dataGCE.test

Simulated data, used to demonstrate the functions of GCEstim. The seed used
is the different from the one used to generate <code>dataGCE</code> but the
remaining parameters are the same.

datasets

Estimation and inference using the Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) framework, a flexible method for solving ill-posed inverse problems and parameter estimation under uncertainty (Golan, Judge, and Miller (1996, ISBN:978-0471145925) "Maximum Entropy Econometrics: Robust Estimation with Limited Data"). The package includes routines for generalized cross entropy estimation of linear models including the implementation of a GME-GCE two steps approach. Diagnostic tools, and options to incorporate prior information through support and prior distributions are available (Macedo, Cabral, Afreixo, Macedo and Angelelli (2025) <doi:10.1007/978-3-031-97589-9_21>). In particular, support spaces can be defined by the user or be internally computed based on the ridge trace or on the distribution of standardized regression coefficients. Different optimization methods for the objective function can be used. An adaptation of the normalized entropy aggregation (Macedo and Costa (2019) <doi:10.1007/978-3-030-26036-1_2> "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) <doi:10.1080/03610918.2022.2057540>) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints.

Cabral Jorge

GCEstim

Regression Coefficients Estimation Using the Generalized Cross
Entropy

Macedo Pedro

Afreixo Vera

dataGCE.test function

A <code>data.frame</code> containing:<dl>
 <dt>X001</dt>
<dd>A N(0,1) independent variable.</dd> <dt>X002</dt>
<dd>A N(0,1) independent variable.</dd> <dt>X003</dt>
<dd>A N(0,1) independent variable.</dd> <dt>X004</dt>
<dd>A N(0,1) independent variable.</dd> <dt>X005</dt>
<dd>A N(0,1) independent variable.</dd> <dt>y</dt>
<dd>A Dependent variable: y = 1 + 3 * X003 + 6 * X004 + 9 * X005 + error;
 the error follows a normal distribution with mean equal to zero and variance
 such that the signal to noise ratio is equal to 5.</dd>
</dl>

Format

Simulated data set generated with fngendata — dataGCE.test

A <code>data.frame</code> containing:<dl>
 <dt>X001</dt>
<dd>A N(0,1) independent variable.</dd>

 <dt>X002</dt>
<dd>A N(0,1) independent variable.</dd>

 <dt>X003</dt>
<dd>A N(0,1) independent variable.</dd>

 <dt>X004</dt>
<dd>A N(0,1) independent variable.</dd>

 <dt>X005</dt>
<dd>A N(0,1) independent variable.</dd>

 <dt>y</dt>
<dd>A Dependent variable: y = 1 + 3 * X003 + 6 * X004 + 9 * X005 + error;
 the error follows a normal distribution with mean equal to zero and variance
 such that the signal to noise ratio is equal to 5.</dd>


</dl>

dataGCE.test: Simulated data set generated with fngendata

Description

Usage

Arguments

Format

Examples