Download Solutions to MAS Theory Exam 2014

1
Solutions to 2014 MAS exam : Theory
1.
a)
2
P (gray | litter 2) = .
5
b)
P (brown) = P (brown | litter 1)P (litter 1) + P (brown | litter 2)P (litter 2)
2 1
3 1
19
= ( )( ) + ( )( ) = .
3 2
5 2
30
c)
P (brown | litter 1)P (litter 1)
P (brown)
2 1
( )( )
10
= 3 19 2 = .
19
30
P (litter 1 | brown) =
2.
A negative binomial random variable has mean
r
p
and variance
r(1−p)
.
p2
a) By the weak law of large numbers (Mean of a sequence of independent random variables, each with mean µ and variance σ 2 , converges in probability to the mean µ), we have
X̄ =
Pn
i=1
n
Xi
converges in probability to pr . That means, for any given small value , the
probability that X̄ is beyond the given distance of
r
p
goes to zero as n increases to ∞.
b) Central limit theorem tells “For a sequence of independent and identically distributed
random variables, each with mean µ and variance σ 2 ,
X̄
√
σ/ n
√
converges in distribution to
N (0, 1).” By the Central limit theorem, we have √n(X̄−r/p)2 converges in distribution to
r(1−p)/p
N (0, 1).
2
c) For a negative binomial random variable with n=30, r=10, p=.5, mean is r/p = 20
and variance is r(1 − p)/p2 = 20.
√
30(X̄ − 20)
√
P (X̄ ≤ 11) = P (
≤
20
√
30(19 − 20)
√
) ≈ P (Z ≤ −1.225) = 0.1103
20
3.
a) The marginal density of X is
fX (x) =
=
Z 1
0
Z 1
0
fX,Y (x, y)dy
1
(x + 2y)dy
4
1
(xy + y 2 )|y=1
=
y=0
4
1
=
(x + 1), 0 ≤ x < 2.
4
b) The conditional density of Y given X is
fX,Y (x, y)
fX (x)
1
(x + 2y)
= 41
(x + 1)
4
x + 2y
=
, 0 ≤ x < 2, 0 ≤ y < 1.
x+1
fY |X (y|x) =
X and Y are not independent because the conditional density of Y given X depends on X.
c) Z =
9
(X+1)
⇒X=
9
Z
− 1. So, 3 < z ≤ 9 and J =
dx
dz
= − z92 . Therefore, the density of
Z is
1 9
81
fZ (z) = ( − 1 + 1)|J| = z −3 , 3 < z ≤ 9.
4 z
4
3
4.
a) By looking at the density function, it is actually a beta distribution with parameters
a = θ and b = 1. Therefore, mean of the distribution is
θ
.
θ+1
Solving
θ̂
θ̂+1
= X̄, we have the
method of moments estimator of θ is
X̄
.
1 − X̄
θ̂ =
b) The log likelihood function of θ is
n
Y
l(θ) = log(
f (xi |θ))
i=1
n
Y
= log(
θxθ−1
)
i
i=1
= log(θn (
n
Y
xi )θ−1
i=1
= n log θ + (θ − 1)
n
X
log xi .
i=1
Then
∂l(θ)
∂θ
=
n
θ
+
Pn
i=1
log xi . Solving
∂l(θ)
∂θ
θ̂ =
= 0, we have
n
−
Pn
i=1
log xi
.
Because the second derivative of l(θ) is − θn2 , which is always negative, the solution above is
a maximizer of l(θ). Therefore θ̂ =
−
Pn n
i=1
log xi
is the MLE of θ.
c) By the large sample theory, we know the asymptotic variance of the MLE is the inverse
of the fisher information, which is
i−1
∂2
l(θ)
∂θ2
n −1
=
− E(− 2 )
θ
n −1 θ2
= ( 2) =
θ
n
I(θ)−1 =
−E
h
4
5.
a) The joint probability mass function of the six random variables, X1 , . . . , X6 , is given
by
6
P
f (x1 , x2 , x3 , x4 , x5 , x6 |θ) =
e−6θ · θi=1
6
Q
xi
.
xi !
i=1
The prior distribution of θ is given by Gamma(2, 1.5),
Γ2 2−1 −1.5θ
θ e
.
1.52
π(θ) =
Thus for the posterior distribution we have the following:
π(θ|x) ∝ f (x|θ) · π(θ)
6
P
∝ e−6θ · θi=1
xi
· θ2−1 e−1.5θ
6
P
∝ θi=1
P
6
which is the kernel of the Gamma
P
6
distribution of θ is Gamma
xi +2−1
e−7.5θ ,
xi + 2 , 7.5 distribution. Therefore, the posterior
i=1
xi + 2 , 7.5 .
i=1
b) A Bayes estimator is the mean of the posterior distribution. Therefore, a Bayes
estimator of θ can be
6
P
θ̂ =
Xi + 2
i=1
7.5
c) A 90% credible interval for θ can be given by the 5th and the 95th percentile of the
P
6
Gamma
i=1
xi + 2 , 7.5 distribution.
5
6.
a) The common probability density function of X1 , X2 , · · · , X5 is given by
f (x) = θe−θx , x ≥ 0.
To derive the distribution of Y , we calculate
P (Y > y) = P (X1 > y, X2 > y, · · · , X5 > y)
= [P (Xi > y)]5
"
=
R∞
#5
θe−θx dx
= e−5θy
y
Hence fY (y) = 5θe−5θy .
b) The power function of the test is given as follows:
π(θ) = Pθ (rejecting H0 )
= Pθ (Y > 18)
= e−5θ·18 = e−90θ
Note that
θ1 > θ2 ⇒ −90θ1 < −90θ2
⇒ e−90θ1 < e−90θ2
Therefor the power function is a decreasing function of θ.
c)The size (the significance level) of the test is:
sup π(θ) = sup e−90θ = e−90/20 = 0.011109
θ∈Ω0
1
θ≥ 20