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Let the pdf \(f\left(x ; \theta_{1}, \theta_{2}\right)\) be of the form $$ \exp \left[p_{1}\left(\theta_{1}, \theta_{2}\right) K_{1}(x)+p_{2}\left(\theta_{1}, \theta_{2}\right) K_{2}(x)+H(x)+q_{1}\left(\theta_{1}, \theta_{2}\right)\right], \quad a

Short Answer

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The pdf \( f(x ; \theta_{1}, \theta_{2}) \) can be reduced to the form \[ f(x ; \theta_{1}, \theta_{2}) = \exp[p(\theta_1, \theta_2) K_2(x) + H(x) + q(\theta_1, \theta_2)]\] where \(p(\theta_1, \theta_2) = p_1(\theta_1, \theta_2)c + p_2(\theta_1, \theta_2)\) and \(q(\theta_1, \theta_2) = p_1(\theta_1, \theta_2) d+ q_1(\theta_1, \theta_2)\). This is due to the homogeneity relation between the derivatives of functions \(K_1\) and \(K_2\), which is a key dependency in finding sufficient statistics and parameters.

Step by step solution

01

Construct the Functions

The given function (pdf) is of the form: \[f(x;\theta_1, \theta_2) = \exp\left[p_1(\theta_1, \theta_2) K_1(x) + p_2(\theta_1, \theta_2) K_2(x) + H(x) + q_1(\theta_1, \theta_2)\right]\] where x is between a and b, and zero elsewhere. The assumption given is that: \(K_1'(x) = c K_2'(x)\)
02

Apply Assumption

Considering the derivate values and the given assumption, we can express \(K_1'(x)\) in terms of \(K_2'(x)\). Therefore, we can re-write \(K_1(x)\) as a function of \(K_2(x)\), which we denote by \(dK_2(x)\), because \(c\) represents the constant of proportionality between the derivatives of \(K_1(x)\) and \(K_2(x)\). So, we get the following relationship: \(K_1(x) = cK_2(x) + d\)
03

Substitute Back

Now substitute these relationships into the original function: \[ f(x ; \theta_{1}, \theta_{2}) = \exp\left[p_1(\theta_1, \theta_2) (cK_2(x) + d) + p_2(\theta_1,\ \theta_2) K_2(x) + H(x) + q_1(\theta_1, \theta_2)\right]\]
04

Simplify the Expression

Proceed to simplify the expression: \[f(x ; \theta_{1}, \theta_{2}) =\exp\left[ (p_1(\theta_1, \theta_2)c + p_2(\theta_1, \theta_2)) K_2(x) + p_1(\theta_1, \theta_2) d + H(x) + q_1(\theta_1, \theta_2)\right]\] This can now be represented as: \[f(x ; \theta_{1}, \theta_{2}) = \exp[p(\theta_1, \theta_2) K_2(x) + H(x) + q(\theta_1, \theta_2)]\] where \( p(\theta_1, \theta_2) = p_1(\theta_1, \theta_2)c + p_2(\theta_1, \theta_2)\) and \(q(\theta_1, \theta_2) = p_1(\theta_1, \theta_2) d+ q_1(\theta_1, \theta_2)\), and x is between a and b, zero elsewhere.

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Most popular questions from this chapter

Let \(Y_{1}

The pdf depicted in Figure \(7.9 .1\) is given by $$ f_{m_{2}}(x)=e^{-x}\left(1+m_{2}^{-1} e^{-x}\right)^{-\left(m_{2}+1\right)}, \quad-\infty0\) (the pdf graphed is for \(m_{2}=0.1\) ). This is a member of a large family of pdfs, \(\log F\) -family, which are useful in survival (lifetime) analysis; see Chapter 3 of Hettmansperger and McKean (2011). (a) Let \(W\) be a random variable with pdf \((7.9 .2)\). Show that \(W=\log Y\), where \(Y\) has an \(F\) -distribution with 2 and \(2 m_{2}\) degrees of freedom. (b) Show that the pdf becomes the logistic \((6.1 .8)\) if \(m_{2}=1\). (c) Consider the location model where $$ X_{i}=\theta+W_{i} \quad i=1, \ldots, n $$ where \(W_{1}, \ldots, W_{n}\) are iid with pdf (7.9.2). Similar to the logistic location model, the order statistics are minimal sufficient for this model. Show, similar to Example \(6.1 .2\), that the mle of \(\theta\) exists.

Let \(Y_{1}

Let a random sample of size \(n\) be taken from a distribution that has the pdf \(f(x ; \theta)=(1 / \theta) \exp (-x / \theta) I_{(0, \infty)}(x)\). Find the mle and MVUE of \(P(X \leq 2)\).

Let \(X_{1}, X_{2}, \ldots, X_{n}\) be a random sample from the uniform distribution with pdf \(f\left(x ; \theta_{1}, \theta_{2}\right)=1 /\left(2 \theta_{2}\right), \theta_{1}-\theta_{2}0\) and the pdf is equal to zero elsewhere. (a) Show that \(Y_{1}=\min \left(X_{i}\right)\) and \(Y_{n}=\max \left(X_{i}\right)\), the joint sufficient statistics for \(\theta_{1}\) and \(\theta_{2}\), are complete. (b) Find the MVUEs of \(\theta_{1}\) and \(\theta_{2}\).

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