Lorenz.boot.combine: Combines bootstrap Lorenz regressions

Description

Lorenz.boot.combine combine outputs of different instances of the Lorenz.boot function.

Usage

Lorenz.boot.combine(boot_list)

Value

An object of class c("LR_boot", "LR") or c("PLR_boot", "PLR"), depending on whether a non-penalized or penalized regression was fitted.

The method confint is used on an object of class "LR_boot" or "PLR_boot" to obtain bootstrap inference on the model parameters.

For the non-penalized Lorenz regression, the returned object is a list containing the following components:

theta: The estimated vector of parameters. In the penalized case, it is a matrix where each row corresponds to a different selection method (e.g., BIC, bootstrap, cross-validation).
Gi.expl: The estimated explained Gini coefficient. In the penalized case, it is a vector, where each element corresponds to a different selection method.
LR2: The Lorenz-\(R^2\) of the regression. In the penalized case, it is a vector, where each element corresponds to a different selection method.
boot_out: An object of class "boot" containing the output of the bootstrap calculation.

For the penalized Lorenz regression, the returned object is a list containing the following components:

path: See Lorenz.Reg for the original path. To this path is added the out-of-bag (OOB) score.
lambda.idx: A vector indicating the index of the optimal lambda obtained by each selection method.
grid.idx: A vector indicating the index of the optimal grid parameter obtained by each selection method.

Note: The returned object may have additional classes such as "PLR_cv" if cross-validation was performed and used as a selection method in the penalized case.

Arguments

boot_list: list of objects, each element being the output of a call to the function Lorenz.boot.

References

Heuchenne, C. and A. Jacquemain (2022). Inference for monotone single-index conditional means: A Lorenz regression approach. Computational Statistics & Data Analysis 167(C).

Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024). A penalised bootstrap estimation procedure for the explained Gini coefficient. Electronic Journal of Statistics 18(1) 247-300.

Examples

Run this code

# \dontshow{
utils::example(Lorenz.Reg, echo = FALSE)
# }
# Continuing the Lorenz.Reg(.) example for the penalized regression:
boot_list <- list()
set.seed(123)
boot_list[[1]] <- Lorenz.boot(PLR, R = 10, boot_out_only = TRUE)
set.seed(456)
boot_list[[2]] <- Lorenz.boot(PLR, R = 10, boot_out_only = TRUE)
PLR_boot <- Lorenz.boot.combine(boot_list)
summary(PLR_boot)