G.hat: Compute the Gn matrix in step 3b of Bei (2024).

In depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Compute the Gn matrix in step 3b of Bei (2024).

Description

Compute the Gn matrix in step 3b of Bei (2024).

Usage

G.hat(
  data,
  beta,
  t,
  hp,
  mi.mat = NULL,
  m.avg = NULL,
  dm.avg = NULL,
  dmi.tens = NULL,
  D = NULL
)

Arguments

data	Data frame.
beta	Vector of coefficients.
t	Time point at which to evaluate the (derivatives of) the moment functions.
hp	List of hyperparamerers.
mi.mat	A precomputed matrix of moment function evaluations at each observation. If supplied, some computations can be skipped. Default is mi.mat = NULL.
m.avg	A precomputed vector of the sample average of the moment functions. If not supplied, this vector is computed. Default is m.avg = NULL.
dm.avg	Matrix of precomputed sample averages of the derivatives of the moment functions. Default is dm.avg = NULL.
dmi.tens	3D tensor of precomputed evaluations of the derivatives of the moment functions. Default is dmi.tens = NULL.
D	Diagonal of D-matrix.

Value

A matrix containing the partial derivatives of the variances of the moment functions. Each row corresponds to a moment function, each column corresponds to a covariate.

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.