![2. i) Let B be a random variable with the Binomial (n, p) distribution, so that Write down the likelihood function L(p) for m independent observations xi,...,Inm 2 marks 6 marks ili) Compute the bias and the mean squared error of the corresponding maximum likeli- from B. Int ii) Show that the maximum likelihood estimate for pis-Σ.ri. mn [7 marks] hood estimator of p. iv) Let X be a continuous random variable with density function for x > 0, and 0 otherwise. Write down the log likelihood function l(e). [2 marks] v) Find the maximum likelihood estimate for α. If you find a stationary value of the log 18 marks likelihood, you must verify that it is a maximum. Total: 25 marks](http://img.homeworklib.com/questions/70558580-c653-11ea-9c15-fbcacc08f8a1.png?x-oss-process=image/resize,w_560)
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2. i) Let B be a random variable with the Binomial (n, p) distribution, so that...
e (4 marks) Let m be an integer with the property that m 2 2. Consider that X1, X2,.. ., Xm are independent Binomial(n,p) random variables, where n is known and p is unknown. Note that p E (0,1). Wr...
e (4 marks) Let m be an integer with the property that m 2 2. Consider that X1, X2,.. ., Xm are independent Binomial(n,p) random variables, where n is known and p is unknown. Note that p E (0,1). Write down the expression of the likelihood function We assume that min(x1, . . . ,xm) 〈 n and max(x1, . . . ,xm) 〉 0 5 marks) Find , and give all possible solutions to the equation dL dL -...
Assume that we have three independent observations: where Xi ~ Binomial(n 7,p) for i E { 1.2.3). The value of p E (0, 1) is not known. When we have observations like this from different, independent...
Assume that we have three independent observations: where Xi ~ Binomial(n 7,p) for i E { 1.2.3). The value of p E (0, 1) is not known. When we have observations like this from different, independent ran- dom variables, we can find joint probabilities by multiplying together th ndividual probabilities. For example This should remind you the discussion on statistical independence of random variables that can be found in the course book (see page 22) Answer the following questions a...
2. (a) Suppose that x1,... , Vn are a random sample from a gamma distribution with...
2. (a) Suppose that x1,... , Vn are a random sample from a gamma distribution with shape parameter α and rate parameter λ, Here α > 0 and λ > 0. Let θ-(α, β). Determine the log-likelihood, 00), and a 2-dimensional sufficient statistic for the data (b) Suppose that xi, ,Xn are a random sample from a U(-9,0) distribution. f(x; 8) otherwise Here θ > 0, Determine the likelihood, L(0), and a one-dimensional sufficient statistic. Note that the likelihood should...
Show all working clearly. Thank you. 1. In this question, X is a continuous random variable...
Show all working clearly. Thank you.
1. In this question, X is a continuous random variable with density function (x)a otherwise where ? is an unknown parameter which is strictly positive. You wish to estimate ? using observations X1 , . …x" of an independent random sample XI…·X" from X Write down the likelihood function L(a), simplifying your answer as much as possi- ble 2 marks] i) Show that the derivative of the log likelihood function (a) is 4 marks]...
3. The water quality of a river was investigated by taking 15 water samples and recording...
3. The water quality of a river was investigated by taking 15 water samples and recording the counts of a particular bacteria for each sample. The data can be represented as {21,..., X15}, where z; is the number of bacteria in sample i, and the xi's are assumed to be independent and follow a Poi(21) distribution. (a) Write down the log-likelihood function for 01. Use this to find the maximum likeli- hood estimate of 0, in terms of the observed...
Consider a random sample X1, ..., Xn from a normal distribution with known mean 0 and...
Consider a random sample X1, ..., Xn from a normal distribution with known mean 0 and unknown variance 0 = 02 (a) Write the likelihood and log-likelihood function (b) Derive the maximum likelihood estimator for 6 (c) Show that the Fisher information matrix is I(O) = 2014 (d) What is the variance of the maximum likelihood estimator for @? Does it attain the Cramer-Rao lower bound? (e) Suppose that you are testing 0 = 1 versus the alternative 0 #...
2. (a) Suppose that xi,...,In are a random sample from a gamma distribution with shape parameter...
2. (a) Suppose that xi,...,In are a random sample from a gamma distribution with shape parameter and rate parameter λ, Γ(a) Here α > 0 and λ > 0. Let θ sufficient statistic for the data (α, β). Determine the log-likelihood, I(0), and a 2-dimensional b) Suppose that xi,...,In are a random sample from a U(-0,) distribution, 1/(20) if- otherwise x-θ f(x;0)-' 0, Here θ > 0, Determine the likelihood, L(0), and a one-dimensional sufficient statistic. Note that the likelihood...
I. The random variables X,, where P(success) = P(X = 1) = p = 1-P(X = 0) for1,2,..., represent a ...
I. The random variables X,, where P(success) = P(X = 1) = p = 1-P(X = 0) for1,2,..., represent a series of independent Bernoulli trials. Let the random variable Y be the trial number on which the first success is achieved (a) Explain why the probability mass function of Y is f(y) = pqy-1, y = 12. where q 1- p. State the distribution of Y. 2 part of your answer you should verify this is a marimum likelihood estima-...
(1 point) A random variable with probability density function p(x; 0) = 0x0–1 for 0 <x<...
(1 point) A random variable with probability density function p(x; 0) = 0x0–1 for 0 <x< 1 with unknown parameter 0 > 0 is sampled three times, yielding the values 0.64,0.65,0.54. Find each of the following. (Write theta for 0.) (a) The likelihood function L(0) = d (b) The derivative of the log-likelihood function [ln L(O)] = dᎾ (c) The maximum likelihood estimate for O is is Ô =
Question 5 15 marks] Let X be a random variable with pdf -{ fx(z) = - 0<r<1 (1) 0 :otherwise, Xa, n>2, be...
Question 5 15 marks] Let X be a random variable with pdf -{ fx(z) = - 0<r<1 (1) 0 :otherwise, Xa, n>2, be iid. random variables with pdf where 0> 0. Let X. X2.... given by (1) (a) Let Ylog X, where X has pdf given by (1). Show that the pdf of Y is Be- otherwise, (b) Show that the log-likelihood given the X, is = n log0+ (0- 1)log X (0 X) Hence show that the maximum likelihood...
e (4 marks) Let m be an integer with the property that m 2 2. Consider that X1, X2,.. ., Xm are independent Binomial(n,p) random variables, where n is known and p is unknown. Note that p E (0,1). Write down the expression of the likelihood function We assume that min(x1, . . . ,xm) 〈 n and max(x1, . . . ,xm) 〉 0 5 marks) Find , and give all possible solutions to the equation dL dL -...
Assume that we have three independent observations: where Xi ~ Binomial(n 7,p) for i E { 1.2.3). The value of p E (0, 1) is not known. When we have observations like this from different, independent ran- dom variables, we can find joint probabilities by multiplying together th ndividual probabilities. For example This should remind you the discussion on statistical independence of random variables that can be found in the course book (see page 22) Answer the following questions a...
2. (a) Suppose that x1,... , Vn are a random sample from a gamma distribution with shape parameter α and rate parameter λ, Here α > 0 and λ > 0. Let θ-(α, β). Determine the log-likelihood, 00), and a 2-dimensional sufficient statistic for the data (b) Suppose that xi, ,Xn are a random sample from a U(-9,0) distribution. f(x; 8) otherwise Here θ > 0, Determine the likelihood, L(0), and a one-dimensional sufficient statistic. Note that the likelihood should...
Show all working clearly. Thank you.
1. In this question, X is a continuous random variable with density function (x)a otherwise where ? is an unknown parameter which is strictly positive. You wish to estimate ? using observations X1 , . …x" of an independent random sample XI…·X" from X Write down the likelihood function L(a), simplifying your answer as much as possi- ble 2 marks] i) Show that the derivative of the log likelihood function (a) is 4 marks]...
3. The water quality of a river was investigated by taking 15 water samples and recording the counts of a particular bacteria for each sample. The data can be represented as {21,..., X15}, where z; is the number of bacteria in sample i, and the xi's are assumed to be independent and follow a Poi(21) distribution. (a) Write down the log-likelihood function for 01. Use this to find the maximum likeli- hood estimate of 0, in terms of the observed...
Consider a random sample X1, ..., Xn from a normal distribution with known mean 0 and unknown variance 0 = 02 (a) Write the likelihood and log-likelihood function (b) Derive the maximum likelihood estimator for 6 (c) Show that the Fisher information matrix is I(O) = 2014 (d) What is the variance of the maximum likelihood estimator for @? Does it attain the Cramer-Rao lower bound? (e) Suppose that you are testing 0 = 1 versus the alternative 0 #...
2. (a) Suppose that xi,...,In are a random sample from a gamma distribution with shape parameter and rate parameter λ, Γ(a) Here α > 0 and λ > 0. Let θ sufficient statistic for the data (α, β). Determine the log-likelihood, I(0), and a 2-dimensional b) Suppose that xi,...,In are a random sample from a U(-0,) distribution, 1/(20) if- otherwise x-θ f(x;0)-' 0, Here θ > 0, Determine the likelihood, L(0), and a one-dimensional sufficient statistic. Note that the likelihood...
I. The random variables X,, where P(success) = P(X = 1) = p = 1-P(X = 0) for1,2,..., represent a series of independent Bernoulli trials. Let the random variable Y be the trial number on which the first success is achieved (a) Explain why the probability mass function of Y is f(y) = pqy-1, y = 12. where q 1- p. State the distribution of Y. 2 part of your answer you should verify this is a marimum likelihood estima-...
(1 point) A random variable with probability density function p(x; 0) = 0x0–1 for 0 <x< 1 with unknown parameter 0 > 0 is sampled three times, yielding the values 0.64,0.65,0.54. Find each of the following. (Write theta for 0.) (a) The likelihood function L(0) = d (b) The derivative of the log-likelihood function [ln L(O)] = dᎾ (c) The maximum likelihood estimate for O is is Ô =
Question 5 15 marks] Let X be a random variable with pdf -{ fx(z) = - 0<r<1 (1) 0 :otherwise, Xa, n>2, be iid. random variables with pdf where 0> 0. Let X. X2.... given by (1) (a) Let Ylog X, where X has pdf given by (1). Show that the pdf of Y is Be- otherwise, (b) Show that the log-likelihood given the X, is = n log0+ (0- 1)log X (0 X) Hence show that the maximum likelihood...
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