Homework Answers
Parametric model: (X , BX , Pθ), Pθ ∈ P = {Pθ|θ ∈ Θ}
where Θ = H0+˙ H1
X = K+˙ A : K: critical region = rejection region / A: acceptance
region
A decision rule d, where
d(x) =
dK , if x ∈ K
dA , if x ∈ A
is called a non randomised test. One tries to choose K in such a
way that
the number of wrong decisions becomes as small as possible. We
distinguish:
Type I error: H0 is correct, but is rejected (decision dK).
Type II error: H1 is correct, but decision for H0 (decision
dA).
If events A and B are not independent,
then the probability of the intersection of A and
B (the probability that both events occur) is defined
by
P(A and B) = P(A)P(B|A).
From this definition, the conditional probability P(B|A) is easily obtained by dividing by P(A):
Add Answer to:
Let (X1.x2, Xn, X. X, . Ж) be mutuolyindependent Berni rv's, with (it s ond A ore nknown puramete...
8. Let X, X2, , xn all be be distributed Normal(μ, σ2). Let X1, X2, ,...
8. Let X, X2, , xn all be be distributed Normal(μ, σ2). Let X1, X2, , xn be mu- tually independent. a) Find the distribution of U-Σǐ! Xi for positive integer m < n b) Find the distribution of Z2 where Z = M Hint: Can the solution from problem #2 be applied here for specific values of a and b?
Please answer question (a) X1 - X X2 – Å a. Let X1, ..., Xn i.i.d....
Please answer question (a)
X1 - X X2 – Å a. Let X1, ..., Xn i.i.d. random variables with X; ~ N(u, o). Express the vector in the | Xn – form AX and find its mean and variance covariance matrix. Show some typical elements of the vari- ance covariance matrix. b. Refer to question (a). The sample variance is given by S2 = n11 21–1(X; – X)2, which can be ex- pressed as S2 = n1X'(I – 111')X (why?)....
6.1.10. Let X1, X2..... Xn be a random sample from a N(0,0%) distribution, where o? is...
6.1.10. Let X1, X2..... Xn be a random sample from a N(0,0%) distribution, where o? is fixed but-X <O<O. (a) Show that the mle ofis X. (b) If is restricted by 0 < < oc, show that the mie of 8 is 8 = max{0,X}.
8. Let X1, X2,...,X, U(0,1) random variables and let M = max(X1, X2,...,xn). - Show that...
8. Let X1, X2,...,X, U(0,1) random variables and let M = max(X1, X2,...,xn). - Show that M. 1, that is, M, converges in probability to 1 as n o . - Show that n(1 - M.) Exp(1), that is, n(1 - M.) converges in distribution to an exponential r.v. with mean 1 as n .
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Let X = x1, x2, . . . , xn be a sequence of n integers. A sub-sequence of X is a sequence obtained from X by deleting some elements. Give an O(n2) algorithm to find the longest monotonically increasing sub-sequences of X.
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability...
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability density function f(x; θ) = (1/2)e-11-01, o < x < oo,-oo < θ < oo. Find the NILE θ.
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability...
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability density function f(x; θ) = (1/2)e-11-01, o < x < oo,-oo < θ < oo. Find the NILE θ.
9 Let Xi, X2, ..., Xn be an independent trials process with normal density of mean 1 and variance 2. Find the moment generating function for (a) X (b) S2 =X1 + X2 . (c) Sn=X1+X2 + . . . + Xn. (d) An...
9 Let Xi, X2, ..., Xn be an independent trials process with normal density of mean 1 and variance 2. Find the moment generating function for (a) X (b) S2 =X1 + X2 . (c) Sn=X1+X2 + . . . + Xn. (d) An -Sn/n
9 Let Xi, X2, ..., Xn be an independent trials process with normal density of mean 1 and variance 2. Find the moment generating function for (a) X (b) S2 =X1 + X2 . (c)...
Let X1, X2, , xn are independent random variables where E(X)-? and Var(X) ?2 for all...
Let X1, X2, , xn are independent random variables where E(X)-? and Var(X) ?2 for all i = 1, 2, , n. Let X-24-xitx2+--+Xy variables. is the average of those random Find E(X) and Var(X).
3. Let X1, X2, . . . , Xn be a random sample from a distribution...
3. Let X1, X2, . . . , Xn be a random sample from a distribution with the probability density function f(x; θ) (1/02)Te-x/θ. O < _T < OO, 0 < θ < 00 . Find the MLE θ
8. Let X, X2, , xn all be be distributed Normal(μ, σ2). Let X1, X2, , xn be mu- tually independent. a) Find the distribution of U-Σǐ! Xi for positive integer m < n b) Find the distribution of Z2 where Z = M Hint: Can the solution from problem #2 be applied here for specific values of a and b?
Please answer question (a)
X1 - X X2 – Å a. Let X1, ..., Xn i.i.d. random variables with X; ~ N(u, o). Express the vector in the | Xn – form AX and find its mean and variance covariance matrix. Show some typical elements of the vari- ance covariance matrix. b. Refer to question (a). The sample variance is given by S2 = n11 21–1(X; – X)2, which can be ex- pressed as S2 = n1X'(I – 111')X (why?)....
6.1.10. Let X1, X2..... Xn be a random sample from a N(0,0%) distribution, where o? is fixed but-X <O<O. (a) Show that the mle ofis X. (b) If is restricted by 0 < < oc, show that the mie of 8 is 8 = max{0,X}.
8. Let X1, X2,...,X, U(0,1) random variables and let M = max(X1, X2,...,xn). - Show that M. 1, that is, M, converges in probability to 1 as n o . - Show that n(1 - M.) Exp(1), that is, n(1 - M.) converges in distribution to an exponential r.v. with mean 1 as n .
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability density function f(x; θ) = (1/2)e-11-01, o < x < oo,-oo < θ < oo. Find the NILE θ.
4. Let X1, X2, ..., Xn be a random sample from a distribution with the probability density function f(x; θ) = (1/2)e-11-01, o < x < oo,-oo < θ < oo. Find the NILE θ.
9 Let Xi, X2, ..., Xn be an independent trials process with normal density of mean 1 and variance 2. Find the moment generating function for (a) X (b) S2 =X1 + X2 . (c) Sn=X1+X2 + . . . + Xn. (d) An -Sn/n
9 Let Xi, X2, ..., Xn be an independent trials process with normal density of mean 1 and variance 2. Find the moment generating function for (a) X (b) S2 =X1 + X2 . (c)...
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3. Let X1, X2, . . . , Xn be a random sample from a distribution with the probability density function f(x; θ) (1/02)Te-x/θ. O < _T < OO, 0 < θ < 00 . Find the MLE θ
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