Difference between revisions of "AP Statistics Curriculum 2007 Laplace"

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(Related Distributions)
(Applications)
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*Insurance claims
 
*Insurance claims
 
*Structural changes in switching-regime model and Kalman filter
 
*Structural changes in switching-regime model and Kalman filter
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===Example===
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Suppose that the return of a certain stock has a Laplace distribution with <font size=3><math>\mu=5</math></font> and <font size=3><math>b=2</math></font>.  Compute the probability that the stock will have a return between 6 and 10.
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We can compute this as follows:
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:<math>P(6 \le X\le 10)=\sum_{x=6}^{10}\frac{1}{2\times 2}\exp(-\frac{|x-5|}{2})=0.262223</math>
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The figure below shows this result using [http://socr.ucla.edu/htmls/dist/Laplace_Distribution.html SOCR distributions]
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<center>[[Image:Laplace.jpg|600px]]</center>

Revision as of 16:13, 11 July 2011

Laplace Distribution

Definition: Laplace distribution is a distribution that is symmetrical and more “peaky” than a normal distribution. The dispersion of the data around the mean is higher than that of a normal distribution. Laplace distribution is also sometimes called the double exponential distribution.


Probability density function: For X~Laplace(\(\mu\),b), the Laplace probability density function is given by

\[\frac{1}{2b}exp(-\frac{|x-\mu|}{b})\]

where

  • e is the natural number (e = 2.71828…)
  • b is a scale parameter (determines the profile of the distribution)
  • \(\mu\) is the mean
  • x is a random variable


Cumulative density function: The Laplace cumulative distribution function is given by

\[ \left\{\begin{matrix} \frac{1}{2}\exp(\frac{x-\mu}{b}) & \mbox{if }x < \mu \\[8pt] 1-\frac{1}{2}\exp(-\frac{x-\mu}{b}) & \mbox{if }x \geq \mu \end{matrix}\right. \]

where

  • e is the natural number (e = 2.71828…)
  • b is a scale parameter (determines the profile of the distribution)
  • \(\mu\) is the mean
  • x is a random variable


Moment generating function: The Laplace moment-generating function is

\[M(t)=\frac{\exp(\mu t)}{1-b^2 t^2} \mbox{ for }|t|<\frac{1}{b}\]


Expectation:

\[E(X)=\mu\!\]


Variance: The gamma variance is

\[Var(X)=2b^2\!\]

Related Distributions

  • If \(X\sim Laplace(\mu,b)\!\), then \(kX+b\sim Laplace(k\mu+b,kb)\!\)
  • If \(X \sim Laplace(0,b)\!\), then \(|X| \sim Exponential(\tfrac{1}{b})\!\) (exponential distribution)

Applications

The Laplace distribution is used for modeling in signal processing, various biological processes, finance, and economics. Examples of events that may be modeled by Laplace distribution include:

  • Credit risk and exotic options in financial engineering
  • Insurance claims
  • Structural changes in switching-regime model and Kalman filter

Example

Suppose that the return of a certain stock has a Laplace distribution with \(\mu=5\) and \(b=2\). Compute the probability that the stock will have a return between 6 and 10.

We can compute this as follows:

\[P(6 \le X\le 10)=\sum_{x=6}^{10}\frac{1}{2\times 2}\exp(-\frac{|x-5|}{2})=0.262223\]

The figure below shows this result using SOCR distributions

Laplace.jpg