Question

Let X = (X1, X2) T be a two-dim random vector, and (R, Θ) be its...

Let X = (X1, X2) T be a two-dim random vector, and (R, Θ) be its polar coordinates, i.e. X1 = R cos(Θ) and X2 = R sin(Θ). Show that X is spherically symmetric if and only if R and Θ are independent, and Θ ∼Uniform(0, 2π).

Homework Answers

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Let x1 and x2 be random vektor such that x1+x2 and x1-x2 are independent and each...
Let x1 and x2 be random vektor such that x1+x2 and x1-x2 are independent and each normal distributed. Show that the random variable vector X=(x1,x2)' is distributed bivariate normal and determine its expected value and covariance matrix
Let X1, X2, . . . , Xn be iid random variables with pdf f(x|θ) =...
Let X1, X2, . . . , Xn be iid random variables with pdf f(x|θ) = θx^(θ−1) , 0 < x < 1, θ > 0. Is there an unbiased estimator of some function γ(θ), whose variance attains the Cramer-Rao lower bound?
Let Θ ∼ Unif.([0, 2π]) and consider X = cos(Θ) and Y = sin(Θ). Can you...
Let Θ ∼ Unif.([0, 2π]) and consider X = cos(Θ) and Y = sin(Θ). Can you find E[X], E[Y], and E[XY]? clearly, x and y are not independent I think E[X] = E[Y] = 0 but how do you find E[XY]?
Let θ > 1 and let X1, X2, ..., Xn be a random sample from the...
Let θ > 1 and let X1, X2, ..., Xn be a random sample from the distribution with probability density function f(x; θ) = 1/xlnθ , 1 < x < θ. c) Let Zn = nlnY1. Find the limiting distribution of Zn. d) Let Wn = nln( θ/Yn ). Find the limiting distribution of Wn.
6. Let θ > 1 and let X1, X2, ..., Xn be a random sample from...
6. Let θ > 1 and let X1, X2, ..., Xn be a random sample from the distribution with probability density function f(x; θ) = 1/(xlnθ) , 1 < x < θ. a) Obtain the maximum likelihood estimator of θ, ˆθ. b) Is ˆθ a consistent estimator of θ? Justify your answer.
Let X1, X2, · · · , Xn be a random sample from the distribution, f(x;...
Let X1, X2, · · · , Xn be a random sample from the distribution, f(x; θ) = (θ + 1)x^ −θ−2 , x > 1, θ > 0. Find the maximum likelihood estimator of θ based on a random sample of size n above
6. Let X1, X2, ..., Xn be a random sample of a random variable X from...
6. Let X1, X2, ..., Xn be a random sample of a random variable X from a distribution with density f (x)  ( 1)x 0 ≤ x ≤ 1 where θ > -1. Obtain, a) Method of Moments Estimator (MME) of parameter θ. b) Maximum Likelihood Estimator (MLE) of parameter θ. c) A random sample of size 5 yields data x1 = 0.92, x2 = 0.7, x3 = 0.65, x4 = 0.4 and x5 = 0.75. Compute ML Estimate...
Let X1, X2, · · · , Xn be a random sample from an exponential distribution...
Let X1, X2, · · · , Xn be a random sample from an exponential distribution f(x) = (1/θ)e^(−x/θ) for x ≥ 0. Show that likelihood ratio test of H0 : θ = θ0 against H1 : θ ≠ θ0 is based on the statistic n∑i=1 Xi.
Let X1, X2 · · · , Xn be a random sample from the distribution with...
Let X1, X2 · · · , Xn be a random sample from the distribution with PDF, f(x) = (θ + 1)x^θ , 0 < x < 1, θ > −1. Find an estimator for θ using the maximum likelihood
Let X1, X2, . . . , Xn be a random sample from a population following...
Let X1, X2, . . . , Xn be a random sample from a population following a uniform(0,2θ) (a) Show that if n is large, the distribution of Z =( X − θ) / ( θ/√ 3n) is approximately N(0, 1). (b) We can estimate θ by X(sample mean) and define W = (X − θ ) / (X/√ 3n) which is also approximately N(0, 1) for large n. Derive a (1 − α)100% confidence interval for θ based on...