cleverly.inference.multiplier_critical_value#

cleverly.inference.multiplier_critical_value(influence_curves: ndarray[tuple[Any, ...], dtype[float64]], std_errors: ndarray[tuple[Any, ...], dtype[float64]], *, n: int, cluster: ndarray[tuple[Any, ...], dtype[int64]] | None = None, alpha: float = 0.05, n_replicates: int = 1000, kind: Literal['rademacher', 'mammen', 'normal'] = 'rademacher', random_state: int | None = None) → float[source]#

The 1 - alpha quantile of the max-t statistic.

Parameters:

influence_curvesndarray: (n, k) influence curves, one column per estimand.
std_errorsndarray: (k,) pointwise standard errors.
nint: Number of observations.
clusterndarray or None: (n,) cluster codes. Weights are drawn per cluster when given.
alphafloat: Joint significance level.
n_replicatesint: Multiplier draws.
kind{“rademacher”, “mammen”, “normal”}, default=”rademacher”: Distribution of the multiplier weights.
random_stateint or None: Seed for those draws.

Returns: