An interesting identity about the expectation \bb E(X\mathbb{I}_{\{X<a\}}\mathbb{I}_{\{Y<b\}}).

Kassandra Mccall

Kassandra Mccall

Answered question

2022-09-29

An interesting identity about the expectation E ( X I { X < a } I { Y < b } )
Given (X,Y) follows a bivariate normal distribution N ( ( 0 0 ) ; ( 1 r r 1 ) ) with 1 < r < 1

Answer & Explanation

Bernard Scott

Bernard Scott

Beginner2022-09-30Added 9 answers

Step 1
I denote the correlation by ρ and the standard normal pdf by ϕ ( ).
The conditional distribution of Y given X = x is known to be N ( ρ x , 1 ρ 2 ).
Then, E [ X 1 X < a 1 Y < b ] = E E [ X 1 X < a 1 Y < b X ] = E [ X 1 X < a P ( Y < b X ) ] = E [ X 1 X < a Φ ( b ρ X 1 ρ 2 ) ] = a x ϕ ( x ) Φ ( b ρ x 1 ρ 2 ) d x
Using ϕ ( x ) = x ϕ ( x ), integrate by parts to get
a x Φ ( b ρ x 1 ρ 2 ) ϕ ( x ) d x = lim A ( Φ ( b ρ x 1 ρ 2 ) ϕ ( x ) | A a ) ρ I = Φ ( b ρ a 1 ρ 2 ) ϕ ( a ) ρ I ,
where I = a 1 1 ρ 2 ϕ ( b ρ x 1 ρ 2 ) ϕ ( x ) d x
Step 2
To find I, write down the expressions of ϕ ( ) and complete squares to get a normal pdf within the integral:
I = 1 2 π 1 ρ 2 a exp [ 1 2 { ( b ρ x 1 ρ 2 ) 2 + x 2 } ] d x = 1 2 π 1 ρ 2 a exp [ 1 2 ( 1 ρ 2 ) ( x 2 + b 2 2 b ρ x ) ] d x = e b 2 / 2 2 π a 1 2 π 1 ρ 2 exp [ 1 2 ( 1 ρ 2 ) ( x b ρ ) 2 ] d x = ϕ ( b ) Φ ( a b ρ 1 ρ 2 )
Thus, E [ X 1 X < a 1 Y < b ] = ϕ ( a ) Φ ( b ρ a 1 ρ 2 ) ρ ϕ ( b ) Φ ( a b ρ 1 ρ 2 )

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