ORDER STATISTICS AND LIMITING DISTRIBUTIONS 1 ORDER STATISTICS

ORDER STATISTICS AND LIMITING DISTRIBUTIONS 1

ORDER STATISTICS • Let X 1, X 2, …, Xn be a r. s. of size n from a distribution of continuous type having pdf f(x), a<x<b. Let X(1) be the smallest of Xi, X(2) be the second smallest of Xi, …, and X(n) be the largest of Xi. • X(i) is the i-th order statistic. 2

ORDER STATISTICS • It is often useful to consider ordered random sample. • Example: suppose a r. s. of five light bulbs is tested and the failure times are observed as (5, 11, 4, 100, 17). These will actually be observed in the order of (4, 5, 11, 17, 100). Interest might be on the kth smallest ordered observation, e. g. stop the experiment after kth failure. We might also be interested in joint distributions of two or more order statistics or functions of them (e. g. range=max – min) 3

ORDER STATISTICS • If X 1, X 2, …, Xn be a r. s. of size n from a population with continuous pdf f(x), then the joint pdf of the order statistics X(1), X(2), …, X(n) is Order statistics are not independent. The joint pdf of ordered sample is not same as the joint pdf of unordered sample. Future reference: For discrete distributions, we need to take ties into account (two X’s being equal). See, Casella and Berger, 1990, pg 231. 4

Example • Suppose that X 1, X 2, X 3 is a r. s. from a population with pdf f(x)=2 x for 0<x<1 Find the joint pdf of order statistics and the marginal pdf of the smallest order statistic. 5

ORDER STATISTICS • The Maximum Order Statistic: X(n) 6

ORDER STATISTICS • The Minimum Order Statistic: X(1) 7

ORDER STATISTICS • k-th Order Statistic y 1 y 2 … yk-1 yk yk+1 … P(X<yk) yn P(X>yk) y # of possible orderings n!/{(k 1)!1!(n k)!} f. X(yk) 8

Example • Same example but now using the previous formulas (without taking the integrals): Suppose that X 1, X 2, X 3 is a r. s. from a population with pdf f(x)=2 x for 0<x<1 Find the marginal pdf of the smallest order statistic. 9

Example • X~Uniform(0, 1). A r. s. of size n is taken. Find the p. d. f. of kth order statistic. • Solution: Let Yk be the kth order statistic. 10

ORDER STATISTICS • Joint p. d. f. of k-th and j-th Order Statistic (for k<j) k-1 items y 1 y 2 … yk-1 P(X<yk) j-k-1 items 1 item yk yk+1 … yj-1 yj yj+1 P(yk<X<yj) f. X(yk) n-j items 1 item yn # of possible orderings n!/{(k 1)!1!(j-k-1)!1!(n j)!} y P(X>yj) f. X(yj) 11

LIMITING DISTRIBUTION 12

Motivation • The p. d. f. of a r. v. often depends on the sample size (i. e. , n) • If X 1, X 2, …, Xn is a sequence of rvs and Yn=u(X 1, X 2, …, Xn) is a function of them, sometimes it is possible to find the exact distribution of Yn (what we have been doing lately) • However, sometimes it is only possible to obtain approximate results when n is large limiting distributions. 13

CONVERGENCE IN DISTRIBUTION • Consider that X 1, X 2, …, Xn is a sequence of rvs and Yn=u(X 1, X 2, …, Xn) be a function of rvs with cdfs Fn(y) so that for each n=1, 2, … where F(y) is continuous. Then, the sequence Yn is said to converge in distribution to Y. 14

CONVERGENCE IN DISTRIBUTION • Theorem: If for every point y at which F(y) is continuous, then Yn is said to have a limiting distribution with cdf F(y). The term “Asymptotic distribution” is sometimes used instead of “limiting distribution” • Definition of convergence in distribution requires only that limiting function agrees with cdf at its points of continuity. 15

Example Let X 1, …, Xn be a random sample from Unif(0, 1). Find the limiting distribution of the max order statistic, if it exists. 16

Example Let {Xn} be a sequence of rvs with pmf Find the limiting distribution of Xn , if it exists. 17

Example Let Yn be the nth order statistic of a random sample X 1, …, Xn from Unif(0, θ). Find the limiting distribution of Zn=n(θ -Yn), if it exists 18

Example Let X 1, …, Xn be a random sample from Exp(θ). Find the limiting distribution of the min order statistic, if it exists. 19

Example • Suppose that X 1, X 2, …, Xn are iid from Exp(1). Find the limiting distribution of the max order statistic, if it exists. 20

LIMITING MOMENT GENERATING FUNCTIONS • Let rv Yn have an mgf Mn(t) that exists for all n. If then Yn has a limiting distribution which is defined by M(t). 21

Example Let =np. The mgf of Poisson( ) The limiting distribution of Binomial rv is the 22 Poisson distribution.

CONVERGENCE IN PROBABILITY (STOCHASTIC CONVERGENCE) • A rv Yn convergence in probability to a rv Y if for every >0. Special case: Y=c where c is a constant not depending on n. The limiting distribution of Yn is degenerate at point c. 23

CHEBYSHEV’S INEQUALITY • Let X be an rv with E(X)= and V(X)= 2. • The Chebyshev’s Inequality can be used to prove stochastic convergence in many cases. 24

CONVERGENCE IN PROBABILITY (STOCHASTIC CONVERGENCE) • The Chebyshev’s Inequality proves the convergence in probability if the following three conditions are satisfied. 1. E(Yn)= n where 25

Example Let X be an rv with E(X)= and V(X)= 2<. For a r. s. of size n, is the sample mean. Is 26

WEAK LAW OF LARGE NUMBERS (WLLN) • Let X 1, X 2, …, Xn be iid rvs with E(Xi)= and V(Xi)= 2<. Define. Then, for every >0, converges in probability to . WLLN states that the sample mean is a good estimate of the population mean. For large n, the probability that the sample mean and population mean are close to each other with probability 1. But more to come on the properties of estimators. 27

Example • Let X 1, X 2, …, Xn be iid rvs from Ber(p). Then, E(X)=p and Var(X)=p(1 -p)<∞. By WLLN, 28

STRONG LAW OF LARGE NUMBERS • Let X 1, X 2, …, Xn be iid rvs with E(Xi)= and V(Xi)= 2<. Define. Then, for every >0, that is, converges almost sure to . 29

Relation between convergences Almost sure convergence is the strongest. (reverse is not true) 30

THE CENTRAL LIMIT THEOREM • Let X 1, X 2, …, Xn be a sequence of iid rvs with E(Xi)= and V(Xi)= 2<∞. Define. Then, or Proof can be found in many books, e. g. Bain and Engelhardt, 1992, page 239 31

Example • Let X 1, X 2, …, Xn be iid rvs from Unif(0, 1) and • Find approximate distribution of Yn. • Find approximate values of – – The 90 th percentile of Yn. • Warning: n=20 is probably not a big enough number of size. 32

SLUTKY’S THEOREM • If Xn X in distribution and Yn a, a constant, in probability, then a) Yn. Xn a. X in distribution. b) Xn+Yn X+a in distribution. 33

SOME THEOREMS ON LIMITING DISTRIBUTIONS • If Xn c>0 in probability, then for any function g(x) continuous at c, g(Xn) g(c) in prob. e. g. • If Xn c in probability and Yn d in probability, then • a. Xn+b. Yn ac+bd in probability. • Xn. Yn cd in probability • 1/Xn 1/c in probability for all c 0. 34

Example X~Gamma( , 1). Show that 35