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Date None Specimen Marks available 2 Reference code SPNone.2.hl.TZ0.7
Level HL only Paper 2 Time zone TZ0
Command term Show that, Hence, and State Question number 7 Adapted from N/A

Question

The random variable \(X\) has cumulative distribution function\[F(x) = \left\{ {\begin{array}{*{20}{c}}
  0&{x < 0} \\
  {{{\left( {\frac{x}{a}} \right)}^3}}&{0 \leqslant x \leqslant a} \\
  1&{x > a}
\end{array}} \right.\]where \(a\) is an unknown parameter. You are given that the mean and variance of \(X\)
are \(\frac{{3a}}{4}\) and \(\frac{{3{a^2}}}{{80}}\) respectively. To estimate the value of \(a\) , a random sample of \(n\) independent observations, \({X_1},{X_2}, \ldots {X_n}\) is taken from the distribution of \(X\) .

 

(i)     Find an expression for \(c\) in terms of \(n\) such that \(U = c\sum\limits_{i = 1}^n {{X_i}} \) is an unbiased estimator for \(a\) .

(ii)     Determine an expression for \({\text{Var}}(U)\) in this case.

[4]
a.

(i)     Show that \({\rm{P}}(Y \le y) = {\left( {\frac{y}{a}} \right)^{3n}},0 \le y \le a\) and deduce an expression for the probability density function of \(Y\) .

(ii)     Find \({\rm{E}}(Y)\) .

(iii)     Show that \({\rm{Var}}(Y) = \frac{{3n{a^2}}}{{(3n + 2){{(3n + 1)}^2}}}\) .

(iv)     Find an expression for \(d\) in terms of \(n\) such that \(V = dY\) is an unbiased estimator for \(a\) .

(v)     Determine an expression for \({\rm{Var}}(V)\) in this case.

[16]
b.

Show that \(\frac{{{\rm{Var}}(U)}}{{{\rm{Var}}(V)}} = \frac{{3n + 2}}{5}\) and hence state, with a reason, which of \(U\) or \(V\) is the more efficient estimator for \(a\) .

[2]
c.

Markscheme

(i)     \({\rm{E}}(U) = c \times n \times \frac{{3a}}{4} = a \Rightarrow c = \frac{4}{{3n}}\)     M1A1

(ii)     \(Var(U) = \frac{{16}}{{9{n^2}}} \times n \times \frac{{3{a^2}}}{{80}} = \frac{{{a^2}}}{{15n}}\)     M1A1

[4 marks]

a.

(i)     \({\rm{P}}(Y \le y) = {\rm{P}}({\text{all }}Xs \le y)\)     M1

\( = \left[ {\rm{P}} \right.{\left. {(X \le y)} \right]^n}\)    (A1)

\( = {\left( {{{\left( {\frac{y}{a}} \right)}^3}} \right)^n}\)     (A1)

Note: Only one of the two A1 marks above may be implied.

 

\( = {\left( {\frac{y}{a}} \right)^{3n}}\)     AG

\(g(y) = \frac{{\rm{d}}}{{{\rm{d}}y}}{\left( {\frac{y}{a}} \right)^{3n}} = \frac{{3n{y^{3n - 1}}}}{{{a^{3n}}}},(0 < y < a)\)     M1A1

(\(g(y) = 0\) otherwise)

 

(ii)     \({\rm{E}}(Y) = \int_0^a {\frac{{3n{y^{3n}}}}{{{a^{3n}}}}} {\rm{d}}y\)     M1

\( = \left[ {\frac{{3n{y^{3n + 1}}}}{{(3n + 1){a^{3n}}}}} \right]_0^a\)     A1

\( = \frac{{3na}}{{3n + 1}}\)     A1

 

(iii)     \({\rm{Var}}(Y) = \int_0^a {\frac{{3n{y^{3n + 1}}}}{{{a^{3n}}}}} {\rm{d}}y - {\left( {\frac{{3na}}{{3n + 1}}} \right)^2}\)     M1

\( = \left[ {\frac{{3n{y^{3n + 2}}}}{{(3n + 2){a^{3n}}}}} \right]_0^a - {\left( {\frac{{3na}}{{3n + 1}}} \right)^2}\)     A1

\( = \frac{{3n{a^2}}}{{3n + 2}} - \frac{{9{n^2}{a^2}}}{{{{(3n + 1)}^2}}}\)     M1

\( = \frac{{3n{a^2}(9{n^2} + 6n + 1) - 9{n^2}{a^2}(3n + 2)}}{{(3n + 2)(3n + 1)}}\)     A1

\( = \frac{{3n{a^2}}}{{(3n + 2){{(3n + 1)}^2}}}\)     AG

 

(iv)     \({\rm{E}}(V) = d \times \frac{{3na}}{{3n + 1}} = a \Rightarrow d = \frac{{3n + 1}}{{3n}}\)     M1A1

 

(v)     \(Var(V) = {\left( {\frac{{3n + 1}}{{3n}}} \right)^2} \times \frac{{3n{a^2}}}{{(3n + 2){{(3n + 1)}^2}}}\)     M1

\( = \frac{{{a^2}}}{{3n(3n + 2)}}\)     A1

 

 

[16 marks]

b.

\(\frac{{{\rm{Var}}(U)}}{{{\rm{Var}}(V)}} = \frac{{\frac{{{a^2}}}{{15n}}}}{{\frac{{{a^2}}}{{3n(3n + 2)}}}}\)     A1

\( = \frac{{3n + 2}}{5}\)     AG

\(V\) is the more efficient estimator because \(3n + 2 > 5\) (for \(n > 1\) )     R1

[2 marks]

c.

Examiners report

[N/A]
a.
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b.
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c.

Syllabus sections

Topic 3 - Statistics and probability » 3.3 » Unbiased estimators and estimates.

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