1. 1 Multivariable Distributions ch4

2. 2  It may be favorable to take more than one measurement on a random experiment.  The data may then be collected in pairs of (x i, y i ).  Def.4.1-1: X & Y are two discrete R.V. defined over the support S. The probability that X=x, Y=y is denoted as f(x,y)=P(X=x,Y=y). f(x,y) is the joint probability mass function (joint p.m.f.) of X and Y:  0≤f(x,y)≤1; ΣΣ (x,y) ∈ S f(x,y)=1; P[(X,Y) ∈ A]=ΣΣ (x,y) ∈ A f(x,y), A ⊆ S.

3. 3 Illustration Example  Ex.4.1-3: Roll a pair of dice: X is the smaller and Y is the larger.  The outcome is (3, 2) or (2, 3) ⇒ X=2 & Y=3 with 2/36 probability.  The outcome is (2, 2) ⇒ X=2 & Y=2 with 1/36 probability.  Thus, the joint p.m.f. of X and Y is 11/3662/362/362/362/362/361/369/3652/362/362/362/361/36 7/3642/362/362/361/36 5/3632/362/361/36 3/3622/361/36 1/3611/36 123456 11/369/367/365/363/361/36 Marginal p.m.f. y x

4. 4 Marginal Probability and Independence  Def.4.1-2: X and Y have the joint p.m.f f(x,y) with space S.  The marginal p.m.f. of X is f 1 (x)=Σ y f(x,y)=P(X=x), x ∈ S 1.  The marginal p.m.f. of Y is f 2 (y)=Σ x f(x,y)=P(Y=y), y ∈ S 2.  X and Y are independent iff P(X=x, Y=y)=P(X=x)P(Y=y), namely, f(x,y)=f 1 (x)f 2 (y), x ∈ S 1, y ∈ S 2.  Otherwise, X and Y are dependent.  X and Y in Ex5.1-3 are dependent: 1/36=f(1,1) ≠ f 1 (1)f 2 (1)=11/36*1/36.  Ex4.1-4: The joint p.m.f. f(x,y)=(x+y)/21, x=1,2,3, y=1,2.  Then, f 1 (x)=Σ y=1~2 (x+y)/21=(2x+3)/21, x=1,2,3.  Likewise, f 2 (1)=Σ x=1~3 (x+y)/21=(6+3y)/21, y=1,2.  Since f(x,y)≠f 1 (x)f 2 (y), X and Y are dependent.  Ex4.1-6: f(x,y)=xy 2 /13, (x,y)=(1,1),(1,2),(2,2).

5. 5 Quick Dependence Checks  Practically, “dependence” can be quickly determined if  The support of X and Y is NOT rectangular, or  S is therefore not the product set {(x,y): x ∈ S 1, y ∈ S 2 }, as in Ex4.1-6.  f(x,y) cannot be factored (separated) into the product of an x-alone expression and a pure y function.  In Ex4.1-4, f(x,y) is a sum, not a product, of x-alone and y-alone functions.  Ex4.1-7: [Probability Histogram for a joint p.m.f.]

6. 6 Mathematical Expectation  If u(X 1,X 2 ) is a function of two R.V. X 1 & X 2, then if it exists, is called the mathematical expectation (or expected value) of u(X 1,X 2 ).  The mean of Xi, i=1,2:  The variance of X i :  Ex4.1-8: A player selects a chip from a bowl having 8 chips: 3 marked (0,0), 2 (1,0), 2 (0,1), 1 (1,1).

7. 7 Probability Density Function Joint  Joint Probability Density Function, joint p.d.f., of two continuous-type R.V. X & Y, is an integrable function f(x,y):  f(x,y)≥0; ∫ y=-∞~∞ ∫ x=-∞~∞ f(x,y)dxdy=1;  P[(X,Y) ∈ A]=∫∫ A f(x,y)dxdy, for an event A.  Ex4.1-9: X and Y have the joint p.d.f.  A={(x,y): 0<x<1, 0<y<x}.  The respective marginal p.d.f.s are X and Y are independent!

8. 8 Independence of Continuous Type R.V.s  Two continuous type R.V. X and Y are independent iff the joint p.d.f. factors into the product of their marginal p.d.f.s.  Ex4.1-10: X and Y have the joint p.d.f.  The support S={(x,y): 0≤x≤y≤1}, bounded by x=0, y=1, x=y lines.  The marginal p.d.f.s are  Various expected values: X and Y are dependent!

9. 9 Multivariate Hypergeometric Distribution  Ex4.1-11: Of 200 students, 40 have As, 60 Bs; 100 Cs, Ds, or Fs.  A sample of size 25 is taken at random without replacement.  X 1 is the number of A students, X 2 is the number of B students, and  25 –X 1 –X 2 is the number of the other students.  The space S = {(x 1,x 2 ): x 1,x 2 ≥0, x 1 +x 2 ≤25}.  The marginal p.m.f. of X 1 can be also obtained as: X 1 and X 2 are dependent! From the knowledge of the model.

10. 10 Binomial ⇒ Trinomial Distribution  Trinomial Distribution: The experiment is repeated n times.  The probability p 1 : perfect, p 2 : second; p 3 : defective, p 3 =1-p 1 -p 2.  X 1 : the number of perfect items, X 2 for second, X 3 for defective.  The joint p.m.f. is  X 1 is b(n,p 1 ), X 2 is b(n,p 2 ); both are dependent.  Ex4.1-13: In manufacturing a certain item,  95% of the items are good; 4% are “seconds”, and 1% defective.  An inspector observes n=20 items selected at random, counting the number X of seconds, and the number Y of defectives.  The probability that at least 2 seconds or at least 2 defective items are found, namely A={(x,y): x≥2 or y≥2}, is

11. 11 Correlation Coefficient  For two R.V. X 1 & X 2,  The mean of X i, i=1,2:  The variance of X i :  The covariance of X 1 & X 2 is  The correlation coefficient of X 1 & X 2 is  Ex4.2-1: X 1 & X 2 have the joint p.m.f. → Not a product ⇒ Dependent!

12. 12 Insights of the Meaning of ρ  Among all points in S, ρ tends to be positive if more points are simultaneously above or below their respective means with larger probability.  The least-squares regression line is a line passing given (μ x,μ y ) with the best slope b s.t. K(b)=E{[(Y-μ y )-b(X-μ x )] 2 } is minimized.  The square of the vertical distance from a point to the line.  ρ= ±1: K(b)=0 ⇒ all the points lie on the least-squares regression line.  ρ= 0: K(b)=σ y 2, the line is y=μ y ; X and Y could be independent!!  ρmeasures the amount of linearity in the probability distribution.

13. 13 Example  Ex4.2-2: Roll a pair of 4-sided die: X is the number of ones, Y is the number of twos and threes.  The joint p.m.f. is  The line of best fit is

14. 14 Independence ⇒ ρ=0  The converse is not necessarily true!  Ex4.2-3: The joint p.m.f. of X and Y is f(x,y)=1/3, (x,y)=(0,1), (1,0), (2,1).  Obviously, the support is not “rectangular”, so X and Y are dependent.  Empirical Data: from n bivariate observations: (x i,y i ), i=1..n.  We can compute the sample mean and variance for each variate.  We can also compute the sample correlation coefficient and the sample least squares regression line.  We can also compute the sample correlation coefficient and the sample least squares regression line. (Ref. p.241) ∵ independence

15. 15 Conditional Distributions  Def.4.3-1: The conditional probability mass function of X, given that Y=y, is defined by g(x|y)=f(x,y)/f 2 (y), if f 2 (y)>0.  Likewise, h(y|x)=f(x,y)/f 1 (x), if f 1 (x)>0.  Ex.4.3-1: X and Y have the joint p.m.f f(x,y)=(x+y)/21, x=1,2,3; y=1,2.  f 1 (x)=(2x+3)/21, x=1,2,3; f 2 (y)=(3y+6)/21, y=1,2.  Thus, given Y=y, the conditional p.m.f. of X is  When y=1, g(x|1)=(x+1)/9, x=1,2,3; g(1|1):g(2|1):g(3|1)=2:3:4.  When y=2, g(x|2)=(x+2)/12, x=1,2,3; g(1|2):g(2|2):g(3|2)=3:4:5.  Similar relationships about h(y|x) can be obtained. Dependent!

16. 16 Conditional Mean and Variance  The conditional mean of Y, given X=x, is  The conditional variance of Y, given X=x, is  Ex.4.3-2: [from Ex.4.3-1] X and Y have the joint p.m.f f(x,y)=(x+y)/21, x=1,2,3; y=1,2.

17. 17 Relationship about Conditional Mean  The point (μ X,μ Y ) locates on the above two lines, and is their junction.  The product of the slopes is ρ 2.  The ratio of the slopes is These relations can derive the unknown from the others known.

18. 18 Example  Ex.4.3-3: X and Y have the trinomial p.m.f. with n, p 1, p 2, p 3 =1-p 1 -p 2  They have the marginal p.m.f. b(n, p 1 ), b(n, p 2 ), so

19. 19 Example for Continuous-type R.V.  Ex4.3-5: [From Ex4.1-10] ⇒ The conditional distribution of Y given X=x is U(x,1). [U(a,b) has mean (b+a)/2, and variance (b-a) 2 /12.]

20. 20 Bivariate Normal Distribution  The joint p.d.f of X : N(μ X,σ X 2 )and Y : N(μ Y,σ Y 2 ) is  Therefore, A linear function of x.A constant w.r.t. x.

21. 21 Examples  Ex.5.6-1:  Ex.5.6-2

22. 22 Bivariate Normal:ρ=0 ⇒ Independence  Thm5.6-1: For X and Y with a bivariate normal distribution with ρ, X and Y are independent iffρ=0.  So are trivariate and multivariate normal distributions.  When ρ=0,

23. 23 Transformations of R.V.s  In Section 3.5, the transformation of a single variable X with f(x) to another Y=v(X), an increasing or decreasing fn, can be done as:  Ex.4.4-1: X: b(n,p), Y=X 2, if n=3, p=1/4, then  What is the transformation u(X/n) leading to a variance free of p? Taylor’s expansion about p:  Ex: X: b(100,1/4) or b(100,9/10). Continuous type Discrete type When the variance is constant, or free of p,

24. 24 Multivariate Transformations  When the function Y=u(X) does not have a single-valued inverse, it needs to consider possible inverse functions individually.  Each range will be delimited to match the right inverse.  For multivariate, the derivative is replaced by the Jacobian.  Continuous R.V. X 1 and X 2 have the joint p.d.f. f(x 1, x 2 ).  If has the single-valued inverse then the joint p.d.f. of Y 1 and Y 2 is  [Most difficult] The mapping of the supports are considered.

25. 25 Transformation to the Independent  Ex4.4-2: X 1 and X 2 have the joint p.d.f. f(x 1, x 2 )=2, 0<x 1 <x 2 <1.  Consider Y 1 =X 1 /X 2, Y 2 =X 2 :  The mapping of the supports:  The marginal p.d.f.:  ∵ g(y 1,y 2 )=g 1 (y 1 )g 2 (y 2 ) ∴ Y 1,Y 2 Independent. →

26. 26 Transformation to the Dependent  Ex4.4-3: X 1 and X 2 are indep., each with p.d.f. f(x)=e -x, 0<x<∞.  Their joint p.d.f. f(x 1, x 2 )= e -x1 e -x2, 0<x 1 <∞, 0<x 2 <∞.  Consider Y 1 =X 1 -X 2, Y 2 =X 1 -X 2 :  The mapping of the supports:  The marginal p.d.f.:  ∵ g(y 1,y 2 ) ≠g 1 (y 1 )g 2 (y 2 ) ∴ Y 1,Y 2 Dependent. → Double exponential p.d.f.

27. 27 Beta Distribution  Ex4.4-4: X 1 and X 2 have indep. Gamma distributions withα,θ and β, θ. Their joint p.d.f. is  Consider Y 1 =X 1 /(X 1 +X 2 ), Y 2 =X 1 +X 2 : i.e., X 1 =Y 1 Y 2, X 2 =Y 2 -Y 1 Y 2.  The marginal p.d.f.:  ∵ g(y 1,y 2 )=g 1 (y 1 )g 2 (y 2 ) ∴ Y 1,Y 2 Independent. Beta p.d.f. Gamma p.d.f.

28. 28 Box-Muller Transformation Box-Muller Transformation  Ex5.3-4: X 1 and X 2 have indep. Uniform distributions U(0,1).  Consider  Two indep. U(0,1) ⇒ two indep. N(0,1)!!

29. 29 Distribution Function Technique  Ex.5.3-5: Z is N(0,1), U is χ 2 (r), Z and U are independent.  The joint p.d.f. of Z and U is χ 2 (r+1)

30. 30 Another Example  Ex.4.4-5: U: χ 2 (r 1 ) and V: χ 2 (r 2 ) are independent.  The joint p.d.f. of Z and U is  The knowledge of known distributions and their associated integration relationships are useful to derive the distributions of unknown distributions. χ 2 (r 1 +r 2 )

31. 31 Order Statistics  The order statistics are the observations of the random sample arranged in magnitude from the smallest to the largest.  Assume there is no tie: identical observations.  Ex6.9-1: n=5 trials: {0.62, 0.98, 0.31, 0.81, 0.53} for the p.d.f. f(x)=2x, 0<x<1. The order statistics are {0.31, 0.53, 0.62, 0.81, 0.98}.  The sample median is 0.62, and the sample range is 0.98-0.31=0.67.  Ex6.9-2: Let Y 1 <Y 2 <Y 3 <Y 4 <Y 5 be the order statistics for X 1, X 2, X 3, X 4, X 5, each from the p.d.f. f(x)=2x, 0<x<1.  Consider P(Y 4 <1/2) ≡at least 4 of X i ’s must be less than 1/2: 4 successes.

32. 32 General Cases  The event that the rth order statistic Y r is at most y, {Y r ≤y}, can occur iff at least r of the n observations are no more than y.  The probability of “success” on each trial is F(y).  We must have at least r successes. Thus,

33. 33 Alternative Approach  A heuristic approach to obtain g r (y):  Within a short interval Δy:  There are (r-1) items fall less than y, and (n-r) items above y+Δy.  The multinomial probability with n trials is approximated as.  Ex5.9-3: (from Ex6.9-2) Y 1 <Y 2 <Y 3 <Y 4 <Y 5 are the order statistics for X 1, X 2, X 3, X 4, X 5, each from the p.d.f. f(x)=2x, 0<x<1. On a single trial

34. 34 More Examples  Ex: 4 indep. Trials(Y 1 ~ Y 4 ) from a distribution with f(x)=1, 0<x<1.  Find the p.d.f. of Y 3.  Ex: 7 indep. trials(Y 1 ~ Y 7 ) from a distribution f(x)=3(1-x) 2, 0<x<1.  Find the p.d.f. of the sample median, i.e. Y 4, is less than  Method 1: find g 4 (y), then  Method 2: find then By Table II on p.647.

35. 35 Order Statistics of Uniform Distributions  Thm3.5-2: if X has a distribution function F(X), which has U(0,1). {F(X 1 ),F(X 2 ),…,F(X n )} ⇒ Wi’s are the order statistics of n indep. observations from U(0,1).  The distribution function of U(0,1) is G(w)=w, 0<w<1.  The p.d.f. of the rth order statistic W r =F(Y r ) is ⇒ Y’s partition the support of X into n+1 parts, and thus n+1 areas under f(x) and above the x-axis.  Each area equals 1/(n+1) on the average. p.d.f. Beta

36. 36 Percentiles Percentiles  The (100p) th sample percentile π p is defined s.t. the area under f(x) to the left of π p is p.  Therefore, Y r is the estimator of π p, where r=(n+1)p.  In case (n+1)p is not an integer, a (weighted) average of Y r and Y r+1 can be used, where r=floor[(n+1)p].  The sample median is  Ex6.9-5: X is the weight of soap; n=12 observations of X is listed:  1013, 1019, 1021, 1024, 1026, 1028, 1033, 1035, 1039, 1040, 1043, 1047.  ∵ n=12, the sample median is  ∵ (n+1)(0.25)=3.25, the 25 th percentile or first quartile is  ∵ (n+1)(0.75)=9.75, the 75 th percentile or third quartile is  ∵ (n+1)(0.6)=7.8, the 60 th percentile

37. 37 Another Example  Ex: The order statistics of 13 indep. Trials(Y 1 <Y 2 < …< Y 13 ) from a continuous type distribution with the 35 th percentile π 0.35.  Ex5.6-7: The order statistics of 13 indep. Trials(Y 1 <Y 2 < …< Y 13 ) from a continuous type distribution with the 35 th percentile π 0.35.  Find P(Y 3 < π 0.35 < Y 7 )  The event {Y 3 < π 0.35 < Y 7 } happens iff there are at least 3 but less than 7 “successes”, where the success probability is p=0.35. By Table II on p.677~681. Success