The following sections provide information about the families of parametric distributions that you can fit with the HISTOGRAM statement. Properties of these distributions are discussed by Johnson, Kotz, and Balakrishnan (1994, 1995).
The fitted density function is
and
Note: This notation is consistent with that of other distributions that you can fit with the HISTOGRAM statement. However, many texts, including Johnson, Kotz, and Balakrishnan (1995), write the beta density function as
The two parameterizations are related as follows:
The range of the beta distribution is bounded below by a threshold parameter and above by . If you specify a fitted beta curve by using the BETA option, must be less than the minimum data value and must be greater than the maximum data value. You can specify and with the THETA= and SIGMA= beta-options in parentheses after the keyword BETA. By default, and . If you specify THETA=EST and SIGMA=EST, maximum likelihood estimates are computed for and . However, three- and four-parameter maximum likelihood estimation does not always converge.
In addition, you can specify and with the ALPHA= and BETA= beta-options, respectively. By default, the procedure calculates maximum likelihood estimates for and . For example, to fit a beta density curve to a set of data bounded below by 32 and above by 212 with maximum likelihood estimates for and , use the following statement:
histogram Length / beta(theta=32 sigma=180);
The beta distributions are also referred to as Pearson Type I or II distributions. These include the power function distribution (), the arc sine distribution (), and the generalized arc sine distributions (, ).
You can use the DATA step function QUANTILE to compute beta quantiles and the DATA step function CDF to compute beta probabilities.
The fitted density function is
where
and
The threshold parameter must be less than or equal to the minimum data value. You can specify with the THRESHOLD= exponential-option. By default, . If you specify THETA=EST, a maximum likelihood estimate is computed for . In addition, you can specify with the SCALE= exponential-option. By default, the procedure calculates a maximum likelihood estimate for . Note that some authors define the scale parameter as .
The exponential distribution is a special case of both the gamma distribution (with ) and the Weibull distribution (with ). A related distribution is the extreme value distribution. If has an exponential distribution, then X has an extreme value distribution.
You can use the DATA step function QUANTILE to compute exponential quantiles and the DATA step function CDF to compute exponential probabilities.
The fitted density function is
where
and
The threshold parameter must be less than the minimum data value. You can specify with the THRESHOLD= gamma-option. By default, . If you specify THETA=EST, a maximum likelihood estimate is computed for . In addition, you can specify and with the SCALE= and ALPHA= gamma-options. By default, the procedure calculates maximum likelihood estimates for and .
The gamma distributions are also referred to as Pearson Type III distributions, and they include the chi-square, exponential, and Erlang distributions. The probability density function for the chi-square distribution is
Notice that this is a gamma distribution with , , and . The exponential distribution is a gamma distribution with , and the Erlang distribution is a gamma distribution with being a positive integer. A related distribution is the Rayleigh distribution. If where the ās are independent variables, then is distributed with a distribution having a probability density function of
If , the preceding distribution is referred to as the Rayleigh distribution.
You can use the DATA step function QUANTILE to compute gamma quantiles and the DATA step function CDF to compute gamma probabilities.
The fitted density function is
where
and
You can specify and with the MU= and SIGMA= Gumbel-options, respectively. By default, the procedure calculates maximum likelihood estimates for these parameters.
Note: The Gumbel distribution is also referred to as Type 1 extreme value distribution.
Note: The random variable X has Gumbel (Type 1 extreme value) distribution if and only if has Weibull distribution and has standard exponential distribution.
The fitted density function is
where
and
The location parameter has to be greater then zero. You can specify with the MU= iGauss-option. In addition, you can specify shape parameter with LAMBDA= iGauss-option. By default, the procedure calculates maximum likelihood estimates for and .
Note: The special case where and corresponds to the Wald distribution.
You can use the DATA step function QUANTILE to compute inverse Gaussian quantiles and the DATA step function CDF to compute inverse Gaussian probabilities.
The fitted density function is
where
and
The threshold parameter must be less than the minimum data value. You can specify with the THRESHOLD= lognormal-option. By default, . If you specify THETA=EST, a maximum likelihood estimate is computed for . You can specify and with the SCALE= and SHAPE= lognormal-options, respectively. By default, the procedure calculates maximum likelihood estimates for these parameters.
Note: The lognormal distribution is also referred to as the distribution in the Johnson system of distributions.
Note: This book uses to denote the shape parameter of the lognormal distribution, whereas is used to denote the scale parameter of the other distributions. The use of to denote the lognormal shape parameter is based on the fact that has a standard normal distribution if X is lognormally distributed. Based on this relationship, you can use the DATA step function PROBIT to compute lognormal quantiles and the DATA step function PROBNORM to compute probabilities.
The fitted density function is
where
and
You can specify and with the MU= and SIGMA= normal-options, respectively. By default, the procedure estimates with the sample mean and with the sample standard deviation.
You can use the DATA step function QUANTILE to compute beta quantiles and the DATA step function CDF to compute normal probabilities.
Note: The normal distribution is also referred to as the distribution in the Johnson system of distributions.
The fitted density function is
where
and
The support of the distribution is for and for .
Note: Special cases of Pareto distribution with and correspond respectively to the exponential distribution with mean and uniform distribution on the interval .
The threshold parameter must be less than the minimum data value. You can specify with the THETA= Pareto-option. By default, . You can also specify and with the ALPHA= and SIGMA= Pareto-options,respectively. By default, the procedure calculates maximum likelihood estimates for these parameters.
Note: Maximum likelihood estimation of the parameters works well if , but not otherwise. In this case the estimators are asymptotically normal and asymptotically efficient. The asymptotic normal distribution of the maximum likelihood estimates has mean and variance-covariance matrix
Note: If no local minimum is found in the region
there is no maximum likelihood estimator. More details on how to find maximum likelihood estimators and suggested algorithm can be found in Grimshaw(1993).
The fitted density function is
where
and
Note: This notation is consistent with that of other distributions that you can fit with the HISTOGRAM statement. However, many texts, including Johnson, Kotz, and Balakrishnan (1995), write the density function of power function distribution as
The two parameterizations are related as follows:
Note: The family of power function distributions is subclass of beta distribution with density function
where with parameter . Therefore, all properties and estimation procedures of beta distribution apply.
The range of the power function distribution is bounded below by a threshold parameter and above by . If you specify a fitted power function curve by using the POWER option, must be less than the minimum data value and must be greater than the maximum data value. You can specify and with the THETA= and SIGMA= power-options in parentheses after the keyword POWER. By default, and . If you specify THETA=EST and SIGMA=EST, maximum likelihood estimates are computed for and . However, three-parameter maximum likelihood estimation does not always converge.
In addition, you can specify with the ALPHA= power-option. By default, the procedure calculates maximum likelihood estimate for . For example, to fit a power function density curve to a set of data bounded below by 32 and above by 212 with maximum likelihood estimate for , use the following statement:
histogram Length / power(theta=32 sigma=180);
The fitted density function is
where
and
Note: The Rayleigh distribution is Weibull distribution with density function
and with shape parameter and scale parameter .
The threshold parameter must be less than the minimum data value. You can specify with the THETA= Rayleigh-option. By default, . In addition you can specify with the SIGMA= Rayleigh-option. By default, the procedure calculates maximum likelihood estimate for .
For example, to fit a Rayleigh density curve to a set of data bounded below by 32 with maximum likelihood estimate for , use the following statement:
histogram Length / rayleigh(theta=32);