Probability distribution fitting or simply distribution fitting is the fitting of a probability distribution to a series of data concerning the repeated measurement of a variable phenomenon. The aim of distribution fitting is to predict the

probability Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speakin ...

or to forecast the

frequency Frequency is the number of occurrences of a repeating event per unit of time. It is also occasionally referred to as ''temporal frequency'' for clarity, and is distinct from ''angular frequency''. Frequency is measured in hertz (Hz) which is eq ...

of occurrence of the magnitude of the phenomenon in a certain interval. There are many probability distributions (see

list of probability distributions Many probability distributions that are important in theory or applications have been given specific names. Discrete distributions With finite support * The Bernoulli distribution, which takes value 1 with probability ''p'' and value 0 with ...

) of which some can be fitted more closely to the observed frequency of the data than others, depending on the characteristics of the phenomenon and of the distribution. The distribution giving a close fit is supposed to lead to good predictions. In distribution fitting, therefore, one needs to select a distribution that suits the data well.

Selection of distribution

The selection of the appropriate distribution depends on the presence or absence of symmetry of the data set with respect to the

central tendency In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution.Weisberg H.F (1992) ''Central Tendency and Variability'', Sage University Paper Series on Quantitative Applications in ...

. ''Symmetrical distributions'' When the data are symmetrically distributed around the mean while the frequency of occurrence of data farther away from the mean diminishes, one may for example select the

normal distribution In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is : f(x) = \frac e^ The parameter \mu ...

, the

logistic distribution Logistic may refer to: Mathematics * Logistic function, a sigmoid function used in many fields ** Logistic map, a recurrence relation that sometimes exhibits chaos ** Logistic regression, a statistical model using the logistic function ** Logit, ...

, or the Student's t-distribution. The first two are very similar, while the last, with one degree of freedom, has "heavier tails" meaning that the values farther away from the mean occur relatively more often (i.e. the

kurtosis In probability theory and statistics, kurtosis (from el, κυρτός, ''kyrtos'' or ''kurtos'', meaning "curved, arching") is a measure of the "tailedness" of the probability distribution of a real-valued random variable. Like skewness, kurt ...

is higher). The

Cauchy distribution The Cauchy distribution, named after Augustin Cauchy, is a continuous probability distribution. It is also known, especially among physicists, as the Lorentz distribution (after Hendrik Lorentz), Cauchy–Lorentz distribution, Lorentz(ian) fun ...

is also symmetric. ''Skew distributions to the right'' Negative and positive skew diagrams (English)

When the larger values tend to be farther away from the mean than the smaller values, one has a skew distribution to the right (i.e. there is positive

skewness In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive, zero, negative, or undefined. For a unimodal ...

), one may for example select the

log-normal distribution In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable is log-normally distributed, then has a norma ...

(i.e. the log values of the data are normally distributed), the

log-logistic distribution In probability and statistics, the log-logistic distribution (known as the Fisk distribution in economics) is a continuous probability distribution for a non-negative random variable. It is used in survival analysis as a parametric model for eve ...

(i.e. the log values of the data follow a

), the

Gumbel distribution In probability theory and statistics, the Gumbel distribution (also known as the type-I generalized extreme value distribution) is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions. Th ...

, the exponential distribution, the Pareto distribution, the

Weibull distribution In probability theory and statistics, the Weibull distribution is a continuous probability distribution. It is named after Swedish mathematician Waloddi Weibull, who described it in detail in 1951, although it was first identified by Maurice Re ...

, the

Burr distribution In probability theory, statistics and econometrics, the Burr Type XII distribution or simply the Burr distribution is a continuous probability distribution for a non-negative random variable. It is also known as the Singh–Maddala distribution a ...

, or the

Fréchet distribution The Fréchet distribution, also known as inverse Weibull distribution, is a special case of the generalized extreme value distribution. It has the cumulative distribution function :\Pr(X \le x)=e^ \text x>0. where ''α'' > 0 is a ...

. The last four distributions are bounded to the left. ''Skew distributions to the left'' When the smaller values tend to be farther away from the mean than the larger values, one has a skew distribution to the left (i.e. there is negative skewness), one may for example select the ''square-normal distribution'' (i.e. the normal distribution applied to the square of the data values),Left (negatively) skewed frequency histograms can be fitted to square Normal or mirrored Gumbel probability functions. On line

/ref> the inverted (mirrored) Gumbel distribution, the

Dagum distribution The Dagum distribution (or Mielke Beta-Kappa distribution) is a continuous probability distribution defined over positive real numbers. It is named after Camilo Dagum, who proposed it in a series of papers in the 1970s. The Dagum distribution ar ...

(mirrored Burr distribution), or the

Gompertz distribution In probability and statistics, the Gompertz distribution is a continuous probability distribution, named after Benjamin Gompertz. The Gompertz distribution is often applied to describe the distribution of adult lifespans by demographers and ac ...

, which is bounded to the left.

Techniques of fitting

The following techniques of distribution fitting exist: *''Parametric methods'', by which the

parameter A parameter (), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.). That is, a parameter is an element of a system that is useful, or critical, when ...

s of the distribution are calculated from the data series. The parametric methods are: ** Method of moments **

Maximum spacing estimation In statistics, maximum spacing estimation (MSE or MSP), or maximum product of spacing estimation (MPS), is a method for estimating the parameters of a univariate parametric model, statistical model. The method requires maximization of the geometr ...

**Method of

L-moment In statistics, L-moments are a sequence of statistics used to summarize the shape of a probability distribution. They are linear combinations of order statistics ( L-statistics) analogous to conventional moments, and can be used to calculate qu ...

s **

Maximum likelihood In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed stat ...

method :: *

Plotting position Plot or Plotting may refer to: Art, media and entertainment * Plot (narrative), the story of a piece of fiction Music * ''The Plot'' (album), a 1976 album by jazz trumpeter Enrico Rava * The Plot (band), a band formed in 2003 Other * ''Plot'' ...

plus

Regression analysis In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one ...

, using a transformation of the cumulative distribution function so that a

linear relation In linear algebra, a linear relation, or simply relation, between elements of a vector space or a module is a linear equation that has these elements as a solution. More precisely, if e_1,\dots,e_n are elements of a (left) module over a ring ...

is found between the

cumulative probability In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable X, or just distribution function of X, evaluated at x, is the probability that X will take a value less than or equal to x. Ev ...

and the values of the data, which may also need to be transformed, depending on the selected probability distribution. In this method the cumulative probability needs to be estimated by the

plotting position Plot or Plotting may refer to: Art, media and entertainment * Plot (narrative), the story of a piece of fiction Music * ''The Plot'' (album), a 1976 album by jazz trumpeter Enrico Rava * The Plot (band), a band formed in 2003 Other * ''Plot'' ...

Software for Generalized and Composite Probability Distributions. International Journal of Mathematical and Computational Methods, 4, 1-

o

/ref> ::

Generalization of distributions

It is customary to transform data logarithmically to fit symmetrical distributions (like the normal distribution, normal and logistic) to data obeying a distribution that is positively skewed (i.e. skew to the right, with

mean There are several kinds of mean in mathematics, especially in statistics. Each mean serves to summarize a given group of data, often to better understand the overall value (magnitude and sign) of a given data set. For a data set, the '' ari ...

mode Mode ( la, modus meaning "manner, tune, measure, due measure, rhythm, melody") may refer to: Arts and entertainment * '' MO''D''E (magazine)'', a defunct U.S. women's fashion magazine * ''Mode'' magazine, a fictional fashion magazine which is ...

, and with a right hand tail that is longer than the left hand tail), see

lognormal distribution In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable is log-normally distributed, then has a normal ...

and the loglogistic distribution. A similar effect can be achieved by taking the square root of the data. To fit a symmetrical distribution to data obeying a negatively skewed distribution (i.e. skewed to the left, with

, and with a right hand tail this is shorter than the left hand tail) one could use the squared values of the data to accomplish the fit. More generally one can raise the data to a power ''p'' in order to fit symmetrical distributions to data obeying a distribution of any skewness, whereby ''p'' < 1 when the skewness is positive and ''p'' > 1 when the skewness is negative. The optimal value of ''p'' is to be found by a numerical method. The numerical method may consist of assuming a range of ''p'' values, then applying the distribution fitting procedure repeatedly for all the assumed ''p'' values, and finally selecting the value of ''p'' for which the sum of squares of deviations of calculated probabilities from measured frequencies ( chi squared) is minimum, as is done in

CumFreq In statistics and data analysis the application software CumFreq is a tool for cumulative frequency analysis of a single variable and for probability distribution fitting. Originally the method was developed for the analysis of hydrological ...

. The generalization enhances the flexibility of probability distributions and increases their applicability in distribution fitting. The versatility of generalization makes it possible, for example, to fit approximately normally distributed data sets to a large number of different probability distributions, while negatively skewed distributions can be fitted to square normal and mirrored Gumbel distributions.Left (negatively) skewed frequency histograms can be fitted to square normal or mirrored Gumbel probability functions

/ref>

Inversion of skewness

Skewed distributions can be inverted (or mirrored) by replacing in the mathematical expression of the cumulative distribution function (F) by its complement: F'=1-F, obtaining the Cumulative distribution function#Complementary cumulative distribution function (tail distribution), complementary distribution function (also called

survival function The survival function is a function that gives the probability that a patient, device, or other object of interest will survive past a certain time. The survival function is also known as the survivor function or reliability function. The te ...

) that gives a mirror image. In this manner, a distribution that is skewed to the right is transformed into a distribution that is skewed to the left and vice versa. :: The technique of skewness inversion increases the number of probability distributions available for distribution fitting and enlarges the distribution fitting opportunities.

Shifting of distributions

Some probability distributions, like the

exponential Exponential may refer to any of several mathematical topics related to exponentiation, including: *Exponential function, also: **Matrix exponential, the matrix analogue to the above *Exponential decay, decrease at a rate proportional to value *Expo ...

, do not support data values (''X'') equal to or less than zero. Yet, when negative data are present, such distributions can still be used replacing ''X'' by ''Y''=''X''-''Xm'', where ''Xm'' is the minimum value of ''X''. This replacement represents a shift of the probability distribution in positive direction, i.e. to the right, because ''Xm'' is negative. After completing the distribution fitting of ''Y'', the corresponding ''X''-values are found from ''X''=''Y''+''Xm'', which represents a back-shift of the distribution in negative direction, i.e. to the left.
The technique of distribution shifting augments the chance to find a properly fitting probability distribution.

Composite distributions

The option exists to use two different probability distributions, one for the lower data range, and one for the higher like for example the Laplace distribution. The ranges are separated by a break-point. The use of such composite (discontinuous) probability distributions can be opportune when the data of the phenomenon studied were obtained under two sets different conditions.

Uncertainty of prediction

Predictions of occurrence based on fitted probability distributions are subject to

uncertainty Uncertainty refers to epistemic situations involving imperfect or unknown information. It applies to predictions of future events, to physical measurements that are already made, or to the unknown. Uncertainty arises in partially observable ...

, which arises from the following conditions: * The true probability distribution of events may deviate from the fitted distribution, as the observed data series may not be totally representative of the real probability of occurrence of the phenomenon due to

random error Observational error (or measurement error) is the difference between a measured value of a quantity and its true value.Dodge, Y. (2003) ''The Oxford Dictionary of Statistical Terms'', OUP. In statistics, an error is not necessarily a "mistake" ...

* The occurrence of events in another situation or in the future may deviate from the fitted distribution as this occurrence can also be subject to random error * A change of environmental conditions may cause a change in the probability of occurrence of the phenomenon An estimate of the uncertainty in the first and second case can be obtained with the binomial probability distribution using for example the probability of exceedance ''Pe'' (i.e. the chance that the event ''X'' is larger than a reference value ''Xr'' of ''X'') and the probability of non-exceedance ''Pn'' (i.e. the chance that the event ''X'' is smaller than or equal to the reference value ''Xr'', this is also called

). In this case there are only two possibilities: either there is exceedance or there is non-exceedance. This duality is the reason that the binomial distribution is applicable. With the binomial distribution one can obtain a

prediction interval In statistical inference, specifically predictive inference, a prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are ...

. Such an interval also estimates the risk of failure, i.e. the chance that the predicted event still remains outside the confidence interval. The confidence or risk analysis may include the

return period A return period, also known as a recurrence interval or repeat interval, is an average time or an estimated average time between events such as earthquakes, floods, landslides, or river discharge flows to occur. It is a statistical measurement typ ...

''T=1/Pe'' as is done in

hydrology Hydrology () is the scientific study of the movement, distribution, and management of water on Earth and other planets, including the water cycle, water resources, and environmental watershed sustainability. A practitioner of hydrology is call ...

Goodness of fit

By ranking the

goodness of fit The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measure ...

of various distributions one can get an impression of which distribution is acceptable and which is not.

Histogram and density function

From the cumulative distribution function (CDF) one can derive a histogram and the

probability density function In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) ca ...

(PDF).

References

{{Distribution fitting software