Recall from the slides that the Gamma distribution can be reparameterized using the two parameters The pdf looks like t...
Recall that the binomial distribution with parameters n and p is governed by Let n be some known number, say n-1000. Then the pmf is 1000 f (y) p)1000-, Write this as an exponential family of the form 1000 fp (v)-h() exp (n (p)T(v) p) where h () then enter η (p) T (y) and B (p) below. To get unique answers, use 1 as the coefficient of y in T (s). T (v)- B(p) Recall that the binomial distribution...
Consider a random sample of size n from a two-parameter exponential dist EXP(e, n). Recall from Exercise 12 that X 1 ., and X are jointly sufficient for O Because Xi:n is complete and sufficient for η for each fixed value of θ, argue from 104.7 that X, and T X1:n X are stochastically independent. ibution, X, 30. Theor (a) Find the MLE θ of θ. (b) Find the UMVUE of η. (c) Show that the conditional pdf of Xi:n...
I. Let X be a random sample from an exponential distribution with unknown rate parameter θ and p.d.f (a) Find the probability of X> 2. (b) Find the moment generating function of X, its mean and variance. (c) Show that if X1 and X2 are two independent random variables with exponential distribution with rate parameter θ, then Y = X1 + 2 is a random variable with a gamma distribution and determine its parameters (you can use the moment generating...
he second form for one-parameter exponential family distributions, introduced during lecture 09.1, was Jy (y | θ) = b(y)ec(0)t(y)-d(0) Let η = c(0). If c is an invertible function, we can rewrite (1) as where η is called the natural, or canonical, parameter and K(n) = d(C-1(n)). Expression (2) is referred to as the canonical representation of the exponential family distribution (a) Function κ(η) is called the log-normalizer: it ensures that the distribution fy(y n) integrates to one. Show that,...
Problem 6. Gamma distribution is defined by its density function T(a) when t 0 and 0 otherwise. Here, θ is unknown parameter, a is a known param- eter Г(a) is a normalization constant (like in chi-square distribution discussed in class, see also p. 99 of the textbook) a) Check that Gamma distribution can be written in the exponential form. b) Use this fact to give a sufficient statistic for θ (you can do it with no additional computations after part...
Recall that the exponential distribution with parameter A > 0 has density g (x) Ae, (x > 0). We write X Exp (A) when a random variable X has this distribution. The Gamma distribution with positive parameters a (shape), B (rate) has density h (x) ox r e , (r > 0). and has expectation.We write X~ Gamma (a, B) when a random variable X has this distribution Suppose we have independent and identically distributed random variables X1,..., Xn, that...
Consider the simplified Bayesian model for normal data The joint posterior pdf is ful, σ21 x)a(σ2,-/2-1 expl_jy.tx, _aPI The marginal posterior pdfs of μ and σ 2 can be obtained by integrating out the other variable (8.30) y@1 x) α (σ2)-m;,-1/2 expl-- Σ.-tri-x)2 (8.31) d. Let q1 and q2 be they/2 and 1-y/2 quantiles of (8.31). Show that the 1-γ credible interval (gi,q2) is identical to the classic confidence interval (5.19) (with ar replaced by y). Hence, a (1-α) stochastic...
Return to the original model. We now introduce a Poisson intensity parameter X for every time point and denote the parameter () that gives the canonical exponential family representation as above by θ, . We choose to employ a linear model connecting the time points t with the canonical parameter of the Poisson distribution above, i.e., n other words, we choose a generalized linear model with Poisson distribution and its canonical link function. That also means that conditioned on t,...
(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...
In this problem, we will model the likelihood of a particular client of a financial firm defaulting on his or her loans based on previous transactions. There are only two outcomes, "Yes" or "No", depending on whether the client eventually defaults or not. It is believed that the client's current balance is a good predictor for this outcome, so that the more money is spent without paying, the more likely it is for that person to default. For each x,...