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 and The nonstandard forms can be obtained for the various functions using (note is a standard uniform random variate).

Function Name | Standard Function | Transformation |
---|---|---|

Cumulative Distribution Function (CDF) | ||

Probability Density Function (PDF) | ||

Percent Point Function (PPF) | ||

Probability Sparsity Function (PSF) | ||

Hazard Function (HF) | ||

Cumulative Hazard Functon (CHF) | ||

Survival Function (SF) | ||

Inverse Survival Function (ISF) | ||

Moment Generating Function (MGF) | ||

Random Variates | ||

(Differential) Entropy | ||

(Non-central) Moments | ||

Central Moments | ||

mean (mode, median), var | ||

skewness, kurtosis |

Non-central moments are defined using the PDF

Note, that these can always be computed using the PPF. Substitute in the above equation and get

which may be easier to compute numerically. Note that so that Central moments are computed similarly

In particular

Skewness is defined as

while (Fisher) kurtosis is

so that a normal distribution has a kurtosis of zero.

The median, is defined as the point at which half of the density is on one side and half on the other. In other words, so that

In addition, the mode, , is defined as the value for which the probability density function reaches it’s peak

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 is the PDF of a random-variable where is a vector of parameters ( *e.g.* and ), then for a collection of independent samples from this distribution, the joint distribution the
random vector is

The maximum likelihood estimate of the parameters are the parameters which maximize this function with fixed and given by the data:

Where

Note that if includes only shape parameters, the location and scale-parameters can be fit by replacing with in the log-likelihood function adding and minimizing, thus

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

where and are assumed known as the mean and variance of the **untransformed** distribution (when and ) and

One shape parameters (paramter in DATAPLOT is a scale-parameter). Standard form is

No moments?

Defined over (Note the CDF evaluation uses Eq. 3.194.1 on pg. 313 of Gradshteyn & Ryzhik (sixth edition).

Therefore,

This is the gamma distribution with and and where is called the degrees of freedom. If are all standard normal distributions, then has (standard) chi-square distribution with degrees of freedom.

The standard form (most often used in standard form only) is

This is just the Gamma distribution with shape parameter an integer.

This is a special case of the Gamma (and Erlang) distributions with shape parameter and the same location and scale parameters. The standard form is therefore ( )

This distribution’s pdf is the average of the inverse-Gaussian and reciprocal inverse-Gaussian pdf . We follow the notation of JKB here with for

This formula can be expressed in terms of the standard formulas for the Cauchy distribution (call the cdf and the pdf ). if is cauchy then is folded cauchy. Note that

No moments

If is Normal with mean and , then is a folded normal with shape parameter , location parameter and scale parameter . This is a special case of the non-central chi distribution with one- degree of freedom and non-centrality parameter Note that . The standard form of the folded normal is

Defined for . The distribution of if is chi-squared with degrees of freedom and is chi-squared with degrees of freedom.

A type of extreme-value distribution with a lower bound. Defined for and

where is Euler’s constant and equal to

Defined for and .

The mean is the negative of the right-skewed Frechet distribution given above, and the other statistical parameters can be computed from

where is Euler’s constant and equal to

Has been used in the analysis of extreme values. Has one shape parameter And

Note that the polygamma function is

where is a generalization of the Riemann zeta function called the Hurwitz zeta function Note that

Extreme value distributions with shape parameter .

For defined on

So,

For defined on For defined over all space

This is just the (left-skewed) Gumbel distribution for c=0.

A general probability form that reduces to many common distributions: and

Special cases are Weibull , half-normal and ordinary gamma distributions If then it is the inverted gamma distribution.

For and . In JKB the two shape parameters are reduced to the single shape-parameter . As is just a scale parameter when . If the distribution reduces to the exponential distribution scaled by Thus, the standard form is given as

where

One of a clase of extreme value distributions (right-skewed).

Note, that is negative the mean for the right-skewed distribution. Similar for median and mode. All other moments are the same.

If is Hyperbolic Secant distributed then is Half-Cauchy distributed. Also, if is (standard) Cauchy distributed, then is Half-Cauchy distributed. Special case of the Folded Cauchy distribution with The standard form is

No moments, as the integrals diverge.

This is a special case of the chi distribution with and and This is also a special case of the folded normal with shape parameter and If is (standard) normally distributed then, is half-normal. The standard form is

In the limit as for the generalized half-logistic we have the half-logistic defined over Also, the distribution of where has logistic distribtution.

Related to the logistic distribution and used in lifetime analysis. Standard form is (defined over all )

where is an integer given by

where is the Bernoulli polynomial of order evaluated at Thus

The standard form involves the shape parameter (in most definitions, is used). (In terms of the regress documentation ) and and is not a parameter in that distribution. A standard form is

This is related to the canonical form or JKB “two-parameter “inverse Gaussian when written in it’s full form with scale parameter and location parameter by taking and then is equal to where is the parameter used by JKB. We prefer this form because of it’s consistent use of the scale parameter. Notice that in JKB the skew and the kurtosis ( ) are both functions only of as shown here, while the variance and mean of the standard form here are transformed appropriately.

The ML estimator of the location parameter is

where is a sequence of mutually independent Laplace RV’s and the median is some number
between the and the order statistic ( *e.g.* take the average of these two) when is even. Also,

Replace with if it is known. If is known then this estimator is distributed as .

Special case of Lévy-stable distribution with and the support is . In standard form

No moments.

A special case of Lévy-stable distributions with and . In standard form it is defined for as

It has no finite moments.

Has one shape parameter >0. (Notice that the “Regress “ where is the scale parameter and is the mean of the underlying normal distribution). The standard form is

Notice that using JKB notation we have and we have given the so-called antilognormal form of the distribution. This is more consistent with the location, scale parameter description of general probability distributions.

Also, note that if is a log-normally distributed random-variable with and and shape parameter Then, is normally distributed with variance and mean

The distribution of where are independent standard normal variables and are constants. (In communications it is called the Marcum-Q function). Can be thought of as a Generalized Rayleigh-Rice distribution. For

The distribution of the ratio

where and are independent and distributed as a standard normal and chi with degrees of freedom. Note and .

A generalized F distribution. Two shape parameters and , and . The in the DATAPLOT reference is a scale parameter.

A generalization of the log-normal distribution and and

This distribution reduces to the log-normal distribution when

A generalization of the normal distribution, for

For this reduces to the normal distribution.

A general-purpose distribution with a variety of shapes controlled by Range of standard distribution is

The R-distribution with parameter is the distribution of the correlation coefficient of a random sample of size drawn from a bivariate normal distribution with The mean of the standard distribution is always zero and as the sample size grows, the distribution’s mass concentrates more closely about this mean.

This is Chi distribution with and and (no location parameter is generally used), the mode of the distribution is

Shape parameter is the incomplete beta integral and

As this distribution approaches the standard normal distribution.

where

The student Z distriubtion is defined over all space with one shape parameter

Interesting moments are

The moment generating function is

One shape parameter giving the distance to the peak as a percentage of the total extent of the non-zero portion. The location parameter is the start of the non- zero portion, and the scale-parameter is the width of the non-zero portion. In standard form we have

This is an exponential distribution defined only over a certain region . In standard form this is

A normal distribution restricted to lie within a certain range given by two parameters and . Notice that this and correspond to the bounds on in standard form. For we get

where

Defined for with shape parameter . Note, the PDF and CDF functions are periodic and are always defined over regardless of the location parameter. Thus, if an input beyond this range is given, it is converted to the equivalent angle in this range. For values of the PDF and CDF formulas below are used. Otherwise, a normal approximation with variance is used.

This can be used for defining circular variance.