.. _sphx_glr_api_gallery_statistics_bootstrap:

Bootstrap resampling
----------------------

Bootstrap confidence intervals via ``scipy.stats.bootstrap``, and the
leave-one-out jackknife.


.. raw:: html

  <div id='sg-tag-list' class='sphx-glr-tag-list'></div>


.. raw:: html

    <div class="sphx-glr-thumbnails">

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Compares a bootstrap confidence interval for the median (which has no simple closed form) against the parametric t-based interval for the mean, on the same skewed dataset.">

.. only:: html

  .. image:: /api/gallery/statistics/bootstrap/images/thumb/sphx_glr_plot_01_bootstrap_vs_parametric_thumb.png
    :alt:

  :doc:`/api/gallery/statistics/bootstrap/plot_01_bootstrap_vs_parametric`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Bootstrap vs. parametric confidence intervals</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Leaves out one observation at a time and recomputes the statistic. For the plug-in variance, the jackknife&#x27;s bias correction recovers the unbiased n - 1 variance exactly, and for the mean its standard error is exactly the classical s / sqrt(n). For a non-smooth statistic such as the midrange, the jackknife standard error is unreliable and disagrees with the bootstrap&#x27;s -- one of the shortcomings Efron&#x27;s bootstrap was designed to overcome.">

.. only:: html

  .. image:: /api/gallery/statistics/bootstrap/images/thumb/sphx_glr_plot_02_jackknife_thumb.png
    :alt:

  :doc:`/api/gallery/statistics/bootstrap/plot_02_jackknife`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">The jackknife</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /api/gallery/statistics/bootstrap/plot_01_bootstrap_vs_parametric
   /api/gallery/statistics/bootstrap/plot_02_jackknife