Regression Coefficients Estimation Using the Generalized Cross Entropy
Accuracy measures
Case Names of lmgce Fitted Models
Change the step from lmgce object
Change the support from lmgce object
Extract cv.lmgce Coefficients
Extract lmgce Model Coefficients
Extract neagging Coefficients
Extract ridgetrace Model Coefficients
Extract tsbootgce Model Coefficients
Extract cv.lmgce Coefficients
Extract lmgce Model Coefficients
Extract neagging Coefficients
Extract ridgetrace Model Coefficients
Extract tsbootgce Model Coefficients
Confidence Intervals for lmgce Model Parameters and Normalized Entro...
Confidence Intervals for tsbootgce Model Parameters and Normalized E...
Cross-validation for lmgce
Residual Degrees-of-Freedom
Entropy Ratio test
Calculate lmgce Fitted Values
Calculate lmgce Fitted Values
Data generating function
Extract Model Formula from lmgce object
Generalized Cross entropy estimation
An add-in to easily generate the code for a lmgceanalysis
lmgce Shiny application
Extract design matrix from lmgce object
Normalized Entropy Aggregation for Inhomogeneous Large-Scale Data - Ne...
Extract the Number of Observations from a lmgce model fit
Normalized Entropy
Plot Diagnostics for a cv.lmgce Object
Plot Diagnostics for a lmgce Object
Plot Diagnostics for a neagging Object
Plot Diagnostics for a ridgetrace Object
Plot Diagnostics for a tsbootgce object
Predict method for lmgce Linear Model Fits
Print cv.lmgce object
Print a lmgce object
Print a ridgetrace object
Print Summary of lmgce Model Fits
Print tsbootgce object
Extract lmgce Model Residuals
Extract lmgce Model Residuals
Function to obtain the ridge trace and choose the support limits given...
Scale coefficients back
Summarise a linear regression model via generalized cross entropy fit
Time series bootstrap Cross entropy estimation
Variable Names of lmgce Fitted Models
Extract lmgce Model's Variance-Covariance Matrix
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.
Useful links