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10.4 Problems on Functions of Random Variables

Deriving the distribution of Z = g(X) — exponential decays, present values of future costs — by mapping value conditions on Z back through g.

Applied Probability, 10.4 Problems on Functions of Random Variables

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13 exercises

10.4 Exercise 1

Suppose X is a nonnegative, absolutely continuous random variable. Let Z=g(X)=Ce-aXZ = g \left(X\right) = C e^{- a X}, where a>0,C>0a > 0 , C > 0. Then 0<Z≤C0 < Z \leq C. Use properties of the exponential and natural log function to show that

F Z ( v ) = 1 - F X - ln ( v / C ) a for 0 < v ≤ C F_{Z} \left(v\right) = 1 - F_{X} \left(-\frac{\ln \left(v / C\right)}{a}\right) \text{for} 0 < v \leq C
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10.4 Exercise 2

Use the result of the exercise to show that if X∼X \sim exponential (λ)\left(\lambda\right), then

F Z ( v ) = v C λ / a 0 < v ≤ C F_{Z} \left(v\right) = \left(\frac{v}{C}\right)^{\lambda / a} 0 < v \leq C
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10.4 Exercise 3

Present value of future costs. Suppose money may be invested at an annual rate a, compounded continually. Then one dollar in hand now, has a value eaxe^{a x} at the end of x years. Hence, one dollar spent x years in the future has a present valuee-axe^{- a x}. Suppose a device put into operation has time to failure (in years) X∼X \sim exponential (λ)\left(\lambda\right). If the cost of replacement at failure is C dollars, then the present value…

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10.4 Exercise 4

Optimal stocking of merchandise. A merchant is planning for the Christmas season. He intends to stock m units of a certain item at a cost of c per unit. Experience indicates demand can be represented by a random variable D∼D \sim Poisson (μ)\left(\mu\right). If units remain in stock at the end of the season, they may be returned with recovery of r per unit. If demand exceeds the number originally ordered, extra units ma…

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10.4 Exercise 5

(See Example 2 from "Functions of a Random Variable") The cultural committee of a student organization has arranged a special deal for tickets to a concert. The agreement is that the organization will purchase ten tickets at $20 each (regardless of the number of individual buyers). Additional tickets are available according to the following schedule:

  • 11-20, $18 each
  • 21-30, $16 each
  • 31-50, $15 e…
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10.4 Exercise 9

For the pair {X,Y}\left\{X , Y\right\} in the exercise, let Z=g(X,Y)=3X2+2XY-Y2Z = g \left(X , Y\right) = 3 X^{2} + 2 X Y - Y^{2}. Determine and plot the distribution function for Z.

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10.4 Exercise 10

For the pair {X,Y}\left\{X , Y\right\} in the exercise, let

W = g ( X , Y ) = X for X + Y ≤ 4 2 Y for X + Y > 4 = I M ( X , Y ) X + I M c ( X , Y ) 2 Y W = g \left(X , Y\right) = \left\{\begin{matrix} X & \text{for} X + Y \leq 4 \\ 2 Y & \text{for} X + Y > 4 \end{matrix}\right = I_{M} \left(X , Y\right) X + I_{M^{c}} \left(X , Y\right) 2 Y

Determine and plot the distribution function for W.

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10.4 Exercise 11

fXY(t,u)=388(2t+3u2)f_{X Y} \left(t , u\right) = \frac{3}{88} \left(2 t + 3 u^{2}\right) for 0≤t≤20 \leq t \leq 2, 0≤u≤1+t0 \leq u \leq 1 + t (see Exercise 15 from "Problems on Random Vectors and Joint Distributions").

Z = I [ 0 , 1 ] ( X ) 4 X + I ( 1 , 2 ] ( X ) ( X + Y ) Z = I_{\left[0 , 1\right]} \left(X\right) 4 X + I_{\left(1 , 2\right]} \left(X\right) \left(X + Y\right)

Determine P(Z≤2)P \left(Z \leq 2\right)

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10.4 Exercise 12

fXY(t,u)=2411tuf_{X Y} \left(t , u\right) = \frac{24}{11} t u for 0≤t≤20 \leq t \leq 2, 0≤u≤min{1,2-t}0 \leq u \leq \min \left\{1 , 2 - t\right\} (see Exercise 17 from "Problems on Random Vectors and Joint Distributions").

Z = I M ( X , Y ) 1 2 X + I M c ( X , Y ) Y 2 , M = { ( t , u ) : u > t } Z = I_{M} \left(X , Y\right) \frac{1}{2} X + I_{M^{c}} \left(X , Y\right) Y^{2} , M = \left\{\left(t , u\right) : u > t\right\}

Determine P(Z≤1/4)P \left(Z \leq 1 / 4\right).

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10.4 Exercise 13

fXY(t,u)=323(t+2u)f_{X Y} \left(t , u\right) = \frac{3}{23} \left(t + 2 u\right) for 0≤t≤20 \leq t \leq 2, 0≤u≤max{2-t,t}0 \leq u \leq \max \left\{2 - t , t\right\} (see Exercise 18 from "Problems on Random Vectors and Joint Distributions").

Z = I M ( X , Y ) ( X + Y ) + I M c ( X , Y ) 2 Y , M = { ( t , u ) : max ( t , u ) ≤ 1 } Z = I_{M} \left(X , Y\right) \left(X + Y\right) + I_{M^{c}} \left(X , Y\right) 2 Y , M = \left\{\left(t , u\right) : \max \left(t , u\right) \leq 1\right\}

Determine P(Z≤1)P \left(Z \leq 1\right).

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10.4 Exercise 14

fXY(t,u)=12179(3t2+u)f_{X Y} \left(t , u\right) = \frac{12}{179} \left(3 t^{2} + u\right), for 0≤t≤20 \leq t \leq 2, 0≤u≤min{2,3-t}0 \leq u \leq \min \left\{2 , 3 - t\right\} (see Exercise 19 from "Problems on Random Vectors and Joint Distributions").

Z = I M ( X , Y ) ( X + Y ) + I M c ( X , Y ) 2 Y 2 , M = { ( t , u ) : t ≤ 1 , u ≥ 1 } Z = I_{M} \left(X , Y\right) \left(X + Y\right) + I_{M^{c}} \left(X , Y\right) 2 Y^{2} , M = \left\{\left(t , u\right) : t \leq 1 , u \geq 1\right\}

Determine P(Z≤2)P \left(Z \leq 2\right).

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10.4 Exercise 16

The class {X,Y,Z}\left\{X , Y , Z\right\} is independent.

          X=-2IA+IB+3ICX = - 2 I_{A} + I_{B} + 3 I_{C}. Minterm probabilities are (in the usual order)

0 . 255 0 . 025 0 . 375 0 . 045 0 . 108 0 . 012 0 . 162 0 . 018 0 . 255 0 . 025 0 . 375 0 . 045 0 . 108 0 . 012 0 . 162 0 . 018

          Y=ID+3IE+IF-3Y = I_{D} + 3 I_{E} + I_{F} - 3. The class {D,E,F}\left\{D , E , F\right\} is independent with

P ( D ) = 0 . 32 P ( E ) = 0 . 56 P ( F ) = 0 . 40 P \left(D\right) = 0 . 32 P \left(E\right) = 0 . 56 P \left(F\right) = 0 . 40

          Z has distribution

Determine P(X2+3XY2>3Z)P \left(X^{2} + 3 X Y^{2} > 3 Z\right).

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10.4 Exercise 17

The simple random variable X has distribution

X = [ - 3 . 1 - 0 . 5 1 . 2 2 . 4 3 . 7 4 . 9 ] P X = [ 0 . 15 0 . 22 0 . 33 0 . 12 0 . 11 0 . 07 ] X = \left[- 3 . 1 - 0 . 5 1 . 2 2 . 4 3 . 7 4 . 9\right] P X = \left[0 . 15 0 . 22 0 . 33 0 . 12 0 . 11 0 . 07\right]
  1. Plot the distribution function FX and the quantile function QX.
  2. Take a random sample of size n=10,000n = 10 , 000. Compare the relative frequency for each value with the probability that value is taken on.
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Adapted from Applied Probability © 2009 Paul E. Pfeiffer. Licensed under CC BY 3.0. Source: Applied Probability. Imported at revision d7a0420.