Moment of method estimation
Suppose a random sample X1, X2, ..., Xn is drawn from a distribution believed to have the following probability density function:
Find the first moment of X supposing that the parameter α is known. Use it to find the method of moment estimator for unknown parameter β. You may find it easier to use the notation m1, m2, ..., mk to denote the first sample moment, second sample moment, ..., kthsample moment, respectively. Is the estimator you derived unbiased?
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Assume that you have random variable X with pdf or pmf f(x; θ1, . . ....
Assume that you have random variable X with pdf or pmf f(x; θ1, . . . , θk). Let X1, . . . , Xn be a random sample from X. Then Mj = (1/n)Xn i=1 (Xi)j is known as the j-th sample moment of the sample. The moment estimators of θ1, . . . , θk, denoted by ˜θ1, . . . , ˜θk, are the values of θ1, . . . , θk which solve the k equations...
Suppose that x1, . . . , xn are a random sample from a B(α, β)...
Suppose that x1, . . . , xn are a random sample from a B(α, β) distribution: f(x; α, β) = x^(α-1) (1-x)^(β-1) Here E[X] = α/(α + β) and E[X^2 ] = ((α + 1)α)/{(α + β + 1)(α + β)}. (a) Show that the method of moments, using the first two moments, gives the equations 0 = α(1 − m1 ) − βm1 m1 − m2 = α(m2 − m1 ) + βm2 (b) Determine the method of moments...
Let the random sample X1, . . . , Xn be taken from the Binomial distribution...
Let the random sample X1, . . . , Xn be taken from the Binomial distribution with parameter θ, which is unknown and must be estimated. Let the prior distribution of θ be the beta distribution with known parameters α > 0 and β > 0. Find the Bayes risk and the Bayes estimator using squared error loss. estimator of θ.
QUESTION 5 Suppose that Yı, Y2,.., Yn independent variables such that where β is an unknown...
QUESTION 5 Suppose that Yı, Y2,.., Yn independent variables such that where β is an unknown parameter, X1, x2-.., xn are known real numbers, and el,e2 independent random errors each with a normal distribution with mean 0 and variance ơ2 ,en are (a) Show that is an unbiased estimator of β. What is the variance of the estimator? (b) Given that the probability density function of Y is elsewhere, show that the maximum likelihood estimator of β is not the...
Let X1,X2,...,Xn be iid exponential random variables with unknown mean β. (b) Find the maximum likelihood...
Let X1,X2,...,Xn be iid exponential random variables with unknown mean β. (b) Find the maximum likelihood estimator of β. (c) Determine whether the maximum likelihood estimator is unbiased for β. (d) Find the mean squared error of the maximum likelihood estimator of β. (e) Find the Cramer-Rao lower bound for the variances of unbiased estimators of β. (f) What is the UMVUE (uniformly minimum variance unbiased estimator) of β? What is your reason? (g) Determine the asymptotic distribution of the...
(4 points) Let Xi, , Xn denote a randon sample from a Normal N(μ, 1) distribution, with 11 as the...
please answer with full soultion. with explantion.
(4 points) Let Xi, , Xn denote a randon sample from a Normal N(μ, 1) distribution, with 11 as the unknown parameter. Let X denote the sample mean. (Note that the mean and the variance of a normal N(μ, σ2) distribution is μ and σ2, respectively.) Is X2 an unbiased estimator for 112? Explain your answer. (Hint: Recall the fornula E(X2) (E(X)Var(X) and apply this formula for X - be careful on the...
1. (40) Suppose that X1, X2, Xn forms an independent and identically distributed sample from a normal distribution with mean μ and variance σ2, both unknown: 2nơ2 (a) Derive the sample variance, S2,...
1. (40) Suppose that X1, X2, Xn forms an independent and identically distributed sample from a normal distribution with mean μ and variance σ2, both unknown: 2nơ2 (a) Derive the sample variance, S2, for this random sample. (b) Derive the maximum likelihood estimator (MLE) of μ and σ2 denoted μ and σ2, respectively. (c) Find the MLE of μ3 (d) Derive the method of moment estimator of μ and σ2, denoted μΜΟΜΕ and σ2MOME, respectively (e) Show that μ and...
You are given the following probability density function, φ2(x), for the cosine of the surface an...
You are given the following probability density function, φ2(x), for the cosine of the surface angle, X, of a laser etching tool. The distribution function has one parameter, α, and one constant, c. PX (x) =竺2-1 a) What is the value of the constant, c? b) What is the moment estimator for α? c) Explain how you can determine if this moment estimator is unbiased. d) Let S (ai... 2s) denote a random sample of sample size n-24 with sample...
Q3 Suppose X1, X2, ..., Xn are i.i.d. Poisson random variables with expected value ). It...
Q3 Suppose X1, X2, ..., Xn are i.i.d. Poisson random variables with expected value ). It is well-known that X is an unbiased estimator for l because I = E(X). 1. Show that X1+Xn is also an unbiased estimator for \. 2 2. Show that S2 (Xi-X) = is also an unbaised esimator for \. n-1 3. Find MSE(S2). (We will need two facts) E com/questions/2476527/variance-of-sample-variance) 2. Fact 2: For Poisson distribution, E[(X – u)4] 312 + 1. (See for...
Let X1,X2,...,Xn denote a random sample from the Rayleigh distribution given by f(x) = ...
Let X1,X2,...,Xn denote a random sample from the Rayleigh distribution given by f(x) = (2x θ)e−x2 θ x > 0; 0, elsewhere with unknown parameter θ > 0. (A) Find the maximum likelihood estimator ˆ θ of θ. (B) If we observer the values x1 = 0.5, x2 = 1.3, and x3 = 1.7, find the maximum likelihood estimate of θ.
QUESTION 5 Suppose that Yı, Y2,.., Yn independent variables such that where β is an unknown parameter, X1, x2-.., xn are known real numbers, and el,e2 independent random errors each with a normal distribution with mean 0 and variance ơ2 ,en are (a) Show that is an unbiased estimator of β. What is the variance of the estimator? (b) Given that the probability density function of Y is elsewhere, show that the maximum likelihood estimator of β is not the...
please answer with full soultion. with explantion.
(4 points) Let Xi, , Xn denote a randon sample from a Normal N(μ, 1) distribution, with 11 as the unknown parameter. Let X denote the sample mean. (Note that the mean and the variance of a normal N(μ, σ2) distribution is μ and σ2, respectively.) Is X2 an unbiased estimator for 112? Explain your answer. (Hint: Recall the fornula E(X2) (E(X)Var(X) and apply this formula for X - be careful on the...
1. (40) Suppose that X1, X2, Xn forms an independent and identically distributed sample from a normal distribution with mean μ and variance σ2, both unknown: 2nơ2 (a) Derive the sample variance, S2, for this random sample. (b) Derive the maximum likelihood estimator (MLE) of μ and σ2 denoted μ and σ2, respectively. (c) Find the MLE of μ3 (d) Derive the method of moment estimator of μ and σ2, denoted μΜΟΜΕ and σ2MOME, respectively (e) Show that μ and...
You are given the following probability density function, φ2(x), for the cosine of the surface angle, X, of a laser etching tool. The distribution function has one parameter, α, and one constant, c. PX (x) =竺2-1 a) What is the value of the constant, c? b) What is the moment estimator for α? c) Explain how you can determine if this moment estimator is unbiased. d) Let S (ai... 2s) denote a random sample of sample size n-24 with sample...
Q3 Suppose X1, X2, ..., Xn are i.i.d. Poisson random variables with expected value ). It is well-known that X is an unbiased estimator for l because I = E(X). 1. Show that X1+Xn is also an unbiased estimator for \. 2 2. Show that S2 (Xi-X) = is also an unbaised esimator for \. n-1 3. Find MSE(S2). (We will need two facts) E com/questions/2476527/variance-of-sample-variance) 2. Fact 2: For Poisson distribution, E[(X – u)4] 312 + 1. (See for...
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