Suppose that (X,Y) has a bivariate normal distribution with parameters μ x , μ y , σ x , σ , ρ . a. Show that ( X − μ x σ x , Y − μ y σ y ) has a bivariate normal distribution with parameters 0,1,0,1, ρ . b. What is the joint distribution of ( a X + b , c Y + d ) .
Suppose that (X,Y) has a bivariate normal distribution with parameters μ x , μ y , σ x , σ , ρ . a. Show that ( X − μ x σ x , Y − μ y σ y ) has a bivariate normal distribution with parameters 0,1,0,1, ρ . b. What is the joint distribution of ( a X + b , c Y + d ) .
Solution Summary: The author explains the bivariate normal distribution of the given function. The joint density function mathrmfleft is a random variable.
Suppose that (X,Y) has a bivariate normal distribution with parameters
μ
x
,
μ
y
,
σ
x
,
σ
,
ρ
.
a. Show that
(
X
−
μ
x
σ
x
,
Y
−
μ
y
σ
y
)
has a bivariate normal distribution with parameters 0,1,0,1,
ρ
.
b. What is the joint distribution of
(
a
X
+
b
,
c
Y
+
d
)
.
Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.
-xx0.
B2 If Xfx(x) find the MGF in the case that
fx(x) =
-
1
28
exp{-|x − a\/ẞ},
Use the MGF to compute E(X) and Var(X).
B3 Consider X ~ Bern(p)
(a) Find Mx(t), the moment generating function of X.
iid
(b) If X1,..., Xn
Bern(p), find the MGF, say My (t) of
n
Y =
ΣΧ
(c) Using the fact that
i=1
n
lim (1
(1+2)"=
N→X
= e²
find limn→∞ My (t) in the case that p satisfies limn→∞ np = λ, say.
(d) State the distribution of Y in the case that n is not large, and the distribution of Y in
the limiting case described in the question.
B1 The density of the x2 distribution is given in the notes as
1
F(§)2/2
(x)=()2/21
x/2-1/2, if x > 0, and
e
where I(t)=√xt-¹e dx is the gamma function.
otherwise,
Find the point at which o(a) has its maximum, i.e. find arg max, o, (x)
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