hunt_wls: Weighted-least-squares hunting

In dScoreTest: Debiased Score Tests for Goodness of Fit and Model Comparison

Weighted-least-squares hunting

Description

Hunt by fitting residuals on X, trained by solving a weighted least squares. See Proposition 2 of Dhawan, Guo and Shah (2026).

Usage

hunt_wls(
  wls_hunt_method,
  resids,
  X,
  X.cols = 1:ncol(X),
  trim.outlier = TRUE,
  arg.wls_hunt_method = NULL,
  predict_fun_hunt = stats::predict
)

Arguments

wls_hunt_method	Function with signature wls_hunt_method(y, X, w, ...) that returns a fitted alternative model \hat{g} \in \mathcal{G} by minimizing \sum_i w_i (y_i - g(x_i))^2. The returned object must support predict_fun_hunt(g, X) for evaluation.
resids	Residuals (i.e., negative scores) of length n from the null model.
X	Covariates of dim n x p.
X.cols	Subset of covariates to hunt. (Default: 1:ncol(X))
trim.outlier	If TRUE, outliers in \hat{h}(X) will be trimmed from the hunted \hat{h} using Tukey's IQR rule.
arg.wls_hunt_method	Named list of additional arguments passed to wls_hunt_method (default to NULL).
predict_fun_hunt	Function with signature predict_fun_hunt(fit, X) returning a numeric vector of predictions from the alternative-model fit. Default stats::predict.

Value

An object of class "hunt", a list with elements:

hunt.fit

The fitted hunt model produced by wls_hunt_method.

trim.bounds

The Tukey IQR trimming bounds, or c(-Inf, Inf) when trim.outlier = FALSE.

predict_fun_hunt

The prediction function for hunt.fit, as supplied.

X.cols

The columns of X used for the hunt, as supplied.

h

A function with signature h(X) giving the hunted signal.

References

Dhawan, A., Guo, F. R. and Shah, R. D. (2026). The debiased score test: hunt-and-test for semiparametric hypotheses. arXiv:2607.28861. https://arxiv.org/abs/2607.28861

dScoreTest documentation built on Sept. 3, 2026, 1:06 a.m.