Problem 1: Suppose that Y1, Y2, . Yn is an IID sample of size n from Bernoulli(p) randomvariables. Recall that the Bernoulli(p) model is the same as the B(n, p) model with n = 1.That is the probability mass function of Y is P(Y = k) = p*(1– p)'-k for k e {0,1}.(a) Argue, using the Law of Large Numbers that Y converge in probability to p(b) Use the central limit theorem to find the limiting distribution of n(Y – p)(c) If p is the probability of success, define the quantity r(p) =L. The quantity T(p) is calledthe odds of occurrence of the event if p=P(success). Use the continuous mapping theorem toshow that 7(Y) r(p).(d) Use the delta method to find the limiting distribution ofVn ( +(p)

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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2. Suppose that X1, . Xn are iid Geometric random variables with frequency func- ... tion f(x; 0) = 0(1 – 0)", x = 0, 1, 2, ..., 0 E (0, 1). Find the ML estimator 0, of 0. Show that Ôn is consistent an find its asymptotic distribution.
Question 3. Let X₁,, Xn be a random sample from a distribution with the pdf given by f₁₁x(x) = exp(-ª) if x ≥ A, otherwise f(x) = 0, where > 0. Find the MLE's of 0 and X. Start by writing the likelihood function and note the constraint involving A. Question 4. #4.2.9 of the textbook.
11. Let (y1, Y2 ... Yn) be independent random sample from the uniform distribution on [0, 1]. (a) show that Z = – In Y; has exponential distribution with parameter 1. (b) Hence or otherwise, show that -2 In Y; xản
4. Continuous random variables X and Y are independent, with parameters ux = 30, ly = 40, Ox = 15, and ơy = 5. Which of the following gives the correct values for ux+Y and ox+y? = 70 and ox+y = 14.1 (A) Hx+Y (B) Мx+ү %3D 70 and o x+y %3D 15.8 (C) Hx+Y = 70 and ox+y = 20 (D) Ux+Y = -10 and ox+Y =14.1 (E) Ux+y =-10 and ox+y = 15.8
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Suppose that the moment-generating function of a random variable X is given by Mx(1) = +" +" + 4 3t 2 4 5t 15 Find the probability mass function of X.
Let X be a discrete random variable with the following PMF 1 1 for x=-2 8 100 for x = -1 Px(x) = for x = 0 1 for x = 1 for x = 2 4 0 otherwise a) Find E[X] and Var(X) b) Now we define a new random variableY = (x+1)², Find PMF of Y and E[Y]
Suppose we take a random sample of size 25 form Bi(10, p). Find the pmf and the CDF of the 5th order statistic Y3.
Q 6.1. Suppose Z = (Z₁, Z2, Z3) is a standard multi-variate Gaussian random variable i.e., for i ≤ 3, Zi~ N(0, 1) are i.i.d. random variables. Each of the random variables, (a)–(d), on the left is equal in distribution to exactly one random variables, (1)–(4), on the right. Pair up according to "equal in distribution" and explain briefly your reasoning. X₁ (a) X₂ (b) (x₂) - (2) (c) (x²) - (¹/1² 1/√²) (2₁) (3/√2 = √2 = (V2) Z₁ (d) (x) - (1) (²) X2 Y₁ (9)-(-) (2) (1=1) Y₂ (1) Y₁ (²) (4) - (1/² (3) (1/√√2 1/√2-1/√2) (x₁) = (²₁ ² ✓/³²) (3) 2 2 √2 1 Z3 Y₁ → ()-()) (4) = Y₂ 1/2) (2) 1) (2)
Q 6.1. Suppose Z = (Z1, Z2, Z3) is a standard multi-variate Gaussian random variable i.e., for i ≤ 3, Zi~ N(0, 1) are i.i.d. random variables. Each of the random variables, (a)-(d), on the left is equal in distribution to exactly one random variables, (1)-(4), on the right. Pair up according to "equal in distribution" and explain briefly your reasoning. (a) (X₂) (¹) (X) = (2) 1/√2 (Ⓒ) (x₂) - (3/1² ¹1/1²) (2) (c = 2 0 = 2 Z₁ 1) (²) 3 (4¹) (X) = (²¹) (1) (2) (3) Y₁ 1 (29) - ()) (2) = Y₂ 1 Y₁ (121) = (1/² √₂) (2) (1/√2 1/√2 /2 −1/√√2, Y₂ X₁ X₂ = 22 1 1 2 0 Y₁ 2 1 (4) (2)) = (²) (²) 1 1 Z₁ Z₂ Z3
Suppose that the random variables Y1,..., Y(n > 2) satisfy Y - Ba, + Ei, i= 1,2,..., T, where 1,..., En are iid N(0, o), and both B and o are unknown. (a) Assume z1,..., In are fixed known constants. Here we observe Y1 = n,., Y observed data Y = y = (y1,..., Yn). Find a two-dimensional sufficient statistic of Y = (Yı,., Yn) for (8,0?). (b) Assume now that a1,...,n are random variables with a known joint distribution m(r1,..., In), and the r,'s are independent of G's (it is traditional in the linear regression to use lower case for independent variables r;'s). In this case, the observed data (Y, x) = {(Yi, r;)}i-1,n. Find a three- dimensional sufficient statistic of (Y, x) for (B,02). Im, and the
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