Result in probability theory
In probability theory , Lévy’s continuity theorem , or Lévy's convergence theorem , named after the French mathematician Paul Lévy , connects convergence in distribution of the sequence of random variables with pointwise convergence of their characteristic functions .
This theorem is the basis for one approach to prove the central limit theorem and is one of the major theorems concerning characteristic functions.
Statement
Suppose we have
a sequence of random variables
{
X
n
}
n
=
1
∞
{\textstyle \{X_{n}\}_{n=1}^{\infty }}
, not necessarily sharing a common probability space , the sequence of corresponding characteristic functions
{
φ
n
}
n
=
1
∞
{\textstyle \{\varphi _{n}\}_{n=1}^{\infty }}
, which by definition are
φ
n
(
t
)
=
E
[
e
i
t
X
n
]
∀
t
∈
R
,
∀
n
∈
N
,
{\displaystyle \varphi _{n}(t)=\operatorname {E} \left\quad \forall t\in \mathbb {R} ,\ \forall n\in \mathbb {N} ,}
where
E
{\displaystyle \operatorname {E} }
is the expected value operator.
If the sequence of characteristic functions converges pointwise to some function
φ
{\displaystyle \varphi }
φ
n
(
t
)
→
φ
(
t
)
∀
t
∈
R
,
{\displaystyle \varphi _{n}(t)\to \varphi (t)\quad \forall t\in \mathbb {R} ,}
then the following statements become equivalent:
X
n
{\displaystyle X_{n}}
converges in distribution to some random variable X
X
n
→
D
X
,
{\displaystyle X_{n}\ {\xrightarrow {\mathcal {D}}}\ X,}
i.e. the cumulative distribution functions corresponding to random variables converge at every continuity point of the c.d.f. of X ;
{
X
n
}
n
=
1
∞
{\textstyle \{X_{n}\}_{n=1}^{\infty }}
is tight :
lim
x
→
∞
(
sup
n
P
[
|
X
n
|
>
x
]
)
=
0
;
{\displaystyle \lim _{x\to \infty }\left(\sup _{n}\operatorname {P} {\big }\right)=0;}
φ
(
t
)
{\displaystyle \varphi (t)}
is a characteristic function of some random variable X ;
φ
(
t
)
{\displaystyle \varphi (t)}
is a continuous function of t ;
φ
(
t
)
{\displaystyle \varphi (t)}
is continuous at t = 0.
Proof
Rigorous proofs of this theorem are available.
References
^ Williams, D. (1991). Probability with Martingales . Cambridge University Press. section 18.1. ISBN 0-521-40605-6 .
Fristedt, B. E.; Gray, L. F. (1996). A modern approach to probability theory . Boston: Birkhäuser. Theorems 14.15 and 18.21. ISBN 0-8176-3807-5 .
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