bankruptcy

The data set contains the ratio of retained earnings (RE) to total assets, and
the ratio of earnings before interests and taxes (EBIT) to total assets of 66
American firms recorded in the form of ratios. Half of the selected firms had
filed for bankruptcy.

datasets

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random.
Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and
Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some special or limiting
cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian,
Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals,
Hyperbolic, and Symmetric Hyperbolic.

Hung Tong

MixtureMissing

Robust and Flexible Model-Based Clustering for Data Sets with
Missing Values at Random

Cristina Tortora

bankruptcy function

A data frame with 66 rows and 3 variables:<dl>
 <dt>Y</dt>
<dd>Status of the firm: 0 for bankruptcy and 1 for financially sound.</dd> <dt>RE</dt>
<dd>Ratio of retained earnings.</dd> <dt>EBIT</dt>
<dd>Ratio of earnings before interests and taxes.</dd>
</dl>

Format

Bankruptcy Data Set — bankruptcy

A data frame with 66 rows and 3 variables:<dl>
 <dt>Y</dt>
<dd>Status of the firm: 0 for bankruptcy and 1 for financially sound.</dd>

 <dt>RE</dt>
<dd>Ratio of retained earnings.</dd>

 <dt>EBIT</dt>
<dd>Ratio of earnings before interests and taxes.</dd>


</dl>

bankruptcy: Bankruptcy Data Set

Description

Usage

Arguments

Format