How to do the following in R: Write a function to generate a random sample of size n from the Gam...
How to do the following in R:
Write a function to generate a random sample of size n from the Gamma(α,1) distribution by the acceptance-rejection method. Generate a random sample of size 1000 from the Gamma(3,1) distribution. (Hint: you may use g(x) ∼ Exp(λ = 1/α) as your proposal distribution, where λ is the rate parameter. Figure out the appropriate constant c).
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How to do the following in R: Write a function to generate a random sample of size n from the Gam...
Please answer the question clearly. Consider a random sample of size n from a Poisson population...
Please answer the question clearly.
Consider a random sample of size n from a Poisson population with parameter λ (a) Find the method of moments estimator for λ. (b) Find the maximum likelihood estimator for λ. Suppose X has a Poisson distribution and the prior distribution for its parameter A is a gamma distribution with parameters and β. (a) Show that the posterior distribution of A given X-x is a gamma distribution with parameters a +r and (b) Find the...
#3.7 distribution. 0 and check that the mode of the generated samples is close to the...
#3.7
distribution. 0 and check that the mode of the generated samples is close to the (check the histogram). theoretical mode mass function 3.5 A discrete random variable X has probability 3 4 AtB.8 HUS 2 X p(x) 0.1 0.2 0.2 0.2 0.3 a random sample of size Use the inverse transform method to generate 1000 from the distribution of X. Construct a relative frequency table and compare the empirical with the theoretical probabilities. Repeat using the R sample function....
Suppose that X1,..., Xn is a random sample from a gamma distribu- tion, The gamma distribution has parameters r and λ,...
Suppose that X1,..., Xn is a random sample from a gamma distribu- tion, The gamma distribution has parameters r and λ, and also has E(X)-r/λ and Var(X)-r/ P. Calculate the method of moments MOM) estimators of r and λ in terms of the first two sample moments Mi and M2
Suppose that X1,..., Xn is a random sample from a gamma distribu- tion, The gamma distribution has parameters r and λ, and also has E(X)-r/λ and Var(X)-r/ P. Calculate the...
5. Let X ∼ Exp(λ) with λ unknown, and suppose X1, X2 is a random sample...
5. Let X ∼ Exp(λ) with λ unknown, and suppose X1, X2 is a random sample of size 2. Show that M = sqrt( X1 · X2 ) is a biased estimator of 1/λ and modify it to create an unbiased estimator. (Hint: During your journey, you’ll need the help of the gamma distribution, the gamma function, and the knowledge that Γ(1/2) = √ π.)
R codeing simulation For n = 20, simulate a random sample of size n from N(µ, 2 2 ), where µ = 1....
R codeing simulation
For n = 20, simulate a random
sample of size n from N(µ, 2 2 ), where µ = 1. Note that we just
use µ = 1 to generate the random sample. In the problem below, µ is
an unknown parameter. Plot in different figures: (a) the likelihood
function of µ, (b) the log likelihood function of µ. Mark in both
plots the maximum likelihood estimate of µ from the generated
random sample
(2) For n-20,...
4 a) Prove that the Box-Muller method described in class generates independent standard ll generate n...
4 a) Prove that the Box-Muller method described in class generates independent standard ll generate n to write a function which wi σ-) random variables b) Suppose that X is an exponential random variable with rate parameter λ and that Y is the integer part of X. Show that Y has a geometric distribution and use this result to give an algorithm to generate a random sample of size n from the geometric distribution with specified success probability p implementing...
5. Let X ~ Exp(A) with λ unknown, and suppose X1,X2 is a random sample of...
5. Let X ~ Exp(A) with λ unknown, and suppose X1,X2 is a random sample of size 2, Show that M-X (Hint: During your journey, you' need the help of the gamma distribution, the gamma function, and the knowledge that Г(1/2-ут) X1 X2 is a biased estimator of - and modify it to create an unbiased estimator
Suppose that you need to generate a random variable Y with a density function f (y)...
Suppose that you need to generate a random variable Y with a density function f (y) corresponding to a beta distribution with range [0,1], and with a non-integer shape parameter for the beta distribution. For this case there is no closed-form cdf or inverse cdf. Suppose your choices for generating Y are either: a) an acceptance-rejection strategy with a constant majorizing function g(u) = V over [0, 1], i.e., generate u1 and u2 IID from a U[0,1] generator and accept...
Using MATLAB, not R codes, I repeat, please, not in R, just MATLAB codes, write the complete code for: 1. Assume Y is an exponential random variable with rate parameter λ=2. (1) Generate 1000 samples...
Using MATLAB, not R codes, I repeat, please, not in R, just MATLAB codes, write the complete code for: 1. Assume Y is an exponential random variable with rate parameter λ=2. (1) Generate 1000 samples from this exponential distribution using inverse transform method (2) Compare the histogram of your samples with the true density of Y.
- Suppose a random sample of size n is taken from the following distribution with a...
- Suppose a random sample of size n is taken from the following distribution with a known positive parameter a. f(x;0,-) = a20 V 27797z exp 0; ; 0<x<00,0< < 0,0 < 8 < 00 elsewhere For this distruttore, the formats for mye or and x-a are respectively, Myo (1) = exp v{(1 - V1 –24*70)} for 1 < 2112 and exp{}(-VT - 2/0)} My-- (1) for 1 < ✓1 - 2t/0 2 Find the maximum likelihood estimators, 0 and...
Please answer the question clearly.
Consider a random sample of size n from a Poisson population with parameter λ (a) Find the method of moments estimator for λ. (b) Find the maximum likelihood estimator for λ. Suppose X has a Poisson distribution and the prior distribution for its parameter A is a gamma distribution with parameters and β. (a) Show that the posterior distribution of A given X-x is a gamma distribution with parameters a +r and (b) Find the...
#3.7
distribution. 0 and check that the mode of the generated samples is close to the (check the histogram). theoretical mode mass function 3.5 A discrete random variable X has probability 3 4 AtB.8 HUS 2 X p(x) 0.1 0.2 0.2 0.2 0.3 a random sample of size Use the inverse transform method to generate 1000 from the distribution of X. Construct a relative frequency table and compare the empirical with the theoretical probabilities. Repeat using the R sample function....
Suppose that X1,..., Xn is a random sample from a gamma distribu- tion, The gamma distribution has parameters r and λ, and also has E(X)-r/λ and Var(X)-r/ P. Calculate the method of moments MOM) estimators of r and λ in terms of the first two sample moments Mi and M2
Suppose that X1,..., Xn is a random sample from a gamma distribu- tion, The gamma distribution has parameters r and λ, and also has E(X)-r/λ and Var(X)-r/ P. Calculate the...
R codeing simulation
For n = 20, simulate a random
sample of size n from N(µ, 2 2 ), where µ = 1. Note that we just
use µ = 1 to generate the random sample. In the problem below, µ is
an unknown parameter. Plot in different figures: (a) the likelihood
function of µ, (b) the log likelihood function of µ. Mark in both
plots the maximum likelihood estimate of µ from the generated
random sample
(2) For n-20,...
4 a) Prove that the Box-Muller method described in class generates independent standard ll generate n to write a function which wi σ-) random variables b) Suppose that X is an exponential random variable with rate parameter λ and that Y is the integer part of X. Show that Y has a geometric distribution and use this result to give an algorithm to generate a random sample of size n from the geometric distribution with specified success probability p implementing...
5. Let X ~ Exp(A) with λ unknown, and suppose X1,X2 is a random sample of size 2, Show that M-X (Hint: During your journey, you' need the help of the gamma distribution, the gamma function, and the knowledge that Г(1/2-ут) X1 X2 is a biased estimator of - and modify it to create an unbiased estimator
- Suppose a random sample of size n is taken from the following distribution with a known positive parameter a. f(x;0,-) = a20 V 27797z exp 0; ; 0<x<00,0< < 0,0 < 8 < 00 elsewhere For this distruttore, the formats for mye or and x-a are respectively, Myo (1) = exp v{(1 - V1 –24*70)} for 1 < 2112 and exp{}(-VT - 2/0)} My-- (1) for 1 < ✓1 - 2t/0 2 Find the maximum likelihood estimators, 0 and...
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