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| [[Image:Pearson system.png|300px|thumb|Diagram of the Pearson system, showing distributions of types I, III, VI, V, and IV in terms of β<sub>1</sub> (squared skewness) and β<sub>2</sub> (traditional kurtosis)]]
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| The '''Pearson distribution''' is a family of [[continuous probability distribution|continuous]] [[probability distribution]]s. It was first published by [[Karl Pearson]] in 1895 and subsequently extended by him in 1901 and 1916 in a series of articles on [[biostatistics]]. | |
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| == History ==
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| The Pearson system was originally devised in an effort to model visibly [[skewness|skew]]ed observations. It was well known at the time how to adjust a theoretical model to fit the first two [[cumulant]]s or [[moment (mathematics)|moment]]s of observed data: Any [[probability distribution]] can be extended straightforwardly to form a [[location-scale family]]. Except in [[pathological (mathematics)|pathological]] cases, a location-scale family can be made to fit the observed [[mean (mathematics)|mean]] (first cumulant) and [[variance]] (second cumulant) arbitrarily well. However, it was not known how to construct probability distributions in which the [[skewness]] (standardized third cumulant) and [[kurtosis]] (standardized fourth cumulant) could be adjusted equally freely. This need became apparent when trying to fit known theoretical models to observed data that exhibited skewness. Pearson's examples include survival data, which are usually asymmetric.
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| In his original paper, Pearson (1895, p. 360) identified four types of distributions (numbered I through IV) in addition to the [[normal distribution]] (which was originally known as type V). The classification depended on whether the distributions were [[support (mathematics)|support]]ed on a bounded interval, on a half-line, or on the whole [[real line]]; and whether they were potentially skewed or necessarily symmetric. A second paper (Pearson 1901) fixed two omissions: it redefined the type V distribution (originally just the [[normal distribution]], but now the [[inverse-gamma distribution]]) and introduced the type VI distribution. Together the first two papers cover the five main types of the Pearson system (I, III, VI, V, and IV). In a third paper, Pearson (1916) introduced further special cases and subtypes (VII through XII).
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| Rhind (1909, pp. 430–432) devised a simple way of visualizing the parameter space of the Pearson system, which was subsequently adopted by Pearson (1916, plate 1 and pp. 430ff., 448ff.). The Pearson types are characterized by two quantities, commonly referred to as β<sub>1</sub> and β<sub>2</sub>. The first is the square of the [[skewness]]: <math>\beta_1 = \gamma_1^2</math> where γ<sub>1</sub> is the skewness, or third [[standardized moment]]. The second is the traditional [[kurtosis]], or fourth standardized moment: β<sub>2</sub> = γ<sub>2</sub> + 3. (Modern treatments define kurtosis γ<sub>2</sub> in terms of cumulants instead of moments, so that for a normal distribution we have γ<sub>2</sub> = 0 and β<sub>2</sub> = 3. Here we follow the historical precedent and use β<sub>2</sub>.) The diagram on the right shows which Pearson type a given concrete distribution (identified by a point (β<sub>1</sub>, β<sub>2</sub>)) belongs to.
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| Many of the skewed and/or non-[[mesokurtic]] distributions familiar to us today were still unknown in the early 1890s. What is now known as the [[beta distribution]] had been used by [[Thomas Bayes]] as a [[posterior distribution]] of the parameter of a [[Bernoulli distribution]] in his 1763 work on [[inverse probability]]. The Beta distribution gained prominence due to its membership in Pearson's system and was known until the 1940s as the Pearson type I distribution.
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| <ref>
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| {{cite web
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| | url = http://jeff560.tripod.com/b.html
| |
| | title = Beta distribution
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| | accessdate = December 9, 2006
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| | last = Miller
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| | first = Jeff
| |
| | coauthors = et al.
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| | date = 2006-07-09
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| | work = [http://jeff560.tripod.com/mathword.html ''Earliest Known Uses of Some of the Words of Mathematics'']
| |
| }}
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| </ref>
| |
| (Pearson's type II distribution is a special case of type I, but is usually no longer singled out.) The [[gamma distribution]] originated from Pearson's work (Pearson 1893, p. 331; Pearson 1895, pp. 357, 360, 373–376) and was known as the Pearson type III distribution, before acquiring its modern name in the 1930s and 1940s.
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| <ref>
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| {{cite web
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| | url = http://jeff560.tripod.com/g.html
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| | title = Gamma distribution
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| | accessdate = December 9, 2006
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| | last = Miller
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| | first = Jeff
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| | coauthors = et al.
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| | date = 2006-12-07
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| | work = [http://jeff560.tripod.com/mathword.html ''Earliest Known Uses of Some of the Words of Mathematics'']
| |
| }}</ref> Pearson's 1895 paper introduced the type IV distribution, which contains [[Student's t-distribution|Student's ''t''-distribution]] as a special case, predating [[William Sealy Gosset]]'s subsequent use by several years. His 1901 paper introduced the [[inverse-gamma distribution]] (type V) and the [[beta prime distribution]] (type VI).
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| == Definition ==
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| A Pearson [[probability density function|density]] ''p'' is defined to be any valid solution to the [[differential equation]] (cf. Pearson 1895, p. 381)
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| :<math>\frac{p'(x)}{p(x)} + \frac{a+x-\lambda}{b_2 (x-\lambda)^2 + b_1 (x-\lambda) + b_0} = 0. \qquad (1) \!</math>
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| with :
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| : <math> b_0=\frac{4 \beta_2-3 \beta_1}{10 \beta_2 -12\beta_1 -18} \mu_2 ,</math>
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| : <math> a=b_1=\sqrt{\mu_2} \sqrt{\beta_1}\frac{\beta_2+3}{10 \beta_2-12\beta_1 -18},</math>
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| : <math>b_2=\frac{2 \beta_2-3 \beta_1 -6}{10 \beta_2-12\beta_1 -18} .</math>
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| According to Ord,<ref>Ord J.K. (1972) p2</ref> Pearson devised the underlying form of Equation (1) on the basis of, firstly, the formula for the derivative of the logarithm of the density function of the [[normal distribution]] (which gives a linear function) and, secondly, from a recurrence relation for values in the [[probability mass function]] of the [[hypergeometric distribution]] (which yields the linear-divided-by-quadratic structure).
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| In Equation (1), the parameter ''a'' determines a [[stationary point]], and hence under some conditions a [[mode (statistics)|mode]] of the distribution, since
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| :<math>p'(\lambda-a) = 0 \!</math>
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| follows directly from the differential equation.
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| Since we are confronted with a [[linear differential equation#First order equation|first order linear differential equation with variable coefficients]], its solution is straightforward:
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| :<math>p(x) \propto \exp\left( -\!\int\!\!\frac{x-a}{b_2 x^2 + b_1 x + b_0} \,\mathrm{d}x \right).</math>
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| The integral in this solution simplifies considerably when certain special cases of the integrand are considered. Pearson (1895, p. 367) distinguished two main cases, determined by the sign of the [[discriminant]] (and hence the number of real [[Root of a function|root]]s) of the [[quadratic function]]
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| :<math>f(x) = b_2\,x^2 + b_1\,x + b_0. \qquad (2)\!</math>
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| ==Particular types of distribution==
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| === Case 1, negative discriminant: The Pearson type IV distribution ===
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| If the discriminant of the quadratic function (2) is negative (<math>b_1^2 - 4 b_2 b_0 < 0</math>), it has no real roots. Then define
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| :<math>y = x + \frac{b_1}{2\,b_2} \!</math> and
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| :<math>\alpha = \frac{\sqrt{4\,b_2\,b_0 - b_1^2\,}}{2\,b_2}. \!</math>
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| Observe that α is a well-defined real number and α ≠ 0, because by assumption <math>4 b_2 b_0 - b_1^2 > 0</math> and therefore ''b''<sub>2</sub> ≠ 0. Applying these substitutions, the quadratic function (2) is transformed into
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| :<math>f(x) = b_2\,(y^2 + \alpha^2). \!</math>
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| <!--NB: ^^^ don't change this to y, f is still a function of x.-->
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| The absence of real roots is obvious from this formulation, because α<sup>2</sup> is necessarily positive.
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| We now express the solution to the differential equation (1) as a function of ''y'':
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| :<math>p(y) \propto \exp\left(- \frac{1}{b_2}\, \int\frac{y - \frac{b_1}{2\,b_2} - a}{y^2 + \alpha^2} \,\mathrm{d}y \right). \!</math>
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| Pearson (1895, p. 362) called this the "trigonometrical<!--sic--> case", because the integral
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| :<math>\int\frac{y-\frac{2\,b_2\,a + b_1}{2\,b_2}}{y^2 + \alpha^2} \,\mathrm{d}y = \frac{1}{2} \ln(y^2 + \alpha^2) - \frac{2\,b_2\,a + b_1}{2\,b_2\,\alpha}\arctan\left(\frac{y}{\alpha}\right) + C_0</math>
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| involves the [[inverse trigonometric function|inverse]] [[trigonometric function|trigonometric]] arctan function. Then
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| :<math>p(y) \propto \exp\left[ -\frac{1}{2\,b_2} \ln\!\left(1+\frac{y^2}{\alpha^2}\right) -\frac{\ln\alpha}{b_2} +\frac{2\,b_2\,a + b_1}{2\,b_2^2\,\alpha} \arctan\left(\frac{y}{\alpha}\right) + C_1 \right]</math>
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| Finally, let
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| :<math>m = \frac{1}{2\,b_2} \!</math> and
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| :<math>\nu = -\frac{2\,b_2\,a + b_1}{2\,b_2^2\,\alpha} \!</math> | |
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| Applying these substitutions, we obtain the parametric function:
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| :<math>p(y) \propto \left[1 + \frac{y^2}{\alpha^2}\right]^{-m} \exp\left[-\nu \arctan\left(\frac{y}{\alpha}\right)\right]</math>
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| This unnormalized density has [[support (mathematics)|support]] on the entire [[real line]]. It depends on a [[scale parameter]] α > 0 and [[shape parameter]]s ''m'' > 1/2 and ν. One parameter was lost when we chose to find the solution to the differential equation (1) as a function of ''y'' rather than ''x''. We therefore reintroduce a fourth parameter, namely the [[location parameter]] ''λ''. We have thus derived the density of the '''Pearson type IV distribution''':
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| :<math>p(x) = \frac{\left|\frac{\Gamma\!\left(m+\frac{\nu}{2}i\right)}{\Gamma(m)}\right|^2}{\alpha\,\mathrm{\Beta}\!\left(m-\frac12, \frac12\right)}
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| \left[1 + \left(\frac{x-\lambda}{\alpha}\right)^{\!2\,} \right]^{-m} \exp\left[-\nu \arctan\left(\frac{x-\lambda}{\alpha}\right)\right]. </math>
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| The [[normalizing constant]] involves the [[complex function|complex]] [[Gamma function]] (Γ) and the [[Beta function]] (B).
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| ==== The Pearson type VII distribution ====
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| [[Image:Pearson type VII distribution PDF.png|300px|thumb|Plot of Pearson type VII densities with λ = 0, σ = 1, and: γ<sub>2</sub> = ∞ (red); γ<sub>2</sub> = 4 (blue); and γ<sub>2</sub> = 0 (black)]]
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| The shape parameter ''ν'' of the Pearson type IV distribution controls its [[skewness]]. If we fix its value at zero, we obtain a symmetric three-parameter family. This special case is known as the '''Pearson type VII distribution''' (cf. Pearson 1916, p. 450). Its density is
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| :<math>p(x) = \frac{1}{\alpha\,\mathrm{\Beta}\!\left(m-\frac12, \frac12\right)} \left[1 + \left(\frac{x-\lambda}{\alpha}\right)^{\!2\,} \right]^{-m},</math>
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| where B is the [[Beta function]].
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| An alternative parameterization (and slight specialization) of the type VII distribution is obtained by letting
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| :<math>\alpha = \sigma\,\sqrt{2\,m-3}, \!</math>
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| which requires ''m'' > 3/2. This entails a minor loss of generality but ensures that the [[variance]] of the distribution exists and is equal to σ<sup>2</sup>. Now the parameter ''m'' only controls the [[kurtosis]] of the distribution. If ''m'' approaches infinity as ''λ'' and ''σ'' are held constant, the [[normal distribution]] arises as a special case: | |
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| :<math>\lim_{m\to\infty}\frac{1}{\sigma\,\sqrt{2\,m-3}\,\mathrm{\Beta}\!\left(m-\frac12, \frac12\right)} \left[1 + \left(\frac{x-\lambda}{\sigma\,\sqrt{2\,m-3}}\right)^{\!2\,} \right]^{-m}</math>
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| :<math>= \frac{1}{\sigma\,\sqrt{2}\,\Gamma\!\left(\frac12\right)} \times \lim_{m\to\infty} \frac{\Gamma(m)}{\Gamma\!\left(m-\frac12\right) \sqrt{m-\frac32}} \times \lim_{m\to\infty} \left[1 + \frac{\left(\frac{x-\lambda}{\sigma}\right)^2}{2\,m-3} \right]^{-m}</math>
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| :<math>= \frac{1}{\sigma\sqrt{2\,\pi}} \times 1 \times \exp\!\left[-\frac12 \left(\frac{x-\lambda}{\sigma}\right)^{\!2\,} \right] </math>
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| This is the density of a normal distribution with mean ''λ'' and standard deviation ''σ''.
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| It is convenient to require that ''m'' > 5/2 and to let
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| :<math>m = \frac52 + \frac{3}{\gamma_2}. \!</math>
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| This is another specialization, and it guarantees that the first four moments of the distribution exist. More specifically, the Pearson type VII distribution parameterized in terms of (λ, σ, γ<sub>2</sub>) has a mean of ''λ'', [[standard deviation]] of ''σ'', [[skewness]] of zero, and [[excess kurtosis]] of γ<sub>2</sub>.
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| ==== Student's ''t''-distribution ====
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| The Pearson type VII distribution is equivalent to the non-standardized [[Student's t-distribution|Student's ''t''-distribution]] with parameters ν > 0, μ, σ<sup>2</sup> by applying the following substitutions to its original parameterization:
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| :<math>\lambda = \mu, \!</math> | |
| :<math>\alpha = \sqrt{\nu\sigma^2}, \!</math> and
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| :<math>m = \frac{\nu+1}{2}, \!</math>
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| Observe that the constraint ''m'' > 1/2 is satisfied.
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| The resulting density is
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| :<math>p(x|\mu,\sigma^2,\nu) = \frac{1}{\sqrt{\nu\sigma^2}\,\mathrm{\Beta}\!\left(\frac{\nu}{2}, \frac12\right)} \left(1+\frac{1}{\nu}\frac{(x-\mu)^2}{\sigma^2}\right)^{-\frac{\nu+1}{2}}, </math>
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| which is easily recognized as the density of a Student's ''t''-distribution.
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| Note also that this implies that the Pearson type VII distribution subsumes the standard [[Student's t-distribution|Student's ''t''-distribution]] and also the standard [[Cauchy distribution]]. In particular, the standard Student's ''t''-distribution arises as a subcase, when μ = 0 and σ<sup>2</sup> = 1, equivalent to the following substitutitons:
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| :<math>\lambda = 0, \!</math>
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| :<math>\alpha = \sqrt{\nu}, \!</math> and
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| :<math>m = \frac{\nu+1}{2}, \!</math>
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| The density of this restricted one-parameter family is a standard Student's ''t'':
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| :<math>p(x) = \frac{1}{\sqrt{\nu}\,\mathrm{\Beta}\!\left(\frac{\nu}{2}, \frac12\right)} \left(1 + \frac{x^2}{\nu} \right)^{-\frac{\nu+1}{2}},</math>
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| === Case 2, non-negative discriminant ===
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| If the quadratic function (2) has a non-negative discriminant (<math>b_1^2 - 4 b_2 b_0 \geq 0</math>), it has real roots ''a''<sub>1</sub> and ''a''<sub>2</sub> (not necessarily distinct):
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| :<math>a_1 = \frac{-b_1 - \sqrt{b_1^2 - 4 b_2 b_0}}{2 b_2}, \!</math>
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| :<math>a_2 = \frac{-b_1 + \sqrt{b_1^2 - 4 b_2 b_0}}{2 b_2}, \!</math>
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| In the presence of real roots the quadratic function (2) can be written as
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| :<math>f(x) = b_2\,(x-a_1)(x-a_2), \!</math>
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| and the solution to the differential equation is therefore
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| :<math>p(x) \propto \exp\left( -\frac{1}{b_2} \int\!\!\frac{x-a}{(x - a_1) (x - a_2)} \,\mathrm{d}x \right). \!</math>
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| Pearson (1895, p. 362) called this the "logarithmic case", because the integral
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| :<math>\int\!\!\frac{x-a}{(x - a_1) (x - a_2)} \,\mathrm{d}x = \frac{(a_1-a)\ln(x-a_1) - (a_2-a)\ln(x-a_2)}{a_1-a_2} + C</math>
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| involves only the [[logarithm]] function, and not the arctan function as in the previous case.
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| Using the substitution
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| :<math>\nu = \frac{1}{b_2\,(a_1-a_2)} \!</math>
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| we obtain the following solution to the differential equation (1):
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| :<math>p(x) \propto (x-a_1)^{-\nu (a_1-a)} (x-a_2)^{\nu (a_2-a)}.</math>
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| Since this density is only known up to a hidden constant of proportionality, that constant can be changed and the density written as follows:
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| :<math>p(x) \propto \left(1-\frac{x}{a_1}\right)^{-\nu (a_1-a)} \left(1-\frac{x}{a_2}\right)^{ \nu (a_2-a)}</math>
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| ==== The Pearson type I distribution ====
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| The '''Pearson type I distribution''' (a generalization of the [[beta distribution]]) arises when the roots of the quadratic equation (2) are of opposite sign, that is, <math>a_1 < 0 < a_2</math>. Then the solution ''p'' is supported on the interval <math>(a_1, a_2)</math>. Apply the substitution
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| :<math>x = a_1 + y (a_2 - a_1) \qquad \mbox{where}\ 0<y<1, \!</math>
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| which yields a solution in terms of ''y'' that is supported on the interval (0, 1):
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| :<math>p(y) \propto \left(\frac{a_1-a_2}{a_1}\;y\right)^{(-a_1+a)\nu} \left(\frac{a_2-a_1}{a_2}\;(1-y)\right)^{(a_2-a)\nu}.</math>
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| One may define:
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| :<math> m_1=\frac{a-a_1}{b_2 (a_1-a_2)} \!</math>
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| :<math> m_2=\frac{a-a_2}{b_2 (a_2-a_1)}\!</math>
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| Regrouping constants and parameters, this simplifies to:
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| :<math>p(y) \propto y^{m_1} (1-y)^{m_2}, \!</math>
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| Thus <math>\frac{x-\lambda-a_1}{a_2-a_1}</math> follows a <math>\Beta(m_1+1,m_2+1)</math> with <math>\lambda=\mu_1-(a_2-a_1) \frac{m_1+1}{m_1+m_2+2}-a_1</math>
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| It turns out that ''m''<sub>1</sub>, ''m''<sub>2</sub> > −1 is necessary and sufficient for ''p'' to be a proper probability density function.
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| ==== The Pearson type II distribution ====
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| The '''Pearson type II distribution''' is a special case of the Pearson type I family restricted to symmetric distributions.
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| For the Pearson Type II Curve,<ref>{{cite web | url = http://links.jstor.org/sici?sici=0362-9791(198923)14%3A3%3C245%3ACVFSRO%3E2.0.CO%3B2-L | title = Critical Values for Spearman's Rank Order Correlation | accessdate = August 22, 2007| last = Ramsey| first = Philip H.| date = 1989-09-01}}</ref>
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| :<math>y = y_{0}\left(1-\frac{x^2}{a^2}\right)^m</math>
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| where
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| :<math>x = \sum d^2/2 -(n^3-n)/12</math>
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| the ordinate, ''y'', is the frequency of <math>\sum d^2</math>. The Pearson Type II Curve is used in computing the table of significant correlation coefficients for [[Spearman's rank correlation coefficient]] when the number of items in a series is less than 100 (or 30, depending on some sources). After that, the distribution mimics a standard [[Student's t-distribution]]. For the table of values, certain values are used as the constants in the previous equation:
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| :<math>m = \frac{5\beta_{2}-9}{2(3-\beta_{2})}</math>
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| :<math>a^2 = \frac{2\mu_{2}\beta_{2}}{3-\beta_{2}}</math>
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| :<math>y_{0} = \frac{N[\Gamma(2m+2)]}{a[2^{2m+1}][\Gamma(m+1)]}</math>
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| The moments of ''x'' used are
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| :<math>\mu_{2} = (n-1)[(n^2+n)/12]^2</math>
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| :<math>\beta_{2}=\frac{3(25n^4-13n^3-73n^2+37n+72)}{25n(n+1)^2(n-1)}</math>
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| ==== The Pearson type III distribution ====
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| :<math>\lambda= \mu_1 + \frac{b_0}{b_1} - (m+1) b_1\!</math>
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| :<math>b_0+b_1 (x-\lambda)\!</math> is <math>\mathrm{Gamma}(m+1,b_1^2)\!</math>
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| The Pearson type III distribution is a [[gamma distribution]] or [[chi-squared distribution]].
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| ==== The Pearson type V distribution ====
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| Defining new parameters:
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| :<math> C_1=\frac{b_1}{2 b_2}\!</math>
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| :<math>\lambda=\mu_1-\frac{a-C_1} {1-2 b_2}\!</math>
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| :<math>x-\lambda\!</math> follows an <math>\operatorname{InverseGamma}(\frac{1}{b_2}-1,\frac{a-C_1}{b_2})\!</math>
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| The Pearson type V distribution is an [[inverse-gamma distribution]].
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| ==== The Pearson type VI distribution ====
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| :<math>\lambda=\mu_1 + (a_2-a_1) \frac{m_2+1}{m_2+m_1+2} - a_2\!</math>
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| :<math>\frac{x-\lambda-a_2}{a_2-a_1}\!</math> follows a :<math>\beta^{\prime}(m_2+1,-m_2-m_1-1)\!</math>
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| The Pearson type VI distribution is a [[beta prime distribution]] or [[F-distribution|''F''-distribution]].
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| == Relation to other distributions ==
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| The Pearson family subsumes the following distributions, among others:
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| * [[beta distribution]] (type I)
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| * [[beta prime distribution]] (type VI)
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| * [[Cauchy distribution]] (type IV)
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| * [[chi-squared distribution]] (type III)
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| * [[uniform distribution (continuous)|continuous uniform distribution]] (limit of type I)
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| * [[exponential distribution]] (type III)
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| * [[gamma distribution]] (type III)
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| * [[F-distribution|''F''-distribution]] (type VI)
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| * [[inverse-chi-squared distribution]] (type V)
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| * [[inverse-gamma distribution]] (type V)
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| * [[normal distribution]] (limit of type I, III, IV, V, or VI)
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| * [[Student's t-distribution|Student's ''t''-distribution]] (type VII, which is the non-skewed subtype of type IV)
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| == Applications ==
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| These models are used in financial markets, given their ability to be parametrised in a way that has intuitive meaning for market traders. A number of models are in current use that capture the stochastic nature of the volatility of rates, stocks etc. and this family of distributions may prove to be one of the more important.
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| In the United States, the Log-Pearson III is the default distribution for flood frequency analysis.
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| ==Notes==
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| <references/>
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| | |
| == Sources ==
| |
| === Primary sources ===
| |
| *{{cite journal
| |
| | last = Pearson
| |
| | first = Karl
| |
| | authorlink = Karl Pearson
| |
| | year = 1893
| |
| | month =
| |
| | title = Contributions to the mathematical theory of evolution [abstract]
| |
| | journal = Proceedings of the Royal Society
| |
| | volume = 54
| |
| | issue =326–330
| |
| | pages = 329–333
| |
| | doi = 10.1098/rspl.1893.0079
| |
| | id =
| |
| | jstor=115538}}
| |
| | |
| *{{cite journal
| |
| | last = Pearson
| |
| | first = Karl
| |
| | authorlink = Karl Pearson
| |
| | year = 1895
| |
| | month =
| |
| | title = Contributions to the mathematical theory of evolution, II: Skew variation in homogeneous material
| |
| | journal = Philosophical Transactions of the Royal Society
| |
| | volume = 186
| |
| | issue =
| |
| | pages = 343–414
| |
| | doi = 10.1098/rsta.1895.0010
| |
| | id =
| |
| | jstor=90649
| |
| | bibcode=1895RSPTA.186..343P}}
| |
| | |
| *{{cite journal
| |
| | last = Pearson
| |
| | first = Karl
| |
| | authorlink = Karl Pearson
| |
| | year = 1901
| |
| | month =
| |
| | title = Mathematical contributions to the theory of evolution, X: Supplement to a memoir on skew variation
| |
| | journal = Philosophical Transactions of the Royal Society A
| |
| | volume = 197
| |
| | issue =287–299
| |
| | pages = 443–459
| |
| | doi = 10.1098/rsta.1901.0023
| |
| | id =
| |
| | jstor=90841|bibcode = 1901RSPTA.197..443P }}
| |
| | |
| *{{cite journal
| |
| | last = Pearson
| |
| | first = Karl
| |
| | authorlink = Karl Pearson
| |
| | year = 1916
| |
| | month =
| |
| | title = Mathematical contributions to the theory of evolution, XIX: Second supplement to a memoir on skew variation
| |
| | journal = Philosophical Transactions of the Royal Society A
| |
| | volume = 216
| |
| | issue =538–548
| |
| | pages = 429–457
| |
| | doi = 10.1098/rsta.1916.0009
| |
| | id =
| |
| | jstor=91092|bibcode = 1916RSPTA.216..429P }}
| |
| | |
| *{{cite journal
| |
| | last = Rhind
| |
| | first = A.
| |
| | authorlink =
| |
| |date=July–October 1909
| |
| | title = Tables to facilitate the computation of the probable errors of the chief constants of skew frequency distributions
| |
| | journal = Biometrika
| |
| | volume = 7
| |
| | issue = 1/2
| |
| | pages = 127–147
| |
| | doi =
| |
| | id =
| |
| | jstor=2345367}}
| |
| | |
| === Secondary sources ===
| |
| | |
| * Milton Abramowitz and Irene A. Stegun (1964). ''[[Abramowitz and Stegun|Handbook of Mathematical Functions]] with Formulas, Graphs, and Mathematical Tables''. [[National Bureau of Standards]].
| |
| | |
| *[[Eric W. Weisstein]] et al. [http://mathworld.wolfram.com/PearsonTypeIIIDistribution.html Pearson Type III Distribution]. From [[MathWorld]].
| |
| | |
| === References ===
| |
| *Elderton, Sir W.P, Johnson, N.L. (1969) ''Systems of Frequency Curves''. Cambridge University Press.
| |
| *Ord J.K. (1972) ''Families of Frequency Distributions''. Griffin, London.
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| | |
| {{ProbDistributions|families}}
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| {{DEFAULTSORT:Pearson Distribution}}
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| [[Category:Continuous distributions]]
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| [[Category:Systems of probability distributions]]
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| [[Category:Probability distributions]]
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