Question

In: Statistics and Probability

Suppose X is a Gamma random variable with shape parameter α and scale parameter θ >...

Suppose X is a Gamma random variable with shape parameter α and scale parameter θ > 0, i.e., the pdf is given as, f(x|α, θ) = 1 Γ(α)θ α x α−1 e −x/θ , 0 < x < ∞, (1) where α > 0, θ > 0 and Γ(a) = Z ∞ 0 x a−1 e −x dx. HINT: see section 3.2 of the textbook. (a) What is the support of X? That is, X = ? (b) Show that the function given in (1) is a true pdf, i.e., that f(x) > 0 for all x ∈ X and Z ∞ −∞ f(x)dx = 1. (c) Show that the moment generating function M(t) for the density defined in (1) exists and is given by M(t) = 1 (1 − θt) α , t < 1 θ (2) (d) Using the moment generating function in (2), show that µ = E(X) = αθ, EX2 = αθ2 + α 2 θ 2 , and σ 2 = αθ2

Solutions

Expert Solution


Related Solutions

Let X1,...,Xn be a random sample from a gamma distribution with shape parameter α and rate...
Let X1,...,Xn be a random sample from a gamma distribution with shape parameter α and rate β (note that this may be a different gamma specification than you are used to). Then f(x | α, β) = (βα/Γ(α))*x^(α−1) * e^(−βx). where x, α, β > 0 (a) Derive the equations that yield the maximum likelihood estimators of α and β. Can they be solved explicitly? Hint: don’t forget your maximum checks, and it may help to do some internet searching...
A random variable X is said to follow the Weibull distribution with shape parameter
A random variable \(X\) is said to follow the Weibull distribution with shape parameter \(\alpha\) and scale parameter \(\beta\), written \(W(\alpha, \beta)\) if its p.d.f. is given by $$ f(x)=\frac{\alpha}{\beta^{\alpha}} x^{\alpha-1} e^{-\left(\frac{g}{3}\right)^{\alpha}} $$ for \(x>0\). The Weibull distribution is used to model lifetime of item subject to failure. If \(\alpha \in(0,1),\) it is used to model decreasing failure rate overtime, whereas if \(\alpha>1,\) one models increasing failure rate over time. It is easy to show that the c.d.f. of \(X\)...
in R. Generate a random sample of size 700 from a gamma distribution with shape parameter...
in R. Generate a random sample of size 700 from a gamma distribution with shape parameter 8 and scale parameter 0.1. Create a histogram of the sample data. Draw the true parametric density (for the specified gamma distribution) on the histogram. The curve for the density should be red. (Note: The “true parametric density” is the distribution from which the sample values were generated. It is NOT the kernel density that is estimated from the data.)
a. Suppose that X is a discrete random variable with pmf f(x) = (2 + θ(2...
a. Suppose that X is a discrete random variable with pmf f(x) = (2 + θ(2 − x))/ 6 , x = 1, 2, 3, where the parameter θ belongs to the parameter space Ω = (θ : −2 < θ < 2). Suppose further that a random sample X1, X2, X3, X4 is taken from this distribution, and the four observed values are {x1, x2, x3, x4} = {3, 2, 3, 1}. Find the maximum likelihood estimate of θ....
If a random variable Xhas the gamma distributionwith α= 2and β = 1, (a) What is...
If a random variable Xhas the gamma distributionwith α= 2and β = 1, (a) What is the probability density functionf(x)? (b) find P(1.8 < X< 2.4). (c) What isE(X) and Var(X)? (d) Put α= 1and β = 2.What isthe probability density functionf(x). (e) What is the name of the distributionin (d)?
The random variable X is distributed with pdf fX(x, θ) = c*x*exp(-(x/θ)2), where x>0 and θ>0....
The random variable X is distributed with pdf fX(x, θ) = c*x*exp(-(x/θ)2), where x>0 and θ>0. a) What is the constant c? b) We consider parameter θ is a number. What is MLE and MOM of θ? Assume you have an i.i.d. sample. Is MOM unbiased? c) Please calculate the Crameer-Rao Lower Bound (CRLB). Compare the variance of MOM with Crameer-Rao Lower Bound (CRLB).
The random variable X is distributed with pdf fX(x, θ) = c*x*exp(-(x/θ)2), where x>0 and θ>0....
The random variable X is distributed with pdf fX(x, θ) = c*x*exp(-(x/θ)2), where x>0 and θ>0. a) Find the distribution of Y = (X1 + ... + Xn)/n where X1, ..., Xn is an i.i.d. sample from fX(x, θ). If you can’t find Y, can you find an approximation of Y when n is large? b) Find the best estimator, i.e. MVUE, of θ?
The random variable X is distributed with pdf fX(x, θ) = c*x*exp(-(x/θ)2), where x>0 and θ>0....
The random variable X is distributed with pdf fX(x, θ) = c*x*exp(-(x/θ)2), where x>0 and θ>0. (Please note the equation includes the term -(x/θ)2 or -(x/θ)^2 if you cannot read that) a) What is the constant c? b) We consider parameter θ is a number. What is MLE and MOM of θ? Assume you have an i.i.d. sample. Is MOM unbiased? c) Please calculate the Cramer-Rao Lower Bound (CRLB). Compare the variance of MOM with Crameer-Rao Lower Bound (CRLB).
The random variable X is distributed with pdffX(x, θ) = c*x*exp(-(x/θ)2), where x>0 and θ>0. (Please...
The random variable X is distributed with pdffX(x, θ) = c*x*exp(-(x/θ)2), where x>0 and θ>0. (Please note the equation includes the term -(x/θ)2 - that is -(x/θ)^2 if your computer doesn't work) a) What is the constant c? b) We consider parameter θ is a number. What is MLE and MOM of θ? Assume you have an i.i.d. sample. Is MOM unbiased? c) Please calculate the Cramer-Rao Lower Bound (CRLB). Compare the variance of MOM with Crameer-Rao Lower Bound (CRLB)....
For a random variable X with a Cauchy distribution with θ = 0 , so that...
For a random variable X with a Cauchy distribution with θ = 0 , so that f(x) =(1/ π)/( 1 + x^2) for -∞ < x < ∞ (a) Show that the expected value of the random variable X does not exist. (b) Show that the variance of the random variable X does not exist. (c) Show that a Cauchy random variable does not have finite moments of order greater than or equal to one.
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT