lf.delta.beta1: Loss function to compute Delta(beta).

In depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Loss function to compute Delta(beta).

Description

This function defines the loss function used in computing the penalized local linear approximation of the test statistic in order to construct the bootstrap distribution of the test statistic.

Usage

lf.delta.beta1(
  Delta.sub,
  vnb,
  phi,
  Gn,
  Omegan,
  beta,
  c,
  r,
  data,
  par.space,
  epsilon.n,
  lambda.n
)

Arguments

Delta.sub	Subvector of Delta.
vnb	Bootstrapped stochastic process.
phi	Moment selection functions.
Gn	First-order approximation matrix.
Omegan	Correlation matrix of sample moment functions.
beta	Coefficient vector.
c	Projection vector.
r	Value of projected coefficient vector.
data	Data frame.
par.space	Matrix containing the bounds on the parameter space.
epsilon.n	Parameter used in constructing the feasible region as in Example 4.1 in Bei (2024). Not used in this function.
lambda.n	Weight of penalty term.

Value

Loss function evaluation evaluated at the given Delta.

References

Bei, X. (2024). Local linearieation based subvector inference in moment inequality models. Journal of Econometrics. 238:105549-

depCensoring documentation built on April 4, 2025, 1:52 a.m.