Suppose that x1, . . . , xn are a random sample having probability density function f(x; θ) = (θ + 1)x^θ , 0 < x < 1. (1) Here the parameter θ > 0.
(a) Show that P(Xi ≤ b; θ) = b^(θ+1) for f(x; θ) given in (1).
(b) Suppose now that because of a recurring computer glitch in storing the observations, only a random subset of the xi are observed. For the rest of the observations, it is only known that xi ≤ 1/2. Let δi = 1 or 0 according to whether xi is observed or not and let d = P i δi denote the number of xi observed; thus n − d of the xi are only known to satisfy xi ≤ 1/2. Determine the likelihood, L(θ), and a 2-dimensional sufficient statistic. Note that d is a random quantity dependent on the data. You can use the result of (b) even if you were unable to show it.
Suppose that x1, . . . , xn are a random sample having probability density function...
. Suppose that x1, . . . , xn are a random sample having probability density function f(x; θ) = (θ + 1)x^θ , 0 < x < (1) Here the parameter θ > 0. (a) Determine the log-likelihood, l(θ), and a 1-dimensional sufficient statistic. (b) Show that P(Xi ≤ b; θ) = b θ+1 for f(x; θ) given in (1). (c) Suppose now that because of a recurring computer glitch in storing the observations, only a random subset of...
1. Suppose that xi, ,xn are a random sample having probability density function Here the parameter θ > 0. (a) Determine the log likelibood, 10), and a 1- dimensional (a) Determine the log-likelihood, l(0), and a 1-dimensional sufficient statistic. (b) Show that P(XiS b;0) = +1 for f(x:0) given in (1). (c) Suppose now that because of a recurring computer glitch in storing the observations, only a random subset of the r, are observed. For the rest of the observations,...
1. Suppose that ri,...,In are a random sample having probability density function Here the parameter 0 >0 (a) Determine the log-likelihood, l(0), and a 1-dimensional sufficient statistic. (b) Show that P(Xi b;0)-μ+1 for f(x;0) given in (1). (c) Suppose now that because of a recurring computer glitch in storing the observations, only a random subset of the ai are observed. For the rest of the observations, it is only known that z; < 1/2. Let δί-1 or 0 according to...
1. Suppose that r,., n are a random sample having probability density function Here the parameter θ > 0. (a) Determine the log-likelihood, (0), and a 1-dimensional sufficient statistic. (b) Show that P(X, S b:0) for f(r;0) given in (1) (c) Suppose now that because of a recurring computer glitch in storing the observations, only a +1 for f(r; random subset of the x, are observed. For the rest of the observations, it is only known that z; < 1/2....
1. Suppose that ri,.., n are a random sample having probability density function Here the paran neter θ > 0. (a) Determine the log-likelihood, (0), and a 1-dimensional sufficient statistio (b) Show that PX, b:0) =&+1 for f(z:0) given in (1). (c) Suppose now that because of a recurring computer glitch in storing the observations, only a random subset of the ai are observed. For the rest of the observations, it is only known that Xi < 1/2. Let δί...
Let X1, . . . , Xn be a random sample from a population with density 8. Let Xi,... ,Xn be a random sample from a population with density 17 J 2.rg2 , if 0<、〈릉 0 , if otherwise ( a) Find the maximum likelihood estimator (MLE) of θ . (b) Find a sufficient statistic for θ (c) Is the above MLE a minimal sufficient statistic? Explain fully.
A random variable X has probability density function f(x)=(a-1)x^(-a),for x>=1. (a) For independent observations x1,...,xn show that the log-likelihood is given by, l(a;x1,...,xn)=nlog(a-1)-a (b) Hence derive an expression for the maximum likelihood estimate for ↵. (c) Suppose we observe data such that n = 6 and 6 i=1 log(xi) = 12. Show that the associated maximum likelihood estimate for ↵ is given by aˆ ↵ =1 .5. logri We were unable to transcribe this image
2. (a) Suppose that x1,... , Vn are a random sample from a gamma distribution with shape parameter α and rate parameter λ, Here α > 0 and λ > 0. Let θ-(α, β). Determine the log-likelihood, 00), and a 2-dimensional sufficient statistic for the data (b) Suppose that xi, ,Xn are a random sample from a U(-9,0) distribution. f(x; 8) otherwise Here θ > 0, Determine the likelihood, L(0), and a one-dimensional sufficient statistic. Note that the likelihood should...
Let XI, X2, , Xn İs a random sample from the probability density function Use factorization theorem to show that X(1) = min(X1 , . . . , Xn) is sufficient for θ Is X(1) minimal sufficient for θ? a. b.
Advanced Statistics, I need help with (c) and (d) 2. Let X1, X2, ..., Xn be a random sample from a Bernoulli(6) distribution with prob- ability function Note that, for a random variable X with a Bernoulli(8) distribution, E [X] var [X] = θ(1-0) θ and (a) Obtain the log-likelihood function, L(0), and hence show that the maximum likelihood estimator of θ is 7l i= I (b) Show that dE (0) (c) Calculate the expected information T(e) EI()] (d) Show...