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Functions of Random Variables Notes of STAT 6205 by Dr. Fan

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Overview Chapter 5 Functions of One random variable o General: distribution function approach o Change-of-variable approach Functions of Two random variables o Change-of-variable approach Functions of Independent random variables Order statistics The Moment Generating Function approach Random functions associated with normal distributions o Student’s t-distribution The Central Limit Theorem o Normal approximation of binomial distribution (Section 10.5) Chebyshev’s Inequality and convergence in probability 6205-Ch52

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General Method: Distribution Function Approach Goal: to find the distribution of Y=h(X) When: the pdf of X, f(x) is known Then the cdf of Y, G(y) is: And the pdf of Y, g(y)=G’(y) 6205-Ch53

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Examples/Exercises Let X~U(0,10) and Y=X^3. Find the cdf and pdf of Y Let X~Exp(mu=2) and Y=Exp(X). Find the cdf and pdf of Y Let X~Gamma(a,b) and X=log(Y). Find the pdf of Y (Loggamma distribution) 6205-Ch54

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Change of Variable Approach When: the pdf of X is known and Y=h(X), a monotonic function (i.e. its inverse function exists; X = V(Y) ) 6205-Ch55

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Examples/Exercises Let Y=(1-X)^3 and find its pdf g(y) Problem 1: f(x)=x/2, 0

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Transformations of Two Random Variables Let f(x1,x2) be the joint pdf of X1,X2 Let Y1=u1(X1,X2) and Y2=u2(X1,X2) where u1, u2 have inverse functions, that is, X1=v1(Y1,Y2) and X2=v2(Y1,Y2) Goal: find the joint pdf of Y1,Y2, g(y1,y2) 6205-Ch57

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Examples/Exercises 1.f(x1,x2)=2 where 0

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Independent Random Variables Let X1, X2, …,Xn be independent random variables Joint pmf (or pdf) of X1, X2, …, Xn: f(x1,x2,…,xn)=f1(x1)f2(x2)…fn(xn) Random sample from a distribution f(x): X1, X2, … Xn are independent and identically distributed; f(x1,x2,…,xn)=f(x1)f(x2)…f(xn) 6205-Ch59

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Examples/Exercises Let X1, X2, …, Xn be a random sample from Exp(0.5). Find the joint p.d.f of this sample. Exercise: What is the probability of seeing at least one Xi less than one? Exactly one less than one? 6205-Ch510

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Functions of Independent R. V.s Theorem Let X1, X2, …, Xn be independent r. v.s. Then: Theorem (page 238) 6205-Ch511

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Examples/Exercises Given a random sample of size n from a distribution with mean mu and SD sigma, find the mean and variance of the sample mean 6205-Ch512

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Moment Generating Function 6205-Ch513

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Examples/Exercises Example: Prove that the sum of i.i.d. Ber(p) r.v.s is a Bin(n, p) r. v. Exercise: Prove that the sum of i.i.d. Exp(mu) r. v.s is a Gamma(a=n, b=0.5) r. v. 1)What is the m.g.f. of Exp(mu)? 2)What is the m.g.f. of Gamma(a,b)? 3)Prove this problem using m.g.f Ch514

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Random Variables Assoc. With Normal Distributions Theorem 1: The distribution of the sum of i.i.d. normal r. v.s is also normal Theorem 2: The distribution of the sum of normal r. v.s is also normal Theorem 3: The distribution of the average of normal r. v.s is also normal 6205-Ch515

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Student’s t-distribution 6205-Ch516

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Proof: 1)Show S^2 and X-bar are independent 2)Use m.g.f to prove the distribution is chi-square Example: Show that the one-sample t test statistic is t- distributed with (n-1) degree of freedom 6205-Ch517

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Features of t distribution t(r) Shape: Bell-shaped Center and Spread: mean=0 if r > 1 variance =r/(r-2) if r > 2(undefined otherwise) M.G.F. does not exist Asymptotic distribution: (show simulation results) As d.f. r goes to infinity, t(r) approaches to N(0,1) 6205-Ch518

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Central Limit Theorem 6205-Ch519

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Examples/Exercises Illustration: Bin(n, p) goes to Normal as n goes to infinity [Aplia: STAT 1000 homework 4 Q3] Problem: Let X-bar be the mean of a random sample of n=25 currents in a strip of wire in which each measurement has a mean of 15 and a variance of 4. Estimate the probability of X-bar falling between 14.4 and Problem: Suppose BART wants to perform some quality control. They know the waiting time for one at a BART station is U(10,30). In a random sample of 30 people, what tis the (approximate) probability that the average waiting time is more than 22 minutes? Recall the mean and variance for U(10,30) is 20 and respectively Ch520

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Chebyshev’s Inequality If the r. v. X has a mean m and variance s ^2, then for every k > 1, Q: how to use this inequality to set up a lower bound of P(|X - m |< ks )? Example: Use this inequality to find a lower bound of the probability that X is no more than 2 S.D. from the mean. Is the lower bound close to the exact probability if X ~ N( m, s ^2 ) 6205-Ch521

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Example: Tossing a Coin If we want to estimate p, the chance of heads for a given coin, how many times share we toss it in order to get a sufficient accurate estimate? Let Y be the # of heads on n flips; sample estimate of p, p-hat = Y/n. Use the Chebyshev’s Inequality to find the required sample size n Ch522

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(Weak) Law of Large Number Let X1, X2, …, Xn be i.i.d. r. v.s with finite mean m and finite S.D. s. Then X-bar converges to m in probability. Proof. By Chebychev’s Inequality Ch523

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