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Problem 4 (Poisson Distributed RV) In a composite experiment, Z is a Poisson-distributed RV with ...
28. In many problems about modeling count data, it is found that values of zero in the data are f...
28. In many problems about modeling count data, it is found that values of zero in the data are far more common than can be explained well using a Poisson model (we can make P(X 0) large for X ~ Pois(A) by making λ small, but that also constrains the mean and variance of X to be small since both are X). The Zero-Inflated Poisson distribution is a modification of the Poisson to address this issue, making it easier to...
3- (20 points) A random experiment consists of simultaneously and independently flipping a coin five times...
3- (20 points) A random experiment consists of simultaneously and independently flipping a coin five times and observing the n-5 resulting values facing up. The coin is biased with: P(heads) - 0.75 : P(tails) p-0.25 Define a Random Variable (RV) X equal to the number of fails that we observe during the flips. a) Give the probability P. that the random variable X will take on the value 3 ANSWER: P,= (simplified number) b) Give the mean of X, that...
(a) Let YA ~ P(λ) denote a Poisson RV with parameter λ. For a non-random function b(A) > 0, consider the the RVs Xx:...
(a) Let YA ~ P(λ) denote a Poisson RV with parameter λ. For a non-random function b(A) > 0, consider the the RVs Xx:-b(A)(YA-A), λ > 0. Use the method of ChFs to find a function b(A) such that XA 1 X as λ 00, where X is a non-degenerate RV. You are expected to establish the fact of convergence and specify the distribution of X ,IE [0,oo)? Explain. (b) Does the distribution of y, converge as ג Hint: (a)...
Let M be a Poisson (λ) random variable having M equal m. If we flip a...
Let M be a Poisson (λ) random variable having M equal m. If we flip a p-biased coin m times and let X be the number of heads, show that X is a Poisson (pλ) random variable. Use the identity for k= 0 to infinity Σy^k/k! =e^y
Problem 4 Let X and y be independent Poisson(A) and Poisson(A2) random variables, respectively. i. Write...
Problem 4 Let X and y be independent Poisson(A) and Poisson(A2) random variables, respectively. i. Write an expression for the PMF of Z -X + Y. i.e.. pz[n] for all possible n. ii. Write an expression for the conditional PMF of X given that Z-n, i.e.. pxjz[kn for all possible k. Which random variable has the same PMF, i.e., is this PMF that of a Bernoulli, binomial, Poisson, geometric, or uniform random variable (which assumes all possible values with equal...
Problem(13) (10 points) An unfair coin is tossed, and it is assumed that the chance of...
Problem(13) (10 points) An unfair coin is tossed, and it is assumed that the chance of getting a head, H. is (Thus the chance of setting tail, T. is.) Consider a random experiment of throwing the coin 5 times. Let S denote the sample space (a) (2 point) Describe the elements in S. (b) (2 point) Let X be the random variable that corresponds to the number of the heads coming up in the four times of tons. What are...
Two discrete random variables X and Y are jointly distributed.
.1. Two discrete random variables X and Y are jointly distributed. The joint pmf is f(z, y) = 1/28 , SX = {0, 1, 2, 3, 4, 5,6}, and SY = {0, .... X), where Y is a non-negative integer a) Find the marginal pdfs of X and Y b) Caculate E(X) and E(Y). 2. Let the joint pdf of X aud Y be a) Draw the graph of the support of X and Y b) Determine c in the joint pdf. c) Find E(X +Y),...
Problem 5. Indicator variables S points possible (graded) Consider a sequence of n 1 independent tosses...
Problem 5. Indicator variables S points possible (graded) Consider a sequence of n 1 independent tosses of a biased coin, at times k = 0,1,2,...,n On each toss, the probability of Heads is p, and the probability of Tails is 1 -p {1,2,.., at time for E resulted in Tails and the toss at time - 1 resulted in A reward of one unit is given if the toss at time Heads. Otherwise, no reward is given at time Let...
Return to the original model. We now introduce a Poisson intensity parameter X for every time point and denote the parameter () that gives the canonical exponential family representation as above by...
Return to the original model. We now introduce a Poisson intensity parameter X for every time point and denote the parameter () that gives the canonical exponential family representation as above by θ, . We choose to employ a linear model connecting the time points t with the canonical parameter of the Poisson distribution above, i.e., n other words, we choose a generalized linear model with Poisson distribution and its canonical link function. That also means that conditioned on t,...
2. The joint density of X and Y is given by Say 0SX S1,0 Sy sa...
2. The joint density of X and Y is given by Say 0SX S1,0 Sy sa fxy(x,y) = {o otherwise. (a) Find fxiy (ay). (b) Set up the integrals (do not evaluate) for evaluating Cov(X,Y). 3. In this question, you will identify the distribution of the sum of independent random variables. I expect you will find that the mgf approach is your friend. (a) Let X and Y be independent Poisson random variables with means X, and A2, respectively, and...
28. In many problems about modeling count data, it is found that values of zero in the data are far more common than can be explained well using a Poisson model (we can make P(X 0) large for X ~ Pois(A) by making λ small, but that also constrains the mean and variance of X to be small since both are X). The Zero-Inflated Poisson distribution is a modification of the Poisson to address this issue, making it easier to...
3- (20 points) A random experiment consists of simultaneously and independently flipping a coin five times and observing the n-5 resulting values facing up. The coin is biased with: P(heads) - 0.75 : P(tails) p-0.25 Define a Random Variable (RV) X equal to the number of fails that we observe during the flips. a) Give the probability P. that the random variable X will take on the value 3 ANSWER: P,= (simplified number) b) Give the mean of X, that...
(a) Let YA ~ P(λ) denote a Poisson RV with parameter λ. For a non-random function b(A) > 0, consider the the RVs Xx:-b(A)(YA-A), λ > 0. Use the method of ChFs to find a function b(A) such that XA 1 X as λ 00, where X is a non-degenerate RV. You are expected to establish the fact of convergence and specify the distribution of X ,IE [0,oo)? Explain. (b) Does the distribution of y, converge as ג Hint: (a)...
Problem 4 Let X and y be independent Poisson(A) and Poisson(A2) random variables, respectively. i. Write an expression for the PMF of Z -X + Y. i.e.. pz[n] for all possible n. ii. Write an expression for the conditional PMF of X given that Z-n, i.e.. pxjz[kn for all possible k. Which random variable has the same PMF, i.e., is this PMF that of a Bernoulli, binomial, Poisson, geometric, or uniform random variable (which assumes all possible values with equal...
Problem(13) (10 points) An unfair coin is tossed, and it is assumed that the chance of getting a head, H. is (Thus the chance of setting tail, T. is.) Consider a random experiment of throwing the coin 5 times. Let S denote the sample space (a) (2 point) Describe the elements in S. (b) (2 point) Let X be the random variable that corresponds to the number of the heads coming up in the four times of tons. What are...
Problem 5. Indicator variables S points possible (graded) Consider a sequence of n 1 independent tosses of a biased coin, at times k = 0,1,2,...,n On each toss, the probability of Heads is p, and the probability of Tails is 1 -p {1,2,.., at time for E resulted in Tails and the toss at time - 1 resulted in A reward of one unit is given if the toss at time Heads. Otherwise, no reward is given at time Let...
Return to the original model. We now introduce a Poisson intensity parameter X for every time point and denote the parameter () that gives the canonical exponential family representation as above by θ, . We choose to employ a linear model connecting the time points t with the canonical parameter of the Poisson distribution above, i.e., n other words, we choose a generalized linear model with Poisson distribution and its canonical link function. That also means that conditioned on t,...
2. The joint density of X and Y is given by Say 0SX S1,0 Sy sa fxy(x,y) = {o otherwise. (a) Find fxiy (ay). (b) Set up the integrals (do not evaluate) for evaluating Cov(X,Y). 3. In this question, you will identify the distribution of the sum of independent random variables. I expect you will find that the mgf approach is your friend. (a) Let X and Y be independent Poisson random variables with means X, and A2, respectively, and...
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