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Stat310       Bivariate distributions


                           Hadley Wickham
Monday, 16 February 2009
1. Recap
               2. Transformations, the cdf and
                  the uniform distribution
               3. Introduction to bivariate distributions
               4. Properties of pdf. Marginal pdfs &
                  expectation
               5. Feedback


Monday, 16 February 2009
Recap

                   X ~ Exponential(θ). Y = log(X).
                   What is fY(y)?
                   X ~ Uniform(0, 10). Y =   X2.

                   What is fY(y)?




Monday, 16 February 2009
Theorem 3.5-1
                   IF
                   Y ~ Uniform(0, 1)
                   F a cdf
                   X = F-1(Y)
                   THEN
                   X has cdf F(x)
                   (Assume F strictly increasing for simplicity of proof, not needed in general)


Monday, 16 February 2009
Theorem 3.5-2
                   IF
                   X has cdf F
                   Y = F(X)
                   THEN
                   Y ~ Uniform(0, 1)
                   (Assume F strictly increasing for simplicity of proof, not needed in general)




Monday, 16 February 2009
http://www.johndcook.com/
          distribution_chart.html



Monday, 16 February 2009
Bivariate random
                               variables
                             Bivariate = two variables


Monday, 16 February 2009
Bivariate rv

                   Previously dealt with single random
                   variables at a time.
                   Now we’re going to look at two (probably
                   related) at a time
                   New tool: multivariate calculus



Monday, 16 February 2009
Monday, 16 February 2009
Monday, 16 February 2009
1
        f (x, y) =                          − 2 < x, y < 2
                   16
                                              What would you call
         What is:
                                               this distribution?
             •       P(X < 0) ?
             •                                Draw diagrams and
                     P(X < 0 and Y < 0) ?
                                               use your intuition
             •       P(Y > 1) ?
             •       P(X > Y) ?
             •       P(X2 + Y2 < 1)


Monday, 16 February 2009
f (x, y) = c a < x, y < b


                           Is this a pdf?
                           How could we work out c?




Monday, 16 February 2009
f (x, y) ≥ 0    ∀x, y


                                    f (x, y) = 1
                               R2




Monday, 16 February 2009
S = {(x, y) : f (x, y) > 0}
      Called the support or
      sample space




Monday, 16 February 2009
What is the
  bivariate cdf going
  to look like?




Monday, 16 February 2009
What is the
  bivariate cdf going
  to look like?
                           x    y
 F (x, y) =                          f (u, v)dvdu
                           −∞   −∞




Monday, 16 February 2009
Your turn

                   F(x, y) =   c(x 2   +   y 2)   -1 < x, y < 1
                   What is c?
                   What is f(x, y)?




Monday, 16 February 2009
Marginal distribution of X


                       fX (x) =                 f (x, y)dy
                                            R

                           Marginal distribution of Y

                           fY (y) =             f (x, y)dx
                                            R


Monday, 16 February 2009
Demo



Monday, 16 February 2009
Feedback



Monday, 16 February 2009

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11 Bivariate

  • 1. Stat310 Bivariate distributions Hadley Wickham Monday, 16 February 2009
  • 2. 1. Recap 2. Transformations, the cdf and the uniform distribution 3. Introduction to bivariate distributions 4. Properties of pdf. Marginal pdfs & expectation 5. Feedback Monday, 16 February 2009
  • 3. Recap X ~ Exponential(θ). Y = log(X). What is fY(y)? X ~ Uniform(0, 10). Y = X2. What is fY(y)? Monday, 16 February 2009
  • 4. Theorem 3.5-1 IF Y ~ Uniform(0, 1) F a cdf X = F-1(Y) THEN X has cdf F(x) (Assume F strictly increasing for simplicity of proof, not needed in general) Monday, 16 February 2009
  • 5. Theorem 3.5-2 IF X has cdf F Y = F(X) THEN Y ~ Uniform(0, 1) (Assume F strictly increasing for simplicity of proof, not needed in general) Monday, 16 February 2009
  • 6. http://www.johndcook.com/ distribution_chart.html Monday, 16 February 2009
  • 7. Bivariate random variables Bivariate = two variables Monday, 16 February 2009
  • 8. Bivariate rv Previously dealt with single random variables at a time. Now we’re going to look at two (probably related) at a time New tool: multivariate calculus Monday, 16 February 2009
  • 11. 1 f (x, y) = − 2 < x, y < 2 16 What would you call What is: this distribution? • P(X < 0) ? • Draw diagrams and P(X < 0 and Y < 0) ? use your intuition • P(Y > 1) ? • P(X > Y) ? • P(X2 + Y2 < 1) Monday, 16 February 2009
  • 12. f (x, y) = c a < x, y < b Is this a pdf? How could we work out c? Monday, 16 February 2009
  • 13. f (x, y) ≥ 0 ∀x, y f (x, y) = 1 R2 Monday, 16 February 2009
  • 14. S = {(x, y) : f (x, y) > 0} Called the support or sample space Monday, 16 February 2009
  • 15. What is the bivariate cdf going to look like? Monday, 16 February 2009
  • 16. What is the bivariate cdf going to look like? x y F (x, y) = f (u, v)dvdu −∞ −∞ Monday, 16 February 2009
  • 17. Your turn F(x, y) = c(x 2 + y 2) -1 < x, y < 1 What is c? What is f(x, y)? Monday, 16 February 2009
  • 18. Marginal distribution of X fX (x) = f (x, y)dy R Marginal distribution of Y fY (y) = f (x, y)dx R Monday, 16 February 2009