Details

A collection of distributions which can be approximated via Edgeworth and Cornish-Fisher expansions

Sum of (non-central) chi-square to powers

Let X_i ~ chi^2(delta_i, v_i) be independently distributed non-central chi-squares, where v_i are the degrees of freedom, and delta_i are the non-centrality parameters. Let w_i and p_i be given constants. Suppose

Y = sum w_i (X_i)^(p_i).

Then Y follows a weighted sum of chi-squares to power distribution. The special case where all the p_i are one is a 'sum of chi-squares' distribution; The special case where all the p_i are one half is a 'sum of chis' distribution;

Lambda Prime

Introduced by Lecoutre, the lambda prime distribution finds use in inference on the Sharpe ratio under normal returns. Suppose y ~ x^2(v), and Z is a standard normal.

T = Z + t sqrt(y/v)

takes a lambda prime distribution with parameters v, t. A lambda prime random variable can be viewed as a confidence variable on a non-central t because

t = (Z' + T)/sqrt(y/v)

Upsilon

The upsilon distribution generalizes the lambda prime to the case of the sum of multiple chi variables. That is, suppose y_i ~ x^2(v_i) independently and independently of Z, a standard normal. Then

T = Z + sum_i t_i sqrt(y_i/v_i)

takes an upsilon distribution with parameter vectors <v_1, v_2, ..., v_k>, <t_1, t_2, ..., t_k>.

The upsilon distribution is used in certain tests of the Sharpe ratio for independent observations.

K Prime

Introduced by Lecoutre, the K prime family of distributions generalize the (singly) non-central t, and lambda prime distributions. Suppose y ~ x^2(v1), and x ~ t(v2,(a/b) sqrt(y/v1)). Then the random variable

T = b x

takes a K prime distribution with parameters v1, v2, a, b. In Lecoutre's terminology, T ~ K'_v1,v2(a,b)

Equivalently, we can think of

T = (bZ + a sqrt(chi2_v1/v1)) / sqrt(chi2_v2/v2)

where Z is a standard normal, and the normal and the (central) chi-squares are independent of each other. When a=0 we recover a central t distribution; when v1=inf we recover a rescaled non-central t distribution; when b=0, we get a rescaled square root of a central F distribution; when v2=inf, we recover a Lambda prime distribution.

Doubly Noncentral t

The doubly noncentral t distribution generalizes the (singly) noncentral t distribution to the case where the numerator is the square root of a scaled noncentral chi-square distribution. That is, if X ~ N(u,1) independently of Y ~ x^2(k,theta), then the random variable

T = X / sqrt(Y/k)

takes a doubly non-central t distribution with parameters k, mu, theta.

Doubly Noncentral F

The doubly noncentral F distribution generalizes the (singly) noncentral F distribution to the case where the numerator is a scaled noncentral chi-square distribution. That is, if X ~ x^2(n1,theta1) independently of Y ~ x^2(n2,theta2), then the random variable

T = (X/n1) / (Y/n2)

takes a doubly non-central F distribution with parameters n1, n2, theta1, theta2.

Parameter recycling

It should be noted that the functions provided by sadists do not recycle their distribution parameters against the `x, p, q` or `n` parameters. This is in contrast to the common R idiom, and may cause some confusion. This is mostly for reasons of performance, but also because some of the distributions have vector-valued parameters; recycling over these would require the user to provide lists of parameters, which would be unpleasant.

Legal Mumbo Jumbo

sadists is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

Note

This package is maintained as a hobby.

Author(s)

Steven E. Pav [email protected]

References

Paolella, Marc. Intermediate Probability: A Computational Approach. Wiley, 2007. http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470026375.html

Lecoutre, Bruno. "Two Useful distributions for Bayesian predictive procedures under normal models." Journal of Statistical Planning and Inference 79, no. 1 (1999): 93-105.

Poitevineau, Jacques, and Lecoutre, Bruno. "Implementing Bayesian predictive procedures: The K-prime and K-square distributions." Computational Statistics and Data Analysis 54, no. 3 (2010): 724-731. http://arxiv.org/abs/1003.4890v1

Walck, C. "HAndbook on Statistical Distributions for experimentalists." 1996. http://www.stat.rice.edu/~dobelman/textfiles/DistributionsHandbook.pdf

shabbychef/sadists documentation built on May 29, 2019, 8:06 p.m.