Poisson bootstrapping

poisson_bootstrap.py

def poisson_bootstrap_nth_percentile_and_ci(
    data: List[float], n_iter: int = 1000, alpha: float = 0.95, percentile: float = 95
) -> Tuple[float, float, float, float]:

    data = np.array(data)
    n = len(data)

    weights = np.random.poisson(1, (n_iter, n))

    percentiles = []

    for weight in weights:
        resampled_data = np.repeat(data, weight)
        percentile = np.percentile(resampled_data, 95)
        percentiles.append(percentile)

    mean_percentile = np.mean(percentiles)
    lower_bound = np.percentile(percentiles, (1 - alpha) / 2 * 100)
    upper_bound = np.percentile(percentiles, (1 + alpha) / 2 * 100)

    return percentiles, mean_percentile, lower_bound, upper_bound