Source code for tailestim.estimators.pickands

"""Pickands estimator implementation for tail index estimation."""

from typing import Any, Dict, Tuple

import numpy as np

from .base import BaseTailEstimator
from .result import TailEstimatorResult
from .tail_methods import pickands_estimator as pickands_estimate




class PickandsEstimator(BaseTailEstimator):
    """Pickands estimator for tail index estimation.

    This class implements the Pickands estimator, which is a simple method
    that does not use bootstrap procedures. Note that estimates can only be
    calculated up to the floor(n/4)-th order statistic.

    Parameters
    ----------
    **kwargs : dict
        Additional parameters (not used by this estimator).

    """



    def __init__(self, **kwargs):
        # Pickands estimator doesn't use bootstrap
        super().__init__(bootstrap=False, **kwargs)


    def _estimate(self, ordered_data: np.ndarray) -> Tuple:
        """Estimate the tail index using the Pickands estimator.

        Parameters
        ----------
        ordered_data : np.ndarray
            Data array in decreasing order.

        Returns
        -------
        Tuple
            Contains estimation results from pickands_estimator.
        """
        return pickands_estimate(ordered_data)



    def get_params(self) -> Dict[str, Any]:
        """Get the parameters of the estimator.

        Returns
        -------
        dict
            Dictionary containing the parameters of the estimator.
        """
        return {**self.kwargs}




    def get_result(self) -> TailEstimatorResult:
        """Get the estimated parameters.

        Attributes
        ----------
        estimator : BaseTailEstimator
            The estimator instance (e.g., HillEstimator, PickandsEstimator, etc.) used for estimation.
        k_arr_ : np.ndarray
            Array of order statistics.
        xi_arr_ : np.ndarray
            Array of tail index estimates.

        Returns
        -------
        TailEstimatorResult
        """
        if self.results is None:
            raise ValueError("Model not fitted yet. Call fit() first.")

        k_arr, xi_arr = self.results

        res = {"estimator": self, "k_arr_": k_arr, "xi_arr_": xi_arr}

        return TailEstimatorResult(res)