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In statistics, the folded-t and half-t distributions are derived from
.
Definitions
The folded non-standardized t distribution is the distribution of the absolute value of the non-standardized t distribution with
ν
{\displaystyle \nu }
degrees of freedom; its probability density function is given by:[citation needed ]
g
(
x
)
=
Γ
(
ν
+
1
2
)
Γ
(
ν
2
)
ν
π
σ
2
{
[
1
+
1
ν
(
x
−
μ
)
2
σ
2
]
−
ν
+
1
2
+
[
1
+
1
ν
(
x
+
μ
)
2
σ
2
]
−
ν
+
1
2
}
(
for
x
≥
0
)
{\displaystyle g\left(x\right)\;=\;{\frac {\Gamma \left({\frac {\nu +1}{2}}\right)}{\Gamma \left({\frac {\nu }{2}}\right){\sqrt {\nu \pi \sigma ^{2}}}}}\left\lbrace \left[1+{\frac {1}{\nu }}{\frac {\left(x-\mu \right)^{2}}{\sigma ^{2}}}\right]^{-{\frac {\nu +1}{2}}}+\left[1+{\frac {1}{\nu }}{\frac {\left(x+\mu \right)^{2}}{\sigma ^{2}}}\right]^{-{\frac {\nu +1}{2}}}\right\rbrace \qquad ({\mbox{for}}\quad x\geq 0)}
.
The half-t distribution results as the special case of
μ
=
0
{\displaystyle \mu =0}
, and the standardized version as the special case of
σ
=
1
{\displaystyle \sigma =1}
.
If
μ
=
0
{\displaystyle \mu =0}
, the folded-t distribution reduces to the special case of the half-t distribution. Its probability density function then simplifies to
g
(
x
)
=
2
Γ
(
ν
+
1
2
)
Γ
(
ν
2
)
ν
π
σ
2
(
1
+
1
ν
x
2
σ
2
)
−
ν
+
1
2
(
for
x
≥
0
)
{\displaystyle g\left(x\right)\;=\;{\frac {2\;\Gamma \left({\frac {\nu +1}{2}}\right)}{\Gamma \left({\frac {\nu }{2}}\right){\sqrt {\nu \pi \sigma ^{2}}}}}\left(1+{\frac {1}{\nu }}{\frac {x^{2}}{\sigma ^{2}}}\right)^{-{\frac {\nu +1}{2}}}\qquad ({\mbox{for}}\quad x\geq 0)}
.
The half-t distribution's first two moments (expectation and variance ) are given by:[1]
E
[
X
]
=
2
σ
ν
π
Γ
(
ν
+
1
2
)
Γ
(
ν
2
)
(
ν
−
1
)
for
ν
>
1
{\displaystyle \operatorname {E} [X]\;=\;2\sigma {\sqrt {\frac {\nu }{\pi }}}{\frac {\Gamma ({\frac {\nu +1}{2}})}{\Gamma ({\frac {\nu }{2}})\,(\nu -1)}}\qquad {\mbox{for}}\quad \nu >1}
,
and
Var
(
X
)
=
σ
2
(
ν
ν
−
2
−
4
ν
π
(
ν
−
1
)
2
(
Γ
(
ν
+
1
2
)
Γ
(
ν
2
)
)
2
)
for
ν
>
2
{\displaystyle \operatorname {Var} (X)\;=\;\sigma ^{2}\left({\frac {\nu }{\nu -2}}-{\frac {4\nu }{\pi (\nu -1)^{2}}}\left({\frac {\Gamma ({\frac {\nu +1}{2}})}{\Gamma ({\frac {\nu }{2}})}}\right)^{2}\right)\qquad {\mbox{for}}\quad \nu >2}
.
Relation to other distributions
Folded-t and half-t generalize the
Student-t distribution
with one degree of freedom, the families of folded and half-t distributions include the folded Cauchy distribution and half-Cauchy distributions for
ν
=
1
{\displaystyle \nu =1}
.
See also
References
Further reading
Psarakis, S.; Panaretos, J. (1990). "The folded t distribution". Communications in Statistics - Theory and Methods . 19 (7): 2717–2734. .
Gelman, A. (2006). "Prior distributions for variance parameters in hierarchical models" . Bayesian Analysis . 1 (3): 515–534. .
Röver, C.; Bender, R.; Dias, S.; Schmid, C.H.; Schmidli, H.; Sturtz, S.; Weber, S.; Friede, T. (2021), "On weakly informative prior distributions for the heterogeneity parameter in Bayesian random‐effects meta‐analysis", Research Synthesis Methods , 12 (4): 448–474,
Wiper, M. P.; Girón, F. J.; Pewsey, Arthur (2008). "Objective Bayesian Inference for the Half-Normal and Half-t Distributions". Communications in Statistics - Theory and Methods . 37 (20): 3165–3185. .
Tancredi, A. (2002). "Accounting for heavy tails in stochastic frontier models" . Working paper (7325). Università degli Studi di Padova.
External links
Functions to evaluate half-t distributions are available in several R packages, e.g. [1] [2] [3] .
Discrete univariate
with finite support with infinite support
Continuous univariate
supported on a bounded interval supported on a semi-infinite interval supported on the whole real line with support whose type varies
Mixed univariate
Multivariate (joint) Directional Degenerate and singular Families