pod.bootstrap_pod_ci

bootstrap_pod_ci(
    X,
    y,
    X_eval,
    threshold,
    model_type,
    model_params,
    bandwidth,
    dist_info,
    n_boot=1000,
)

Estimates 95% Confidence Bounds for the PoD curve via Bootstrapping.

This function resamples the original data with replacement n_boot times. For each resample, it refits the Mean Model (dynamically rebuilding either a Polynomial or Kriging model), recalculates residuals, and generates a new PoD curve. If Kriging is selected, the optimizer is disabled during bootstrapping to remain computationally tractable.

Parameters

Name	Type	Description	Default
X	np.ndarray	Original input data.	required
y	np.ndarray	Original outcome data.	required
X_eval	np.ndarray	Grid points for evaluation.	required
threshold	float	Detection threshold.	required
model_type	str	The type of mean model (‘Polynomial’ or ‘Kriging’).	required
model_params	Any	Model parameters (integer degree for Poly, kernel for Kriging).	required
bandwidth	float	Smoothing bandwidth (fixed from original fit).	required
dist_info	Tuple[str, Tuple]	Error distribution (fixed from original fit).	required
n_boot	int	Number of bootstrap iterations. Defaults to 1000.	1000

Returns

Name	Type	Description
	Tuple[np.ndarray, np.ndarray]	Tuple[np.ndarray, np.ndarray]: - lower_ci: The 2.5th percentile PoD curve (Lower 95% Bound). - upper_ci: The 97.5th percentile PoD curve (Upper 95% Bound).

Examples

# Generate 95% confidence bounds
lower, upper = bootstrap_pod_ci(
    X, y, X_eval, threshold=0.5,
    model_type='Polynomial', model_params=3,
    bandwidth=1.5, dist_info=('norm', (0, 1)), n_boot=100
)