AP Statistics Curriculum 2007 Laplace

From Socr

(Difference between revisions)
Jump to: navigation, search
(Related Distributions)
(Applications)
Line 49: Line 49:
*Insurance claims
*Insurance claims
*Structural changes in switching-regime model and Kalman filter
*Structural changes in switching-regime model and Kalman filter
 +
 +
===Example===
 +
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.
 +
 +
We can compute this as follows:
 +
 +
:<math>P(6 \le X\le 10)=\sum_{x=6}^{10}\frac{1}{2\times 2}\exp(-\frac{|x-5|}{2})=0.262223</math>
 +
 +
The figure below shows this result using [http://socr.ucla.edu/htmls/dist/Laplace_Distribution.html SOCR distributions]
 +
<center>[[Image:Laplace.jpg|600px]]</center>

Revision as of 21:05, 11 July 2011

Contents

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(μ,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)
  • μ 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)
  • μ 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

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 μ = 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

Personal tools