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# Continuous Statistical Distributions

## Overview

All distributions will have location (L) and Scale (S) parameters along with any shape parameters needed, the names for the shape parameters will vary. Standard form for the distributions will be given where \(L=0.0\) and \(S=1.0.\) The nonstandard forms can be obtained for the various functions using (note \(U\) is a standard uniform random variate).

Function Name | Standard Function | Transformation
--- | --- | ---
Cumulative Distribution Function (CDF) | \(F\left(x\right)\) | \(F\left(x;L,S\right)=F\left(\frac{\left(x-L\right)}{S}\right)\)
Probability Density Function (PDF) | \(f\left(x\right)=F^{\prime}\left(x\right)\) | \(f\left(x;L,S\right)=\frac{1}{S}f\left(\frac{\left(x-L\right)}{S}\right)\)
Percent Point Function (PPF) | \(G\left(q\right)=F^{-1}\left(q\right)\) | \(G\left(q;L,S\right)=L+SG\left(q\right)\)
Probability Sparsity Function (PSF) | \(g\left(q\right)=G^{\prime}\left(q\right)\) | \(g\left(q;L,S\right)=Sg\left(q\right)\)
Hazard Function (HF) | \(h_{a}\left(x\right)=\frac{f\left(x\right)}{1-F\left(x\right)}\) | \(h_{a}\left(x;L,S\right)=\frac{1}{S}h_{a}\left(\frac{\left(x-L\right)}{S}\right)\)
Cumulative Hazard Function (CHF) | \(H_{a}\left(x\right)=\) \(\log\frac{1}{1-F\left(x\right)}\) | \(H_{a}\left(x;L,S\right)=H_{a}\left(\frac{\left(x-L\right)}{S}\right)\)
Survival Function (SF) | \(S\left(x\right)=1-F\left(x\right)\) | \(S\left(x;L,S\right)=S\left(\frac{\left(x-L\right)}{S}\right)\)
Inverse Survival Function (ISF) | \(Z\left(\alpha\right)=S^{-1}\left(\alpha\right)=G\left(1-\alpha\right)\) | \(Z\left(\alpha;L,S\right)=L+SZ\left(\alpha\right)\)
Moment Generating Function (MGF) | \(M_{Y}\left(t\right)=E\left[e^{Yt}\right]\) | \(M_{X}\left(t\right)=e^{Lt}M_{Y}\left(St\right)\)
Random Variates | \(Y=G\left(U\right)\) | \(X=L+SY\)
(Differential) Entropy | \(h\left[Y\right]=-\int f\left(y\right)\log f\left(y\right)dy\) | \(h\left[X\right]=h\left[Y\right]+\log S\)
(Non-central) Moments | \(\mu_{n}^{\prime}=E\left[Y^{n}\right]\) | \(E\left[X^{n}\right]=L^{n}\sum_{k=0}^{N}\left(\begin{array}{c} n\\ k\end{array}\right)\left(\frac{S}{L}\right)^{k}\mu_{k}^{\prime}\)
Central Moments | \(\mu_{n}=E\left[\left(Y-\mu\right)^{n}\right]\) | \(E\left[\left(X-\mu_{X}\right)^{n}\right]=S^{n}\mu_{n}\)
mean (mode, median), var | \(\mu,\,\mu_{2}\) | \(L+S\mu,\, S^{2}\mu_{2}\)
skewness | \(\gamma_{1}=\frac{\mu_{3}}{\left(\mu_{2}\right)^{3/2}}\) | \(\gamma_{1}\)
kurtosis | \(\gamma_{2}=\frac{\mu_{4}}{\left(\mu_{2}\right)^{2}}-3\) | \(\gamma_{2}\)

### Moments

Non-central moments are defined using the PDF

\[\mu_{n}^{\prime}=\int_{-\infty}^{\infty}x^{n}f\left(x\right)dx.\]

Note, that these can always be computed using the PPF. Substitute \(x=G\left(q\right)\) in the above equation and get

\[\mu_{n}^{\prime}=\int_{0}^{1}G^{n}\left(q\right)dq\]

which may be easier to compute numerically. Note that \(q=F\left(x\right)\) so that \(dq=f\left(x\right)dx.\) Central moments are computed similarly \(\mu=\mu_{1}^{\prime}\)

\begin{eqnarray*} \mu_{n} & = & \int_{-\infty}^{\infty}\left(x-\mu\right)^{n}f\left(x\right)dx\\ & = & \int_{0}^{1}\left(G\left(q\right)-\mu\right)^{n}dq\\ & = & \sum_{k=0}^{n}\left(\begin{array}{c} n\\ k\end{array}\right)\left(-\mu\right)^{k}\mu_{n-k}^{\prime}\end{eqnarray*}

In particular

\begin{eqnarray*} \mu_{3} & = & \mu_{3}^{\prime}-3\mu\mu_{2}^{\prime}+2\mu^{3}\\ & = & \mu_{3}^{\prime}-3\mu\mu_{2}-\mu^{3}\\ \mu_{4} & = & \mu_{4}^{\prime}-4\mu\mu_{3}^{\prime}+6\mu^{2}\mu_{2}^{\prime}-3\mu^{4}\\ & = & \mu_{4}^{\prime}-4\mu\mu_{3}-6\mu^{2}\mu_{2}-\mu^{4}\end{eqnarray*}

Skewness is defined as

\[\gamma_{1}=\sqrt{\beta_{1}}=\frac{\mu_{3}}{\mu_{2}^{3/2}}\]

while (Fisher) kurtosis is

\[\gamma_{2}=\frac{\mu_{4}}{\mu_{2}^{2}}-3,\]

so that a normal distribution has a kurtosis of zero.

### Median and mode

The median, \(m_{n}\) is defined as the point at which half of the density is on one side and half on the other. In other words, \(F\left(m_{n}\right)=\frac{1}{2}\) so that

\[m_{n}=G\left(\frac{1}{2}\right).\]

In addition, the mode, \(m_{d}\) , is defined as the value for which the probability density function reaches it’s peak

\[m_{d}=\arg\max_{x}f\left(x\right).\]

### Fitting data

To fit data to a distribution, maximizing the likelihood function is common. Alternatively, some distributions have well-known minimum variance unbiased estimators. These will be chosen by default, but the likelihood function will always be available for minimizing.

If \(f\left(x;\boldsymbol{\theta}\right)\) is the PDF of a random-variable where \(\boldsymbol{\theta}\) is a vector of parameters ( e.g. \(L\) and \(S\) ), then for a collection of \(N\) independent samples from this distribution, the joint distribution the random vector \(\mathbf{x}\) is

\[f\left(\mathbf{x};\boldsymbol{\theta}\right)=\prod_{i=1}^{N}f\left(x_{i};\boldsymbol{\theta}\right).\]

The maximum likelihood estimate of the parameters \(\boldsymbol{\theta}\) are the parameters which maximize this function with \(\mathbf{x}\) fixed and given by the data:

\begin{eqnarray*} \boldsymbol{\theta}_{es} & = & \arg\max_{\boldsymbol{\theta}}f\left(\mathbf{x};\boldsymbol{\theta}\right)\\ & = & \arg\min_{\boldsymbol{\theta}}l_{\mathbf{x}}\left(\boldsymbol{\theta}\right).\end{eqnarray*}

Where

\begin{eqnarray*} l_{\mathbf{x}}\left(\boldsymbol{\theta}\right) & = & -\sum_{i=1}^{N}\log f\left(x_{i};\boldsymbol{\theta}\right)\\ & = & -N\overline{\log f\left(x_{i};\boldsymbol{\theta}\right)}\end{eqnarray*}

Note that if \(\boldsymbol{\theta}\) includes only shape parameters, the location and scale-parameters can be fit by replacing \(x_{i}\) with \(\left(x_{i}-L\right)/S\) in the log-likelihood function adding \(N\log S\) and minimizing, thus

\begin{eqnarray*} l_{\mathbf{x}}\left(L,S;\boldsymbol{\theta}\right) & = & N\log S-\sum_{i=1}^{N}\log f\left(\frac{x_{i}-L}{S};\boldsymbol{\theta}\right)\\ & = & N\log S+l_{\frac{\mathbf{x}-S}{L}}\left(\boldsymbol{\theta}\right)\end{eqnarray*}

If desired, sample estimates for \(L\) and \(S\) (not necessarily maximum likelihood estimates) can be obtained from samples estimates of the mean and variance using

\begin{eqnarray*} \hat{S} & = & \sqrt{\frac{\hat{\mu}_{2}}{\mu_{2}}}\\ \hat{L} & = & \hat{\mu}-\hat{S}\mu\end{eqnarray*}

where \(\mu\) and \(\mu_{2}\) are assumed known as the mean and variance of the untransformed distribution (when \(L=0\) and \(S=1\) ) and

\begin{eqnarray*} \hat{\mu} & = & \frac{1}{N}\sum_{i=1}^{N}x_{i}=\bar{\mathbf{x}}\\ \hat{\mu}_{2} & = & \frac{1}{N-1}\sum_{i=1}^{N}\left(x_{i}-\hat{\mu}\right)^{2}=\frac{N}{N-1}\overline{\left(\mathbf{x}-\bar{\mathbf{x}}\right)^{2}}\end{eqnarray*}

### Standard notation for mean

We will use

\[\overline{y\left(\mathbf{x}\right)}=\frac{1}{N}\sum_{i=1}^{N}y\left(x_{i}\right)\]

where \(N\) should be clear from context as the number of samples \(x_{i}\)

### References

- Documentation for ranlib, rv2, cdflib

- Eric Weisstein’s world of mathematics http://mathworld.wolfram.com/, http://mathworld.wolfram.com/topics/StatisticalDistributions.html

- Documentation to Regress+ by Michael McLaughlin item Engineering and Statistics Handbook (NIST), https://www.itl.nist.gov/div898/handbook/

- Documentation for DATAPLOT from NIST, https://www.itl.nist.gov/div898/software/dataplot/distribu.htm

- Norman Johnson, Samuel Kotz, and N. Balakrishnan Continuous Univariate Distributions, second edition, Volumes I and II, Wiley & Sons, 1994.

In the tutorials several special functions appear repeatedly and are listed here.

Symbol | Description | Definition
--- | --- | ---
\(\gamma\left(s, x\right)\) | lower incomplete Gamma function | \(\int_0^x t^{s-1} e^{-t} dt\)
\(\Gamma\left(s, x\right)\) | upper incomplete Gamma function | \(\int_x^\infty t^{s-1} e^{-t} dt\)
\(B\left(x;a,b\right)\) | incomplete Beta function | \(\int_{0}^{x} t^{a-1}\left(1-t\right)^{b-1} dt\)
\(I\left(x;a,b\right)\) | regularized incomplete Beta function | \(\frac{\Gamma\left(a+b\right)}{\Gamma\left(a\right)\Gamma\left(b\right)} \int_{0}^{x} t^{a-1}\left(1-t\right)^{b-1} dt\)
\(\phi\left(x\right)\) | PDF for normal distribution | \(\frac{1}{\sqrt{2\pi}}e^{-x^{2}/2}\)
\(\Phi\left(x\right)\) | CDF for normal distribution | \(\int_{-\infty}^{x}\phi\left(t\right) dt = \frac{1}{2}+\frac{1}{2}\mathrm{erf}\left(\frac{x}{\sqrt{2}}\right)\)
\(\psi\left(z\right)\) | digamma function | \(\frac{d}{dz} \log\left(\Gamma\left(z\right)\right)\)
\(\psi_{n}\left(z\right)\) | polygamma function | \(\frac{d^{n+1}}{dz^{n+1}}\log\left(\Gamma\left(z\right)\right)\)
\(I_{\nu}\left(y\right)\) | modified Bessel function of the first kind |
\(\mathrm{Ei}(\mathrm{z})\) | exponential integral | \(-\int_{-x}^\infty \frac{e^{-t}}{t} dt\)
\(\zeta\left(n\right)\) | Riemann zeta function | \(\sum_{k=1}^{\infty} \frac{1}{k^{n}}\)
\(\zeta\left(n,z\right)\) | Hurwitz zeta function | \(\sum_{k=0}^{\infty} \frac{1}{\left(k+z\right)^{n}}\)
\(\,{}_{p}F_{q}(a_{1},\ldots,a_{p};b_{1},\ldots,b_{q};z)\) | Hypergeometric function | \(\sum_{n=0}^{\infty} {\frac{(a_{1})_{n}\cdots(a_{p})_{n}}{(b_{1})_{n}\cdots(b_{q})_{n}}} \,{\frac{z^{n}}{n!}}\)

### Continuous Distributions in `scipy.stats`

- Alpha Distribution
- Anglit Distribution
- Arcsine Distribution
- Beta Distribution
- Beta Prime Distribution
- Bradford Distribution
- Burr Distribution
- Burr12 Distribution
- Cauchy Distribution
- Skewed Cauchy Distribution
- Chi Distribution
- Chi-squared Distribution
- Cosine Distribution
- Double Gamma Distribution
- Double Weibull Distribution
- Erlang Distribution
- Exponential Distribution
- Exponentiated Weibull Distribution
- Exponential Power Distribution
- Fatigue Life (Birnbaum-Saunders) Distribution
- Fisk (Log Logistic) Distribution
- Folded Cauchy Distribution
- Folded Normal Distribution
- Fratio (or F) Distribution
- Gamma Distribution
- Generalized Logistic Distribution
- Generalized Pareto Distribution
- Generalized Exponential Distribution
- Generalized Extreme Value Distribution
- Generalized Gamma Distribution
- Generalized Half-Logistic Distribution
- Generalized Hyperbolic Distribution
- Generalized Inverse Gaussian Distribution
- Generalized Normal Distribution
- Gibrat Distribution
- Gompertz (Truncated Gumbel) Distribution
- Gumbel (LogWeibull, Fisher-Tippetts, Type I Extreme Value) Distribution
- Gumbel Left-skewed (for minimum order statistic) Distribution
- HalfCauchy Distribution
- HalfNormal Distribution
- Half-Logistic Distribution
- Hyperbolic Secant Distribution
- Gauss Hypergeometric Distribution
- Inverted Gamma Distribution
- Inverse Normal (Inverse Gaussian) Distribution
- Inverted Weibull Distribution
- Jones and Faddy Skew-T Distribution
- Johnson SB Distribution
- Johnson SU Distribution
- KSone Distribution
- KStwo Distribution
- KStwobign Distribution
- Laplace (Double Exponential, Bilateral Exponential) Distribution
- Asymmetric Laplace Distribution
- Left-skewed Lévy Distribution
- Lévy Distribution
- Logistic (Sech-squared) Distribution
- Log Double Exponential (Log-Laplace) Distribution
- Log Gamma Distribution
- Log Normal (Cobb-Douglass) Distribution
- Log-Uniform Distribution
- Maxwell Distribution
- Mielke’s Beta-Kappa Distribution
- Nakagami Distribution
- Noncentral chi-squared Distribution
- Noncentral F Distribution
- Noncentral t Distribution
- Normal Distribution
- Normal Inverse Gaussian Distribution
- Pareto Distribution
- Pareto Second Kind (Lomax) Distribution
- Power Log Normal Distribution
- Power Normal Distribution
- Power-function Distribution
- R-distribution Distribution
- Rayleigh Distribution
- Rice Distribution
- Reciprocal Inverse Gaussian Distribution
- Relativistic Breit-Wigner Distribution
- Semicircular Distribution
- Studentized Range Distribution
- Student t Distribution
- Trapezoidal Distribution
- Triangular Distribution
- Truncated Exponential Distribution
- Truncated Normal Distribution
- Truncated Pareto Distribution
- Truncated Weibull Minimum Extreme Value Distribution
- Tukey-Lambda Distribution
- Uniform Distribution
- Von Mises Distribution
- Wald Distribution
- Weibull Maximum Extreme Value Distribution
- Weibull Minimum Extreme Value Distribution
- Wrapped Cauchy Distribution