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TRANSFORMATION OF
FUNCTION OF A RANDOM
VARIABLE
UNIVARIATE
TRANSFORMATIONS
TRANSFORMATION OF RANDOM
VARIABLES
• If X is an rv with cdf F(x), then Y=g(X) is
also an rv.
• If we write y=g(x), the function g(x) defines
a mapping from the original sample space
of X, S, to a new sample space, , the
sample space of the rv Y.
g(x): S
2
TRANSFORMATION OF RANDOM
VARIABLES
• Let y=g(x) define a 1-to-1 transformation.
That is, the equation y=g(x) can be solved
1
x
g
(y)
uniquely:
• Ex: Y=X-1 X=Y+1 1-to-1
• Ex: Y=X² X=± sqrt(Y)
not 1-to-1
• When transformation is not 1-to-1, find
disjoint partitions of S for which
transformation is 1-to-1.
3
TRANSFORMATION OF RANDOM
VARIABLES
If X is a discrete r.v. then S is countable. The sample
space for Y=g(X) is ={y:y=g(x),x S}, also
countable. The pmf for Y is
fY y P Y y
xg 1 y
P X x
xg 1 y
f x
4
Example
• Let X~GEO(p). That is, f (x) p(1 p) x 1 for x 1,2,3,...
• Find the p.m.f. of Y=X-1
• Solution: X=Y+1
y
f Y ( y) f X ( y 1) p(1 p) for y 0,1,2,...
• P.m.f. of the number of failures before the first
success
• Recall: X~GEO(p) is the p.m.f. of number of
Bernoulli trials required to get the first success
5
Example
• Let X be an rv with pmf
1/ 5, x 2
1/ 6, x 1
p x 1/ 5, x 0
1/15, x 1
11/ 30, x 2
Let Y=X2.
S ={2, 1,0,1,2}
1/ 5, y 0
p( y ) 7 / 30, y 1
17 / 30, y 4
={0,1,4}
6
FUNCTIONS OF CONTINUOUS
RANDOM VARIABLE
• Let X be an rv of the continuous type with pdf f.
Let y=g(x) be differentiable for all x and non-zero.
Then, Y=g(X) is also an rv of the continuous type
with pdf given by
d 1
1
g ( y) | for y
f ( g ( y)) |
dy
h( y)
0
o.w.
7
FUNCTIONS OF CONTINUOUS
RANDOM VARIABLE
• Example: Let X have the density
1, 0 x 1
f x
0, otherwise
Let Y=eX.
X=g1 (y)=log Y dx=(1/y)dy.
1
h y 1. ,0 log y 1
y
1
, 1 y e
h y y
0, otherwise
8
FUNCTIONS OF CONTINUOUS
RANDOM VARIABLE
• Example: Let X have the density
1 x2 / 2
f x
e
, x .
2
Let Y=X2. Find the pdf of Y.
9
CDF method
2x
F
(
x
)
1
e
for x 0
• Example: Let
Consider Y eX . What is the p.d.f. of Y?
• Solution:
FY ( y) P(Y y) P(e X y) P(X ln y)
FX (ln y) 1 y 2 for y 1
d
f Y ( y)
FY ( y) 2 y 3 for y 1
dy
10
CDF method
• Example: Consider a continuous r.v. X,
and Y=X². Find p.d.f. of Y.
• Solution:
FY ( y) P(X 2 y) P( y X y ) FX ( y ) FX ( y )
d
d
f Y ( y) f X ( y ) ( y ) f X ( y ) ( y )
dy
dy
1
[f X ( y ) f X ( y )]
2 y
11
TRANSFORMATION OF
FUNCTION OF TWO OR MORE
RANDOM VARIABLES
BIVARIATE
TRANSFORMATIONS
DISCRETE CASE
• Let X1 and X2 be a bivariate random vector
with a known probability distribution function.
Consider a new bivariate random vector (U, V)
defined by U=g1(X1, X2) and V=g2(X1, X2)
where g1(X1, X2) and g2(X1, X2) are some
functions of X1 and X2 .
13
DISCRETE CASE
• Then, the joint pmf of (U,V) is
fU ,V u, v Pr U u,V v
f
X1 , X 2
x1 , x2 AU ,V
x1 , x2
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EXAMPLE
• Let X1 and X2 be independent Poisson
distribution random variables with parameters
1 and 2. Find the distribution of U=X1+X2.
15
CONTINUOUS CASE
• Let X=(X1, X2, …, Xn) have a continuous joint
distribution for which its joint pdf is f, and
consider the joint pdf of new random variables
Y1, Y2,…, Yk defined as
Y1 g1 X 1 , X 2 , , X n
Y2 g 2 X 1 , X 2 , , X n
*
Yk g k X 1 , X 2 , , X n
T
X Y
~
~
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CONTINUOUS CASE
• If the transformation T is one-to-one and onto,
then there is no problem of determining the
inverse transformation, and we can invert the
equation in (*) and obtain new equations as
follows:
17
CONTINUOUS CASE
1
x1 g1 y1 , y2 , , yk
x2 g 21 y1 , y2 , , yk
* *
1
xn g n y1 , y2 , , yk n
1
g i / y i
• Assuming that the partial derivatives
exist at every point (y1, y2,…,yk=n). Under these
assumptions, we have the following
determinant J
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CONTINUOUS CASE
g11
g11
yn
y1
J det
1
g 1
g
n
n
yn
y1
called as the Jacobian of the transformation
specified by (**). Then, the joint pdf of Y1,
Y2,…,Yk can be obtained by using the change
of variable technique of multiple variables.
19
CONTINUOUS CASE
• As a result, the new p.d.f. is defined as
follows:
f X1 ,, X n g11 , g 21 ,, g n1 | J |, for y1 , y2 ,, yn
g y1 , y2 ,, yn
0, otherwise
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Example
• Recall that I claimed: Let X1,X2,…,Xn be
independent rvs with Xi~Gamma(i, ).
Then, X ~ Gamma ,
n
i 1
i
n
i 1
i
• Prove this for n=2 (for simplicity).
21
M.G.F. Method
• If X1,X2,…,Xn are independent random
variables with
MGFs
M
xi (t), then the
n
MGF of Y Xi is MY (t) MX1 (t)...MXn (t)
i 1
22
Example
• Recall that I claimed:
~ Bin n , p . Then,
independent
Let X
i
i
X ~ Bin n n
k
i 1
i
1
2
n , p .
k
• Let’s prove this.
23
Example
• Recall that I claimed: Let X1,X2,…,Xn be
independent rvs with Xi~Gamma(i, ).
Then,
X ~ Gamma ,
n
i 1
i
n
i 1
i
• We proved this with transformation
technique for n=2.
• Now, prove this for general n.
24
More Examples on
Transformations
• Example 1:
• Recall that I claimed: If X~N( , 2), then
X
Z
~ N (0,1)
• Let’s prove this.
25
Example 2
• Recall that I claimed:
Let X be an rv with X~N(0, 1). Then,
X ~
2
2
1
Let’s prove this.
26
Example 3
• Let X~N( , 2) and Y=exp(X). Find the
p.d.f. of Y.
27
Example 4
Recall that I claimed:
• If X and Y have independent N(0,1)
distribution, then Z=X/Y has a Cauchy
distribution with =0 and σ=1.
Recall the p.d.f. of Cauchy distribution:
f (x)
1
1 (
1
x
)2
, 0
Let’s prove this claim.
28
Example 5
• See Examples 6.3.12 and 6.3.13 in Bain
and Engelhardt (pages 207 & 208 in 2nd
edition). This is an example of two
different transformations:
• In Example 6.3.12: In Example 6.3.13:
X1 & X2 ~ Exp(1)
X1 & X2 ~ Exp(1)
Y1=X1
Y1=X1-X2
Y2=X1+X2
Y2=X1+X2
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Example 6
• Let X1 and X2 are independent with
N(μ1,σ²1) and N(μ2,σ²2), respectively. Find
the p.d.f. of Y=X1-X2.
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