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Unformatted text preview: Transformations Let X be a random variable with cdf F X ( x ). Let Y = g ( x ). For any set A , P ( Y ∈ A ) = P ( g ( X ) ∈ A ) = P ( X ∈ g 1 ( A )) . 1. When X is discrete The pmf of Y is f Y ( y ) = P ( Y = y ) = X x ∈ g 1 ( y ) P ( X = x ) = X x ∈ g 1 ( y ) f X ( x ) for y ∈ Y , and f Y ( y ) = 0 for y not in Y . 2. When X is continuous The pdf of Y is f Y ( y ) = f x ( g 1 ( y ))  d dy g 1 ( y )  y ∈ Y otherwise Theorem : Let X have pdf f X ( x ), let Y = g ( X ), and define the sample space of X be X . Suppose there exists a partition, A , A 1 , ..., A k , of X such that P ( X ∈ A ) = 0 and f X ( x ) is continuous on each A i . Further, suppose there exist functions g 1 ( x ) , ..., g k ( x ), defined on A 1 , ..., A k , respectively, satisfying 1. g ( x ) = g i ( x ), for x ∈ A i , 2. g i ( x ), is monotone on A i , 3. the set Y = { y : y = g i ( x ) for some x ∈ A i } is the same for each i = 1 , ..., k , and 4. g 1 i ( y ) has a continuous derivative on Y , for each i = 1 , ..., k . Then f Y ( y ) = ∑ k i =1 f x ( g 1 i ( y ))  d dy g 1 i ( y )  y ∈ Y otherwise 14 Examples 1. Let X ∼ U (0 , 1). Find the distribution of Y = X n . 2. Let X have standard normal distribution. Find the pdf of Y = X 2 . 15 Conditional Distributions Definition : Let ( X, Y ) be a discrete bivariate random vector with joint pmf f ( x, y ) and marginal pmfs f X ( x ) and f Y ( y ). For any x such that P ( X = x ) = f X ( x ) > 0, the conditional pmf of Y given X = x is the function of y denoted by f ( y  x ) and defined by f ( y  x ) = P ( Y = y  X = x ) = f ( x, y ) f X ( x ) ....
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This note was uploaded on 06/05/2011 for the course STATISTICS 2006 taught by Professor Ho during the Spring '11 term at CUHK.
 Spring '11
 Ho
 Statistics

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