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The moment generating function ф(t) of random variable X is defined for all values of t by et*p(x), if X is discrete e...
The moment generating function ф(t) of random variable X is defined for all values of t by et*p(x), if X is discrete e f (x)dx, if X is continus (a) Find the moment generating function of a Binomial random variable X with parameters n (the total number of trials) and p (the probability of success). (b) If X and Y are independent Binomial random variables with parameters (n1 p) and (n2, p), respectively, then what is the distribution of X...
For each n, let Xn be a binomial random variable with n trials and probability of...
For each n, let Xn be a binomial random variable with n trials and probability of success p Yn Use the Weak Law of Large Numbers to show that is a consistent estimator of p. (b) Explain why it follows from (a) that (1 isaconsistent estimator of p(1 -p) and that 1) is a consistent estimator of p(-P) 7l V n
The moment generating function (MGF) for a random variable X is: Mx (t) = E[e'X]. Onc...
The moment generating function (MGF) for a random variable X is: Mx (t) = E[e'X]. Onc useful property of moment generating functions is that they make it relatively casy to compute weighted sums of independent random variables: Z=aX+BY M26) - Mx(at)My (Bt). (A) Derive the MGF for a Poisson random variable X with parameter 1. (B) Let X be a Poisson random variable with parameter 1, as above, and let y be a Poisson random variable with parameter y. X...
Let X1, X2, .. , Xn be a random sample of size n from a geometric distribution with pmf =0.75 . 0.25z-1, f(x) X-1.2.3....
Let X1, X2, .. , Xn be a random sample of size n from a geometric distribution with pmf =0.75 . 0.25z-1, f(x) X-1.2.3. ) Let Zn 3 n n-2ућ. Find Mz, (t), the mgf of Žn. Then find the limiting mgf limn→oo MZm (t). What is the limiting distribution of Z,'?
Let X1, X2, .. , Xn be a random sample of size n from a geometric distribution with pmf =0.75 . 0.25z-1, f(x) X-1.2.3.
) Let Zn 3...
7. Let X a be random variable with probability density function given by -1 < x < 1 fx(x) otherwise (a) Find the...
7. Let X a be random variable with probability density function given by -1 < x < 1 fx(x) otherwise (a) Find the mean u and variance o2 of X (b) Derive the moment generating function of X and state the values for which it is defined (c) For the value(s) at which the moment generating function found in part (b) is (are) not defined, what should the moment generating function be defined as? Justify your answer (d) Let X1,...
Only 1-6) N(4,) "x.xx be a random sample from variance, respectively. In order to show that...
Only 1-6)
N(4,) "x.xx be a random sample from variance, respectively. In order to show that and let X and S be sample mean and sample 1. Let and 5 are independent, tollow the steps below. 1-1) Use the change of variable technique =nx-x,- x and show the joint pdf of ,X,,X is (n-1) n- exp f(,x) 20 2a av2 Use Jacobian for n x n variable transformation 1-2) Use the fact that N(u,a n), and show that the conditional...
4. Fix > 0. For n > λ let Xn be Geometric(A/n). Show that X n/n...
4. Fix > 0. For n > λ let Xn be Geometric(A/n). Show that X n/n converges in distribution to an Exponential(A). (Hint: again, compute moment generating functions.)
Let X1, X2, ....,. Xn, be a set of independent random variables, each distributed as a...
Let X1, X2, ....,. Xn, be a set of independent random variables, each distributed as a normal random variable with parameters μί and σ. Let х, ai Use properties of moment generating functions to determine the distribution of Y, meaning: find the type of distribution we get, and its expected value and variance
(1 point) In Unit 3, I claimed that the sum of independent, identically distributed exponential r...
(1 point) In Unit 3, I claimed that the sum of independent, identically distributed exponential random variables is a gamma random variable. Now that we know about moment generating functions, we can prove it. Let X be exponential with mean A 4. The density is 4 a) Find the moment generating function of X, and evaluate at t 3.9 The mgf of a gamma is more tedious to find, so l'll give it to you here. Let W Gamma(n, A...
Let X1, X2, ...... Xn be a random sample of size n from EXP() distribution , ,...
Let X1, X2, ......
Xn be a random sample of size n from
EXP()
distribution ,
, zero , elsewhere.
Given, mean of distribution
and variances
and mgf
a) Show that the mle
for
is
. Is
a consistent estimator for
?
b)Show that Fisher information
. Is mle of
an efficiency estimator for
? why or why not? Justify your answer.
c) what is the mle estimator of
? Is the mle of
a consistent estimator for
?
d) Is...
The moment generating function ф(t) of random variable X is defined for all values of t by et*p(x), if X is discrete e f (x)dx, if X is continus (a) Find the moment generating function of a Binomial random variable X with parameters n (the total number of trials) and p (the probability of success). (b) If X and Y are independent Binomial random variables with parameters (n1 p) and (n2, p), respectively, then what is the distribution of X...
For each n, let Xn be a binomial random variable with n trials and probability of success p Yn Use the Weak Law of Large Numbers to show that is a consistent estimator of p. (b) Explain why it follows from (a) that (1 isaconsistent estimator of p(1 -p) and that 1) is a consistent estimator of p(-P) 7l V n
The moment generating function (MGF) for a random variable X is: Mx (t) = E[e'X]. Onc useful property of moment generating functions is that they make it relatively casy to compute weighted sums of independent random variables: Z=aX+BY M26) - Mx(at)My (Bt). (A) Derive the MGF for a Poisson random variable X with parameter 1. (B) Let X be a Poisson random variable with parameter 1, as above, and let y be a Poisson random variable with parameter y. X...
Let X1, X2, .. , Xn be a random sample of size n from a geometric distribution with pmf =0.75 . 0.25z-1, f(x) X-1.2.3. ) Let Zn 3 n n-2ућ. Find Mz, (t), the mgf of Žn. Then find the limiting mgf limn→oo MZm (t). What is the limiting distribution of Z,'?
Let X1, X2, .. , Xn be a random sample of size n from a geometric distribution with pmf =0.75 . 0.25z-1, f(x) X-1.2.3.
) Let Zn 3...
7. Let X a be random variable with probability density function given by -1 < x < 1 fx(x) otherwise (a) Find the mean u and variance o2 of X (b) Derive the moment generating function of X and state the values for which it is defined (c) For the value(s) at which the moment generating function found in part (b) is (are) not defined, what should the moment generating function be defined as? Justify your answer (d) Let X1,...
Only 1-6)
N(4,) "x.xx be a random sample from variance, respectively. In order to show that and let X and S be sample mean and sample 1. Let and 5 are independent, tollow the steps below. 1-1) Use the change of variable technique =nx-x,- x and show the joint pdf of ,X,,X is (n-1) n- exp f(,x) 20 2a av2 Use Jacobian for n x n variable transformation 1-2) Use the fact that N(u,a n), and show that the conditional...
4. Fix > 0. For n > λ let Xn be Geometric(A/n). Show that X n/n converges in distribution to an Exponential(A). (Hint: again, compute moment generating functions.)
Let X1, X2, ....,. Xn, be a set of independent random variables, each distributed as a normal random variable with parameters μί and σ. Let х, ai Use properties of moment generating functions to determine the distribution of Y, meaning: find the type of distribution we get, and its expected value and variance
(1 point) In Unit 3, I claimed that the sum of independent, identically distributed exponential random variables is a gamma random variable. Now that we know about moment generating functions, we can prove it. Let X be exponential with mean A 4. The density is 4 a) Find the moment generating function of X, and evaluate at t 3.9 The mgf of a gamma is more tedious to find, so l'll give it to you here. Let W Gamma(n, A...
Let X1, X2, ......
Xn be a random sample of size n from
EXP()
distribution ,
, zero , elsewhere.
Given, mean of distribution
and variances
and mgf
a) Show that the mle
for
is
. Is
a consistent estimator for
?
b)Show that Fisher information
. Is mle of
an efficiency estimator for
? why or why not? Justify your answer.
c) what is the mle estimator of
? Is the mle of
a consistent estimator for
?
d) Is...
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