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Suppose that \({X_1},...,{X_n}\) form a random sample from the Bernoulli distribution with parameter p. Let \({\bar X_n}\) be the sample average. Use the variance stabilizing transformation found in Exercise 5 of Section 6.5 to construct an approximate coefficient γ confidence interval for p

Short Answer

Expert verified

A function that stabilizes the variance given in the exercise is

\(\alpha \left( x \right) = \arcsin \left( {\sqrt x } \right)\)

Step by step solution

01

Step1:Given information

\({X_1},...,{X_n}\) form a random sample from the Bernoulli distribution with parameter p.

In the exercise 5 of section 6.5 there is variance stabilizing transformation. With the help of it there is a process to do that.

02

Construction of an approximate coefficient γ confidence interval for p.

Limits for a large sample exact coefficient \(\gamma \) confidence interval \(\left( {A,B} \right)\) for the distribution with parameters \(\mu \) and known \({\sigma ^2}\) is given by

\(\begin{align}A &= {{\bar X}_n} - {\Phi ^{ - 1}}\left( {\frac{{1 + \gamma }}{2}} \right)\frac{\sigma }{{\sqrt n }},\\B &= {{\bar X}_n} + {\Phi ^{ - 1}}\left( {\frac{{1 + \gamma }}{2}} \right)\frac{\sigma }{{\sqrt n }},\end{align}\)

Where \(\gamma \in \left( {0,1} \right)\)

In this case,

\(\begin{align}A &= \arcsin \left( {\sqrt {{{\bar X}_n}} } \right) - {\Phi ^{ - 1}}\left( {\frac{{1 + \gamma }}{2}} \right)\frac{1}{{\sqrt n }}\\B &= \arcsin \left( {\sqrt {{{\bar X}_n}} } \right) + {\Phi ^{ - 1}}\left( {\frac{{1 + \gamma }}{2}} \right)\frac{1}{{\sqrt n }}\end{align}\)

Because \(P\left( {A < \arcsin \left( {\sqrt p } \right) < B} \right) \approx \gamma \)

This yields an approximate confidence interval for p with confidence \(\gamma \)

\(\begin{align}{A_1} &= {\sin ^2}\left( {\arcsin \left( {\sqrt {{{\bar X}_n}} } \right) - {\Phi ^{ - 1}}\left( {\frac{{1 + \gamma }}{2}} \right)\frac{1}{{\sqrt n }}} \right)\\{B_1} &= {\sin ^2}\left( {\arcsin \left( {\sqrt {{{\bar X}_n}} } \right) + {\Phi ^{ - 1}}\left( {\frac{{1 + \gamma }}{2}} \right)\frac{1}{{\sqrt n }}} \right)\end{align}\)

Now, the interval \(\left( {{A_1},{B_1}} \right)\) is the \(\gamma \) coefficient for the interval p.

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Most popular questions from this chapter

Suppose that we will sample 20 chunks of cheese in Example 8.2.3. Let\({\bf{T = }}\sum\limits_{{\bf{i = 1}}}^{{\bf{20}}} {{{\left( {{{\bf{X}}_{\bf{i}}}{\bf{ - \mu }}} \right)}^{\bf{2}}}{\bf{/20}}} \)wherexiis the concentration of lactic acid in theith chunk. Assume thatσ2=0.09. What numbercsatisfies Pr(T≤c)=0.9?

Suppose that a random sampleX1, . . . , Xnis to be taken from the uniform distribution on the interval (0, θ) and thatθis unknown. How large must a random sample be taken in order\({\bf{P}}\left( {{\bf{|max}}\left\{ {{{\bf{X}}_{\bf{1}}}{\bf{,}}...{\bf{,}}{{\bf{X}}_{\bf{n}}}} \right\}{\bf{ - \theta |}} \le {\bf{0}}{\bf{.10}}} \right) \ge {\bf{0}}{\bf{.95}}\) for all possibleθ?

Question:Suppose that a random variable X can take only the five values\({\bf{x = 1,2,3,4,5}}\) with the following probabilities:

\(\begin{aligned}{}{\bf{f}}\left( {{\bf{1}}\left| {\bf{\theta }} \right.} \right){\bf{ = }}{{\bf{\theta }}^{\bf{3}}}{\bf{,}}\,\,\,\,{\bf{f}}\left( {{\bf{2}}\left| {\bf{\theta }} \right.} \right){\bf{ = }}{{\bf{\theta }}^{\bf{2}}}\left( {{\bf{1}} - {\bf{\theta }}} \right){\bf{,}}\\{\bf{f}}\left( {{\bf{3}}\left| {\bf{\theta }} \right.} \right){\bf{ = 2\theta }}\left( {{\bf{1}} - {\bf{\theta }}} \right){\bf{,}}\,\,\,{\bf{f}}\left( {{\bf{4}}\left| {\bf{\theta }} \right.} \right){\bf{ = \theta }}{\left( {{\bf{1}} - {\bf{\theta }}} \right)^{\bf{2}}}{\bf{,}}\\{\bf{f}}\left( {{\bf{5}}\left| {\bf{\theta }} \right.} \right){\bf{ = }}{\left( {{\bf{1}} - {\bf{\theta }}} \right)^{\bf{3}}}{\bf{.}}\end{aligned}\)

Here, the value of the parameter θ is unknown (0 ≤ θ ≤ 1).

a. Verify that the sum of the five given probabilities is 1 for every value of θ.

b. Consider an estimator δc(X) that has the following form:

\(\begin{aligned}{}{{\bf{\delta }}_{\bf{c}}}\left( {\bf{1}} \right){\bf{ = 1,}}\,\,{{\bf{\delta }}_{\bf{c}}}\left( {\bf{2}} \right){\bf{ = 2}} - {\bf{2c,}}\,\,{{\bf{\delta }}_{\bf{c}}}\left( {\bf{3}} \right){\bf{ = c,}}\\{{\bf{\delta }}_{\bf{c}}}\left( {\bf{4}} \right){\bf{ = 1}} - {\bf{2c,}}\,\,{{\bf{\delta }}_{\bf{c}}}\left( {\bf{5}} \right){\bf{ = 0}}{\bf{.}}\end{aligned}\)

Show that for each constant, c\({{\bf{\delta }}_{\bf{c}}}\left( {\bf{X}} \right)\)is an unbiased estimator of θ.

c. Let\({{\bf{\theta }}_{\bf{0}}}\)be a number such that\({\bf{0 < }}{{\bf{\theta }}_{\bf{0}}}{\bf{ < 1}}\). Determine a constant\({{\bf{c}}_{\bf{0}}}\)such that when\({\bf{\theta = }}{{\bf{\theta }}_{\bf{0}}}\)the variance is smaller than the variance \({{\bf{\delta }}_{\bf{c}}}\left( {\bf{X}} \right)\)for every other value of c.

Sketch the p.d.f. of the\({{\bf{\chi }}^{\bf{2}}}\)distribution withmdegrees of freedom for each of the following values ofm. Locate the mean, the median, and the mode on each sketch. (a)m=1;(b)m=2; (c)m=3; (d)m=4.

Suppose thatX1, . . . , Xnform a random sample from the normal distribution with meanμand variance \({\sigma ^2}\). Find the distribution of

\(\frac{{n{{\left( {{{\bar X}_n} - \mu } \right)}^2}}}{{{\sigma ^2}}}\).

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