Arcsine distribution

In probability theory, the arcsine distribution is the probability distribution whose cumulative distribution function involves the arcsine and the square root:

${\displaystyle F(x)={\frac {2}{\pi }}\arcsin \left({\sqrt {x}}\right)={\frac {\arcsin(2x-1)}{\pi }}+{\frac {1}{2}}}${\displaystyle F(x)={\frac {2}{\pi }}\arcsin \left({\sqrt {x}}\right)={\frac {\arcsin(2x-1)}{\pi }}+{\frac {1}{2}}}

for 0 ≤ x ≤ 1, and whose probability density function is

${\displaystyle f(x)={\frac {1}{\pi {\sqrt {x(1-x)}}}}}${\displaystyle f(x)={\frac {1}{\pi {\sqrt {x(1-x)}}}}}

on (0, 1). The standard arcsine distribution is a special case of the beta distribution with α = β = 1/2. That is, if ${\displaystyle X}${\displaystyle X} is an arcsine-distributed random variable, then ${\displaystyle X\sim {\rm {Beta}}{\bigl (}{\tfrac {1}{2}},{\tfrac {1}{2}}{\bigr )}}${\displaystyle X\sim {\rm {Beta}}{\bigl (}{\tfrac {1}{2}},{\tfrac {1}{2}}{\bigr )}}. By extension, the arcsine distribution is a special case of the Pearson type I distribution.

The arcsine distribution appears in the Lévy arcsine law, in the Erdős arcsine law, and as the Jeffreys prior for the probability of success of a Bernoulli trial.^{[1] [2]} The arcsine probability density is a distribution that appears in several random-walk fundamental theorems. In a fair coin toss random walk, the probability for the time of the last visit to the origin is distributed as an (U-shaped) arcsine distribution.^{[3] [4]} In a two-player fair-coin-toss game, a player is said to be in the lead if the random walk (that started at the origin) is above the origin. The most probable number of times that a given player will be in the lead, in a game of length 2N, is not N. On the contrary, N is the least likely number of times that the player will be in the lead. The most likely number of times in the lead is 0 or 2N (following the arcsine distribution).

The distribution can be expanded to include any bounded support from a ≤ x ≤ b by a simple transformation

${\displaystyle F(x)={\frac {2}{\pi }}\arcsin \left({\sqrt {\frac {x-a}{b-a}}}\right)}${\displaystyle F(x)={\frac {2}{\pi }}\arcsin \left({\sqrt {\frac {x-a}{b-a}}}\right)}

for a ≤ x ≤ b, and whose probability density function is

${\displaystyle f(x)={\frac {1}{\pi {\sqrt {(x-a)(b-x)}}}}}${\displaystyle f(x)={\frac {1}{\pi {\sqrt {(x-a)(b-x)}}}}}

on (a, b).

The generalized standard arcsine distribution on (0,1) with probability density function

${\displaystyle f(x;\alpha )={\frac {\sin \pi \alpha }{\pi }}x^{-\alpha }(1-x)^{\alpha -1}}${\displaystyle f(x;\alpha )={\frac {\sin \pi \alpha }{\pi }}x^{-\alpha }(1-x)^{\alpha -1}}

is also a special case of the beta distribution with parameters ${\displaystyle {\rm {Beta}}(1-\alpha ,\alpha )}${\displaystyle {\rm {Beta}}(1-\alpha ,\alpha )}.

Note that when ${\displaystyle \alpha ={\tfrac {1}{2}}}${\displaystyle \alpha ={\tfrac {1}{2}}} the general arcsine distribution reduces to the standard distribution listed above.

• Arcsine distribution is closed under translation and scaling by a positive factor
  • If ${\displaystyle X\sim {\rm {Arcsine}}(a,b)\ {\text{then }}kX+c\sim {\rm {Arcsine}}(ak+c,bk+c)}${\displaystyle X\sim {\rm {Arcsine}}(a,b)\ {\text{then }}kX+c\sim {\rm {Arcsine}}(ak+c,bk+c)}
• The square of an arcsine distribution over (-1, 1) has arcsine distribution over (0, 1)
  • If ${\displaystyle X\sim {\rm {Arcsine}}(-1,1)\ {\text{then }}X^{2}\sim {\rm {Arcsine}}(0,1)}${\displaystyle X\sim {\rm {Arcsine}}(-1,1)\ {\text{then }}X^{2}\sim {\rm {Arcsine}}(0,1)}
• The coordinates of points uniformly selected on a circle of radius ${\displaystyle r}${\displaystyle r} centered at the origin (0, 0), have an ${\displaystyle {\rm {Arcsine}}(-r,r)}${\displaystyle {\rm {Arcsine}}(-r,r)} distribution
  • For example, if we select a point uniformly on the circumference, ${\displaystyle U\sim {\rm {Uniform}}(0,2\pi r)}${\displaystyle U\sim {\rm {Uniform}}(0,2\pi r)}, we have that the point's x coordinate distribution is ${\displaystyle r\cdot \cos(U)\sim {\rm {Arcsine}}(-r,r)}${\displaystyle r\cdot \cos(U)\sim {\rm {Arcsine}}(-r,r)}, and its y coordinate distribution is ${\textstyle r\cdot \sin(U)\sim {\rm {Arcsine}}(-r,r)}${\textstyle r\cdot \sin(U)\sim {\rm {Arcsine}}(-r,r)}

The characteristic function of the generalized arcsine distribution is a zero order Bessel function of the first kind, multiplied by a complex exponential, given by ${\displaystyle e^{it{\frac {b+a}{2}}}J_{0}({\frac {b-a}{2}}t)}${\displaystyle e^{it{\frac {b+a}{2}}}J_{0}({\frac {b-a}{2}}t)}. For the special case of ${\displaystyle b=-a}${\displaystyle b=-a}, the characteristic function takes the form of ${\displaystyle J_{0}(bt)}${\displaystyle J_{0}(bt)}.

• If U and V are i.i.d uniform (−π,π) random variables, then ${\displaystyle \sin(U)}${\displaystyle \sin(U)}, ${\displaystyle \sin(2U)}${\displaystyle \sin(2U)}, ${\displaystyle -\cos(2U)}${\displaystyle -\cos(2U)}, ${\displaystyle \sin(U+V)}${\displaystyle \sin(U+V)} and ${\displaystyle \sin(U-V)}${\displaystyle \sin(U-V)} all have an ${\displaystyle {\rm {Arcsine}}(-1,1)}${\displaystyle {\rm {Arcsine}}(-1,1)} distribution.
• If ${\displaystyle X}${\displaystyle X} is the generalized arcsine distribution with shape parameter ${\displaystyle \alpha }${\displaystyle \alpha } supported on the finite interval [a,b] then ${\displaystyle {\frac {X-a}{b-a}}\sim {\rm {Beta}}(1-\alpha ,\alpha )\ }${\displaystyle {\frac {X-a}{b-a}}\sim {\rm {Beta}}(1-\alpha ,\alpha )\ }
• If X ~ Cauchy(0, 1) then ${\displaystyle {\tfrac {1}{1+X^{2}}}}${\displaystyle {\tfrac {1}{1+X^{2}}}} has a standard arcsine distribution

• Rogozin, B.A. (2001) [1994], "Arcsine distribution", Encyclopedia of Mathematics , EMS Press

• Continuous distributions

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• Short description matches Wikidata