STRUCT (3)

NAME
boost::accumulators::impl::non_coherent_tail_mean_impl - Estimation of the (non-coherent) tail mean based on order statistics (for both left and right tails)

SYNOPSIS

// In header: <boost/accumulators/statistics/tail_mean.hpp>
template<typename Sample, typename LeftRight>
struct non_coherent_tail_mean_impl {
  // types
  typedef numeric::functional::average< Sample, std::size_t >::result_type float_type;
  typedef float_type                                                       result_type;
  // construct/copy/destruct
  non_coherent_tail_mean_impl(dont_care);
  // public member functions
  template<typename Args> result_type result(Args const &) const;
};

DESCRIPTION

An estimation of the non-coherent tail mean [1][IMAGE][2] [$ is given by the mean of the [3][IMAGE][4] [$eil$] smallest samples (left tail) or the mean of the [5][IMAGE][6] [$eil$] largest samples (right tail), [7][IMAGE][8] [$n$] being the total number of samples and [9][IMAGE][10] [$lpha$] the quantile level:

Equation 1.14.  [11].sp .RS 4 [IMAGE][12] \[ \widehat{NCTM}_{n,\alpha}^{\mathrm{right}}(X) = \frac{1}{\lceil n(1-\alpha)\rceil} \sum_{i=\lceil \alpha n \rceil}^n X_{i:n} \] .RE

Equation 1.15.  [13].sp .RS 4 [IMAGE][14] \[ \widehat{NCTM}_{n,\alpha}^{\mathrm{left}}(X) = \frac{1}{\lceil n\alpha\rceil} \sum_{i=1}^{\lceil \alpha n \rceil} X_{i:n} \] .RE

It thus requires the caching of at least the [15][IMAGE][4] [$eil$] smallest or the [16][IMAGE][6] [$eil$] largest samples.

  non_coherent_tail_mean_impl public construct/copy/destruct

    1.

    non_coherent_tail_mean_impl(dont_care);

  non_coherent_tail_mean_impl public member functions

    1.

    template<typename Args> result_type result(Args const & args) const;