Dagum distribution

Last updated
Dagum Distribution
Probability density function
DagumPDF.png
Cumulative distribution function
DagumCDF.png
Parameters shape
shape
scale
Support
PDF
CDF
Mean

Contents

[1]
Median
Mode
Variance

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. [2] [3] The Dagum distribution arose from several variants of a new model on the size distribution of personal income and is mostly associated with the study of income distribution. There is both a three-parameter specification (Type I) and a four-parameter specification (Type II) of the Dagum distribution; a summary of the genesis of this distribution can be found in "A Guide to the Dagum Distributions". [4] A general source on statistical size distributions often cited in work using the Dagum distribution is Statistical Size Distributions in Economics and Actuarial Sciences. [5]

Definition

The cumulative distribution function of the Dagum distribution (Type I) is given by

The corresponding probability density function is given by

The quantile function is given by

The Dagum distribution can be derived as a special case of the generalized Beta II (GB2) distribution (a generalization of the Beta prime distribution):

There is also an intimate relationship between the Dagum and Singh–Maddala / Burr distribution.

The cumulative distribution function of the Dagum (Type II) distribution adds a point mass at the origin and then follows a Dagum (Type I) distribution over the rest of the support (i.e. over the positive halfline)

Use in economics

The Dagum distribution is often used to model income and wealth distribution. The relation between the Dagum Type I and the gini coefficient is summarized in the formula below: [6]

where is the gamma function. Note that this value is independent from the scale-parameter, .

Although the Dagum distribution is not the only three parameter distribution used to model income distribution it is usually the most appropriate. [7]

The Dagum distribution has been extended to model net wealth distribution, accounting for the observed frequencies of negative and null net wealth. This generalized model, known as the Dagum General Model of Net Wealth Distribution, [8] is a mixture model consisting of an atomic distribution at zero (representing economic units with no wealth) with two continuous distributions for negative and positive net wealth.

Related Research Articles

<span class="mw-page-title-main">Cauchy distribution</span> Probability distribution

The Cauchy distribution, named after Augustin Cauchy, is a continuous probability distribution. It is also known, especially among physicists, as the Lorentz distribution, Cauchy–Lorentz distribution, Lorentz(ian) function, or Breit–Wigner distribution. The Cauchy distribution is the distribution of the x-intercept of a ray issuing from with a uniformly distributed angle. It is also the distribution of the ratio of two independent normally distributed random variables with mean zero.

<span class="mw-page-title-main">Pareto distribution</span> Probability distribution

The Pareto distribution, named after the Italian civil engineer, economist, and sociologist Vilfredo Pareto, is a power-law probability distribution that is used in description of social, quality control, scientific, geophysical, actuarial, and many other types of observable phenomena; the principle originally applied to describing the distribution of wealth in a society, fitting the trend that a large portion of wealth is held by a small fraction of the population. The Pareto principle or "80-20 rule" stating that 80% of outcomes are due to 20% of causes was named in honour of Pareto, but the concepts are distinct, and only Pareto distributions with shape value of log45 ≈ 1.16 precisely reflect it. Empirical observation has shown that this 80-20 distribution fits a wide range of cases, including natural phenomena and human activities.

<span class="mw-page-title-main">Gamma distribution</span> Probability distribution

In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. The exponential distribution, Erlang distribution, and chi-squared distribution are special cases of the gamma distribution. There are two equivalent parameterizations in common use:

  1. With a shape parameter and a scale parameter .
  2. With a shape parameter and an inverse scale parameter , called a rate parameter.
<span class="mw-page-title-main">Stable distribution</span> Distribution of variables which satisfies a stability property under linear combinations

In probability theory, a distribution is said to be stable if a linear combination of two independent random variables with this distribution has the same distribution, up to location and scale parameters. A random variable is said to be stable if its distribution is stable. The stable distribution family is also sometimes referred to as the Lévy alpha-stable distribution, after Paul Lévy, the first mathematician to have studied it.

In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions. By the extreme value theorem the GEV distribution is the only possible limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables. Note that a limit distribution needs to exist, which requires regularity conditions on the tail of the distribution. Despite this, the GEV distribution is often used as an approximation to model the maxima of long (finite) sequences of random variables.

<span class="mw-page-title-main">Inverse-gamma distribution</span> Two-parameter family of continuous probability distributions

In probability theory and statistics, the inverse gamma distribution is a two-parameter family of continuous probability distributions on the positive real line, which is the distribution of the reciprocal of a variable distributed according to the gamma distribution.

<span class="mw-page-title-main">Chi distribution</span> Probability distribution

In probability theory and statistics, the chi distribution is a continuous probability distribution over the non-negative real line. It is the distribution of the positive square root of a sum of squared independent Gaussian random variables. Equivalently, it is the distribution of the Euclidean distance between a multivariate Gaussian random variable and the origin. It is thus related to the chi-squared distribution by describing the distribution of the positive square roots of a variable obeying a chi-squared distribution.

<span class="mw-page-title-main">Kumaraswamy distribution</span>

In probability and statistics, the Kumaraswamy's double bounded distribution is a family of continuous probability distributions defined on the interval (0,1). It is similar to the beta distribution, but much simpler to use especially in simulation studies since its probability density function, cumulative distribution function and quantile functions can be expressed in closed form. This distribution was originally proposed by Poondi Kumaraswamy for variables that are lower and upper bounded with a zero-inflation. This was extended to inflations at both extremes [0,1] in later work with S. G. Fletcher.

<span class="mw-page-title-main">ARGUS distribution</span>

In physics, the ARGUS distribution, named after the particle physics experiment ARGUS, is the probability distribution of the reconstructed invariant mass of a decayed particle candidate in continuum background.

<span class="mw-page-title-main">Beta prime distribution</span> Probability distribution

In probability theory and statistics, the beta prime distribution is an absolutely continuous probability distribution. If has a beta distribution, then the odds has a beta prime distribution.

A ratio distribution is a probability distribution constructed as the distribution of the ratio of random variables having two other known distributions. Given two random variables X and Y, the distribution of the random variable Z that is formed as the ratio Z = X/Y is a ratio distribution.

<span class="mw-page-title-main">Fréchet distribution</span> Continuous probability 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

<span class="mw-page-title-main">Log-logistic distribution</span>

In probability and statistics, the log-logistic distribution is a continuous probability distribution for a non-negative random variable. It is used in survival analysis as a parametric model for events whose rate increases initially and decreases later, as, for example, mortality rate from cancer following diagnosis or treatment. It has also been used in hydrology to model stream flow and precipitation, in economics as a simple model of the distribution of wealth or income, and in networking to model the transmission times of data considering both the network and the software.

The term generalized logistic distribution is used as the name for several different families of probability distributions. For example, Johnson et al. list four forms, which are listed below.

<span class="mw-page-title-main">Generalized gamma distribution</span> Probability Distribution

The generalized gamma distribution is a continuous probability distribution with two shape parameters. It is a generalization of the gamma distribution which has one shape parameter. Since many distributions commonly used for parametric models in survival analysis are special cases of the generalized gamma, it is sometimes used to determine which parametric model is appropriate for a given set of data. Another example is the half-normal distribution.

In probability, statistics, economics, and actuarial science, the Benini distribution is a continuous probability distribution that is a statistical size distribution often applied to model incomes, severity of claims or losses in actuarial applications, and other economic data. Its tail behavior decays faster than a power law, but not as fast as an exponential. This distribution was introduced by Rodolfo Benini in 1905. Somewhat later than Benini's original work, the distribution has been independently discovered or discussed by a number of authors.

In statistics, the Davis distributions are a family of continuous probability distributions. It is named after Harold T. Davis (1892–1974), who in 1941 proposed this distribution to model income sizes.. It is a generalization of the Planck's law of radiation from statistical physics.

In statistics, the matrix t-distribution is the generalization of the multivariate t-distribution from vectors to matrices. The matrix t-distribution shares the same relationship with the multivariate t-distribution that the matrix normal distribution shares with the multivariate normal distribution. For example, the matrix t-distribution is the compound distribution that results from sampling from a matrix normal distribution having sampled the covariance matrix of the matrix normal from an inverse Wishart distribution.

In probability and statistics, the generalized beta distribution is a continuous probability distribution with four shape parameters, including more than thirty named distributions as limiting or special cases. It has been used in the modeling of income distribution, stock returns, as well as in regression analysis. The exponential generalized beta (EGB) distribution follows directly from the GB and generalizes other common distributions.

In statistics, the matrix F distribution is a matrix variate generalization of the F distribution which is defined on real-valued positive-definite matrices. In Bayesian statistics it can be used as the semi conjugate prior for the covariance matrix or precision matrix of multivariate normal distributions, and related distributions.

References

  1. Chotikapanich, Duangkamon; et al. (2018). "Using the GB2 Income Distribution". Econometrics. 6 (2): 21. doi: 10.3390/econometrics6020021 . hdl: 10419/195459 .
  2. Dagum, Camilo (1975). "A model of income distribution and the conditions of existence of moments of finite order". Bulletin of the International Statistical Institute . 46 (Proceedings of the 40th Session of the ISI, Contributed Paper): 199–205.
  3. Dagum, Camilo (1977). "A new model of personal income distribution: Specification and estimation". Économie Appliquée. 30: 413–437.
  4. Kleiber, Christian (2008). "A Guide to the Dagum Distributions" (PDF). In Chotikapanich, Duangkamon (ed.). Modeling Income Distributions and Lorenz Curves. Economic Studies in Inequality, Social Exclusion and Well-Being. Springer. pp. 97–117. doi:10.1007/978-0-387-72796-7_6. ISBN   978-0-387-72756-1.
  5. Kleiber, Christian; Kotz, Samuel (2003). Statistical Size Distributions in Economics and Actuarial Sciences. Wiley. ISBN   0-471-15064-9.
  6. Kleiber, Christian (2007). "A Guide to the Dagum Distributions". Working Paper.
  7. Bandourian, Ripsy; et al. (2002). "A Comparison of Parametric Models of Income Distribution Across Countries and Over Time". Luxembourg Income Study Working Paper No. 305. SSRN   324900.
  8. Dagum, Camilo (2006). "Wealth distribution models: analisys and applications". Statistica. 66 (3): 235–268. doi:10.6092/issn.1973-2201/1243. ISSN   1973-2201.