For the Rayleigh distribution:
1a) Determine the most powerful critical region for testing
H0 θ=θ0 against H1 θ=θ1
(θ1 > θ0) using a random sample of size
n.
1b) Find the uniformly most powerful H0
θ<θ0 against H1 θ>θ1
For the Rayleigh distribution:
1a) Determine the most powerful critical region for testing
H0 θ=θ0 against H1 θ=θ1
(θ1 > θ0) using a random sample of size
n.
1b) Find the uniformly most powerful H0
θ<θ0 against H1 θ>θ1
For the GEOMETRIC distribution:
1a) Determine the most powerful critical region for testing
H0 p=p0 against H1 p=θp
(p1 > p0) using a random sample of size
n.
1b) Find the uniformly most powerful H0
p<θ0 against H1 p>θ1
For the geometric distribution:
1a) Determine the most powerful critical region for testing
H0 p=p0 against H1 p=θp (p1 > p0)
using a random sample of size n.
1b) Find the uniformly most powerful H0
p<θ0 against H1 p>θ1
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.
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. (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
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)....
Consider a Bernoulli distribution f(x|θ) = θ^x (1 − θ)^(1−x) for
x = 0 and x = 1. Let the prior distribution for θ be f(θ) = 6θ(1 −
θ) for θ ∈ (0, 1).
(a) Find the posterior distribution for θ.
(b) Find the Bayes’ estimator for θ.
Let f(x; θ) = θxθ−1 for 0 < x < 1 and θ ∈ Ω = {θ : 0 <
θ < ∞}. Let X1, . . . , Xn denote a random sample of size n from
this distribution. (a) Sketch the pdf of X for (i) θ = 1/2, (ii) θ
= 1 and (iii) θ = 2. (b) Show that ˆθ = −n/ ln (Qn i=1 Xi) is the
maximum likelihood estimator of θ. (c) Determine the...