance matrix of the classical estimators
to robust estimators (Maronna, R.A., Martin, D. and Yohai, V. (2006)
<doi:10.1002/0470010940>) (Todorov, V. and Filzmoser, P. (2009)
<doi:10.18637/jss.v032.i03>), which is our choice of robust factor
analysis. We created an object oriented solution for robust factor
analysis based on new S4 classes.

Frederic Bertrand

robustfa

Object Oriented Solution for Robust Factor Analysis

Ying-Ying Zhang (Robert)

stock611 function

A data frame with 611 observations on the following 12 variables.<dl>
 <dt><code>code</code></dt>
<dd>a numeric vector</dd> <dt><code>name</code></dt>
<dd>a numeric vector: the Chinese stocks name is replaced by integer, it can be found by its code.</dd> <dt><code>x1</code></dt>
<dd>a numeric vector: main business income (China Yuan)</dd> <dt><code>x2</code></dt>
<dd>a numeric vector: main business profit (China Yuan)</dd> <dt><code>x3</code></dt>
<dd>a numeric vector: total profit (China Yuan)</dd> <dt><code>x4</code></dt>
<dd>a numeric vector: net profit (China Yuan)</dd> <dt><code>x5</code></dt>
<dd>a numeric vector: earnings per share (EPS) (China Yuan)</dd> <dt><code>x6</code></dt>
<dd>a numeric vector: net assets per share (China Yuan)</dd> <dt><code>x7</code></dt>
<dd>a numeric vector: net return on assets (%)</dd> <dt><code>x8</code></dt>
<dd>a numeric vector: total return on assets (%)</dd> <dt><code>x9</code></dt>
<dd>a numeric vector: total assets (China Yuan)</dd> <dt><code>x10</code></dt>
<dd>a numeric vector: equity</dd> 
</dl>

Format

The Stocks Data - Year 2001 — stock611

A data frame with 611 observations on the following 12 variables.<dl>
 <dt><code>code</code></dt>
<dd>a numeric vector</dd>

 <dt><code>name</code></dt>
<dd>a numeric vector: the Chinese stocks name is replaced by integer, it can be found by its code.</dd>

 <dt><code>x1</code></dt>
<dd>a numeric vector: main business income (China Yuan)</dd>

 <dt><code>x2</code></dt>
<dd>a numeric vector: main business profit (China Yuan)</dd>

 <dt><code>x3</code></dt>
<dd>a numeric vector: total profit (China Yuan)</dd>

 <dt><code>x4</code></dt>
<dd>a numeric vector: net profit (China Yuan)</dd>

 <dt><code>x5</code></dt>
<dd>a numeric vector: earnings per share (EPS) (China Yuan)</dd>

 <dt><code>x6</code></dt>
<dd>a numeric vector: net assets per share (China Yuan)</dd>

 <dt><code>x7</code></dt>
<dd>a numeric vector: net return on assets (%)</dd>

 <dt><code>x8</code></dt>
<dd>a numeric vector: total return on assets (%)</dd>

 <dt><code>x9</code></dt>
<dd>a numeric vector: total assets (China Yuan)</dd>

 <dt><code>x10</code></dt>
<dd>a numeric vector: equity</dd>

 
</dl>

stock611: The Stocks Data - Year 2001

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