OLS Gradient and Hessian

Computes the statistics needed for the OLS-based prediction-powered inference.

Usage

ols_get_stats(
  est,
  X_l,
  Y_l,
  f_l,
  X_u,
  f_u,
  w_l = NULL,
  w_u = NULL,
  use_u = TRUE
)

Arguments

est

(vector): Point estimates of the coefficients.

X_l

(matrix): Covariates for the labeled data set.

Y_l

(vector): Labels for the labeled data set.

f_l

(vector): Predictions for the labeled data set.

X_u

(matrix): Covariates for the unlabeled data set.

f_u

(vector): Predictions for the unlabeled data set.

w_l

(vector, optional): Sample weights for the labeled data set.

w_u

(vector, optional): Sample weights for the unlabeled data set.

use_u

(boolean, optional): Whether to use the unlabeled data set.

Value

(list): A list containing the following:

grads

(matrix): n x p matrix gradient of the loss function with respect to the coefficients.

grads_hat

(matrix): n x p matrix gradient of the loss function with respect to the coefficients, evaluated using the labeled predictions.

grads_hat_unlabeled

(matrix): N x p matrix gradient of the loss function with respect to the coefficients, evaluated using the unlabeled predictions.

inv_hessian

(matrix): p x p matrix inverse Hessian of the loss function with respect to the coefficients.