[section:nc_chi_squared_dist Noncentral Chi-Squared Distribution]

``#include <boost/math/distributions/non_central_chi_squared.hpp>``

   namespace boost{ namespace math{

   template <class RealType = double,
             class ``__Policy``   = ``__policy_class`` >
   class non_central_chi_squared_distribution;

   typedef non_central_chi_squared_distribution<> non_central_chi_squared;

   template <class RealType, class ``__Policy``>
   class non_central_chi_squared_distribution
   {
   public:
      typedef RealType  value_type;
      typedef Policy    policy_type;

      // Constructor:
      non_central_chi_squared_distribution(RealType v, RealType lambda);

      // Accessor to degrees of freedom parameter v:
      RealType degrees_of_freedom()const;

      // Accessor to non centrality parameter lambda:
      RealType non_centrality()const;

      // Parameter finders:
      static RealType find_degrees_of_freedom(RealType lambda, RealType x, RealType p);
      template <class A, class B, class C>
      static RealType find_degrees_of_freedom(const complemented3_type<A,B,C>& c);

      static RealType find_non_centrality(RealType v, RealType x, RealType p);
      template <class A, class B, class C>
      static RealType find_non_centrality(const complemented3_type<A,B,C>& c);
   };

   }} // namespaces

The noncentral chi-squared distribution is a generalization of the
__chi_squared_distrib. If X[sub i] are [nu] independent, normally
distributed random variables with means [mu][sub i] and variances
[sigma][sub i][super 2], then the random variable

[equation nc_chi_squ_ref1]

is distributed according to the noncentral chi-squared distribution.

The noncentral chi-squared distribution has two parameters:
[nu] which specifies the number of degrees of freedom
(i.e. the number of X[sub i]), and [lambda] which is related to the
mean of the random variables X[sub i] by:

[equation nc_chi_squ_ref2]

(Note that some references define [lambda] as one half of the above sum).

This leads to a PDF of:

[equation nc_chi_squ_ref3]

where ['f(x;k)] is the central chi-squared distribution PDF, and
['I[sub v](x)] is a modified Bessel function of the first kind.

The following graph illustrates how the distribution changes
for different values of [lambda]:

[graph nccs_pdf]

[h4 Member Functions]

      non_central_chi_squared_distribution(RealType v, RealType lambda);

Constructs a Chi-Squared distribution with /v/ degrees of freedom
and non-centrality parameter /lambda/.

Requires v > 0 and lambda >= 0, otherwise calls __domain_error.

      RealType degrees_of_freedom()const;

Returns the parameter /v/ from which this object was constructed.

      RealType non_centrality()const;

Returns the parameter /lambda/ from which this object was constructed.


   static RealType find_degrees_of_freedom(RealType lambda, RealType x, RealType p);

This function returns the number of degrees of freedom /v/ such that:
`cdf(non_central_chi_squared<RealType, Policy>(v, lambda), x) == p`

   template <class A, class B, class C>
   static RealType find_degrees_of_freedom(const complemented3_type<A,B,C>& c);

When called with argument `boost::math::complement(lambda, x, q)`
this function returns the number of degrees of freedom /v/ such that:

`cdf(complement(non_central_chi_squared<RealType, Policy>(v, lambda), x)) == q`.

   static RealType find_non_centrality(RealType v, RealType x, RealType p);

This function returns the non centrality parameter /lambda/ such that:

`cdf(non_central_chi_squared<RealType, Policy>(v, lambda), x) == p`

   template <class A, class B, class C>
   static RealType find_non_centrality(const complemented3_type<A,B,C>& c);

When called with argument `boost::math::complement(v, x, q)`
this function returns the non centrality parameter /lambda/ such that:

`cdf(complement(non_central_chi_squared<RealType, Policy>(v, lambda), x)) == q`.

[h4 Non-member Accessors]

All the [link math_toolkit.dist_ref.nmp usual non-member accessor functions]
that are generic to all distributions are supported: __usual_accessors.

The domain of the random variable is \[0, +[infin]\].

[h4 Examples]

There is a
[link math_toolkit.stat_tut.weg.nccs_eg worked example]
for the noncentral chi-squared distribution.

[h4 Accuracy]

The following table shows the peak errors
(in units of [@http://en.wikipedia.org/wiki/Machine_epsilon epsilon])
found on various platforms with various floating point types.
The failures in the comparison to the [@http://www.r-project.org/ R Math library],
seem to be mostly in the corner cases when the probablity would be very small.
Unless otherwise specified any floating-point type that is narrower
than the one shown will have __zero_error.

[table_non_central_chi_squared_CDF]

[table_non_central_chi_squared_CDF_complement]

Error rates for the quantile
functions are broadly similar.  Special mention should go to
the `mode` function: there is no closed form for this function,
so it is evaluated numerically by finding the maxima of the PDF:
in principal this can not produce an accuracy greater than the
square root of the machine epsilon.

[h4 Tests]

There are two sets of test data used to verify this implementation:
firstly we can compare with published data, for example with
Table 6 of "Self-Validating Computations of Probabilities for
Selected Central and Noncentral Univariate Probability Functions",
Morgan C. Wang and William J. Kennedy,
Journal of the American Statistical Association,
Vol. 89, No. 427. (Sep., 1994), pp. 878-887.
Secondly, we have tables of test data, computed with this
implementation and using interval arithmetic - this data should
be accurate to at least 50 decimal digits - and is the used for
our accuracy tests.

[h4 Implementation]

The CDF and its complement are evaluated as follows:

First we determine which of the two values (the CDF or its
complement) is likely to be the smaller: for this we can use the
relation due to Temme (see "Asymptotic and Numerical Aspects of the
Noncentral Chi-Square Distribution", N. M. Temme, Computers Math. Applic.
Vol 25, No. 5, 55-63, 1993) that:

F([nu],[lambda];[nu]+[lambda]) [asymp] 0.5

and so compute the CDF when the random variable is less than
[nu]+[lambda], and its complement when the random variable is
greater than [nu]+[lambda].  If necessary the computed result
is then subtracted from 1 to give the desired result (the CDF or its
complement).

For small values of the non centrality parameter, the CDF is computed
using the method of Ding (see "Algorithm AS 275: Computing the Non-Central
#2 Distribution Function", Cherng G. Ding, Applied Statistics, Vol. 41,
No. 2. (1992), pp. 478-482).  This uses the following series representation:

[equation nc_chi_squ_ref4]

which requires just one call to __gamma_p_derivative with the subsequent
terms being computed by recursion as shown above.

For larger values of the non-centrality parameter, Ding's method can take
an unreasonable number of terms before convergence is achieved.  Furthermore,
the largest term is not the first term, so in extreme cases the first term may
be zero, leading to a zero result, even though the true value may be non-zero.

Therefore, when the non-centrality parameter is greater than 200, the method due
to Krishnamoorthy (see "Computing discrete mixtures of continuous distributions:
noncentral chisquare, noncentral t and the distribution of the
square of the sample multiple correlation coefficient",
Denise Benton and K. Krishnamoorthy, Computational Statistics &
Data Analysis, 43, (2003), 249-267) is used.

This method uses the well known sum:

[equation nc_chi_squ_ref5]

Where P[sub a](x) is the incomplete gamma function.

The method starts at the [lambda]th term, which is where the Poisson weighting
function achieves its maximum value, although this is not necessarily
the largest overall term.  Subsequent terms are calculated via the normal
recurrence relations for the incomplete gamma function, and iteration proceeds
both forwards and backwards until sufficient precision has been achieved.  It
should be noted that recurrence in the forwards direction of P[sub a](x) is
numerically unstable.  However, since we always start /after/ the largest
term in the series, numeric instability is introduced more slowly than the
series converges.

Computation of the complement of the CDF uses an extension of Krishnamoorthy's
method, given that:

[equation nc_chi_squ_ref6]

we can again start at the [lambda]'th term and proceed in both directions from
there until the required precision is achieved.  This time it is backwards
recursion on the incomplete gamma function Q[sub a](x) which is unstable.
However, as long as we start well /before/ the largest term, this is not an
issue in practice.

The PDF is computed directly using the relation:

[equation nc_chi_squ_ref3]

Where ['f(x; v)] is the PDF of the central __chi_squared_distrib and
['I[sub v](x)] is a modified Bessel function, see __cyl_bessel_i.
For small values of the
non-centrality parameter the relation in terms of __cyl_bessel_i
is used.  However, this method fails for large values of the
non-centrality parameter, so in that case the infinite sum is
evaluated using the method of Benton and Krishnamoorthy, and
the usual recurrence relations for successive terms.

The quantile functions are computed by numeric inversion of the CDF.
An improve starting quess is from
Thomas Luu,
[@http://discovery.ucl.ac.uk/1482128/, Fast and accurate parallel computation of quantile functions for random number generation, Doctorial Thesis, 2016].

There is no [@http://en.wikipedia.org/wiki/Closed_form closed form]
for the mode of the noncentral chi-squared
distribution: it is computed numerically by finding the maximum
of the PDF.  Likewise, the median is computed numerically via
the quantile.

The remaining non-member functions use the following formulas:

[equation nc_chi_squ_ref7]

Some analytic properties of noncentral distributions
(particularly unimodality, and monotonicity of their modes)
are surveyed and summarized by:

Andrea van Aubel & Wolfgang Gawronski, Applied Mathematics and Computation, 141 (2003) 3-12.

[endsect] [/section:nc_chi_squared_dist]

[/ nc_chi_squared.qbk
  Copyright 2008 John Maddock and Paul A. Bristow.
  Distributed under the Boost Software License, Version 1.0.
  (See accompanying file LICENSE_1_0.txt or copy at
  http://www.boost.org/LICENSE_1_0.txt).
]

