# AP Statistics Curriculum 2007 MultivariateNormal

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=== Definition=== | === Definition=== | ||

- | In k-dimensions, a random vector <math>X = (X_1, \cdots, X_k)</math> is multivariate normally distributed if it satisfies any one of the following ''equivalent'' conditions | + | In k-dimensions, a random vector <math>X = (X_1, \cdots, X_k)</math> is multivariate normally distributed if it satisfies any one of the following ''equivalent'' conditions (Gut, 2009): |

* Every linear combination of its components ''Y'' = ''a''<sub>1</sub>''X''<sub>1</sub> + … + ''a<sub>k</sub>X<sub>k</sub>'' is [[AP_Statistics_Curriculum_2007_Normal_Prob|normally distributed]]. In other words, for any constant vector <math>a\in R^k</math>, the linear combination (which is univariate random variable) <math>Y = a^TX = \sum_{i=1}^{k}{a_iX_i}</math> has a univariate normal distribution. | * Every linear combination of its components ''Y'' = ''a''<sub>1</sub>''X''<sub>1</sub> + … + ''a<sub>k</sub>X<sub>k</sub>'' is [[AP_Statistics_Curriculum_2007_Normal_Prob|normally distributed]]. In other words, for any constant vector <math>a\in R^k</math>, the linear combination (which is univariate random variable) <math>Y = a^TX = \sum_{i=1}^{k}{a_iX_i}</math> has a univariate normal distribution. | ||

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: <math> | : <math> | ||

f_X(x) = \frac{1}{ (2\pi)^{k/2}|\Sigma|^{1/2} } | f_X(x) = \frac{1}{ (2\pi)^{k/2}|\Sigma|^{1/2} } | ||

- | \exp\!\Big( {-\tfrac{1}{2}}(x-\mu)'\Sigma^{-1}(x-\mu) \Big) | + | \exp\!\Big( {-\tfrac{1}{2}}(x-\mu)'\Sigma^{-1}(x-\mu) \Big) |

- | </math> | + | </math>, where |Σ| is the determinant of Σ, and where (2π)<sup>''k''/2</sup>|Σ|<sup>1/2</sup> = |2πΣ|<sup>1/2</sup>. This formulation reduces to the density of the univariate normal distribution if Σ is a scalar (i.e., a 1×1 matrix). |

- | where |Σ| is the determinant of Σ, and where (2π)<sup>''k''/2</sup>|Σ|<sup>1/2</sup> = |2πΣ|<sup>1/2</sup>. This formulation reduces to the density of the univariate normal distribution if Σ is a scalar (i.e., a 1×1 matrix). | + | |

If the variance-covariance matrix is singular, the corresponding distribution has no density. An example of this case is the distribution of the vector of residual-errors in the ordinary least squares regression. Note also that the ''X''<sub>''i''</sub> are in general ''not'' independent; they can be seen as the result of applying the matrix ''A'' to a collection of independent Gaussian variables ''Z''. | If the variance-covariance matrix is singular, the corresponding distribution has no density. An example of this case is the distribution of the vector of residual-errors in the ordinary least squares regression. Note also that the ''X''<sub>''i''</sub> are in general ''not'' independent; they can be seen as the result of applying the matrix ''A'' to a collection of independent Gaussian variables ''Z''. | ||

- | |||

- | |||

===Bivariate (2D) case=== | ===Bivariate (2D) case=== | ||

- | In 2-dimensions, the nonsingular bi-variate Normal distribution with ( | + | : See the SOCR Bivariate Normal Distribution [[SOCR_BivariateNormal_JS_Activity| Activity]] and corresponding [http://socr.ucla.edu/htmls/HTML5/BivariateNormal/ Webapp]. |

+ | |||

+ | In 2-dimensions, the nonsingular bi-variate Normal distribution with (<math>k=rank(\Sigma) = 2</math>), the probability density function of a (bivariate) vector (X,Y) is | ||

: <math> | : <math> | ||

f(x,y) = | f(x,y) = | ||

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</math> | </math> | ||

- | In the bivariate case, the first equivalent condition for multivariate normality is less restrictive: it is sufficient to verify that countably many distinct linear combinations of X and Y are normal in order to conclude that the vector | + | In the bivariate case, the first equivalent condition for multivariate normality is less restrictive: it is sufficient to verify that countably many distinct linear combinations of X and Y are normal in order to conclude that the vector <math> [ X, Y ] ^T</math> is bivariate normal. |

===Properties=== | ===Properties=== | ||

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====Two normally distributed random variables need not be jointly bivariate normal==== | ====Two normally distributed random variables need not be jointly bivariate normal==== | ||

- | The fact that two random variables ''X'' and ''Y'' both have a normal distribution does not imply that the pair (''X'', ''Y'') has a joint normal distribution. A simple example is | + | The fact that two random variables ''X'' and ''Y'' both have a normal distribution does not imply that the pair (''X'', ''Y'') has a joint normal distribution. A simple example is provided below: |

+ | : Let X ~ N(0,1). | ||

+ | : Let <math>Y = \begin{cases} X,& |X| > 1.33,\\ | ||

+ | -X,& |X| \leq 1.33.\end{cases}</math> | ||

+ | Then, both X and Y are individually Normally distributed; however, the pair (X,Y) is '''not''' jointly bivariate Normal distributed (of course, the constant c=1.33 is not special, any other non-trivial constant also works). | ||

+ | |||

+ | Furthermore, as X and Y are not independent, the sum Z = X+Y is not guaranteed to be a (univariate) Normal variable. In this case, it's clear that Z is not Normal: | ||

+ | : <math>Z = \begin{cases} 0,& |X| \leq 1.33,\\ | ||

+ | 2X,& |X| > 1.33.\end{cases}</math> | ||

+ | |||

+ | ===Applications=== | ||

+ | [[SOCR_EduMaterials_Activities_2D_PointSegmentation_EM_Mixture| This SOCR activity demonstrates the use of 2D Gaussian distribution, expectation maximization and mixture modeling for classification of points (objects) in 2D]]. | ||

===[[EBook_Problems_MultivariateNormal|Problems]]=== | ===[[EBook_Problems_MultivariateNormal|Problems]]=== | ||

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===References=== | ===References=== | ||

- | + | * Gut, A. (2009): [http://books.google.com/books?id=ufxMwdtrmOAC An Intermediate Course in Probability, Springer 2009, chapter 5, ISBN 9781441901613]. | |

<hr> | <hr> |

## Current revision as of 00:02, 22 July 2012

## Contents |

## EBook - Multivariate Normal Distribution

The multivariate normal distribution, or multivariate Gaussian distribution, is a generalization of the univariate (one-dimensional) normal distribution to higher dimensions. A random vector is said to be multivariate normally distributed if every linear combination of its components has a univariate normal distribution. The multivariate normal distribution may be used to study different associations (e.g., correlations) between real-valued random variables.

### Definition

In k-dimensions, a random vector is multivariate normally distributed if it satisfies any one of the following *equivalent* conditions (Gut, 2009):

- Every linear combination of its components
*Y*=*a*_{1}*X*_{1}+ … +*a*is normally distributed. In other words, for any constant vector , the linear combination (which is univariate random variable) has a univariate normal distribution._{k}X_{k}

- There exists a random
*ℓ*-vector*Z*, whose components are independent normal random variables, a*k*-vector*μ*, and a*k×ℓ*matrix*A*, such that*X*=*A**Z*+ μ. Here*ℓ*is the*rank*of the variance-covariance matrix.

- There is a
*k*-vector*μ*and a symmetric, nonnegative-definite*k×k*matrix Σ, such that the characteristic function of*X*is

- When the support of
*X*is the entire space**R**^{k}, there exists a*k*-vector*μ*and a symmetric positive-definite*k×k*variance-covariance matrix Σ, such that the probability density function of*X*can be expressed as

- , where |Σ| is the determinant of Σ, and where (2π)
^{k/2}|Σ|^{1/2}= |2πΣ|^{1/2}. This formulation reduces to the density of the univariate normal distribution if Σ is a scalar (i.e., a 1×1 matrix).

If the variance-covariance matrix is singular, the corresponding distribution has no density. An example of this case is the distribution of the vector of residual-errors in the ordinary least squares regression. Note also that the *X*_{i} are in general *not* independent; they can be seen as the result of applying the matrix *A* to a collection of independent Gaussian variables *Z*.

### Bivariate (2D) case

In 2-dimensions, the nonsingular bi-variate Normal distribution with (*k* = *r**a**n**k*(Σ) = 2), the probability density function of a (bivariate) vector (X,Y) is

where *ρ* is the correlation between *X* and *Y*. In this case,

In the bivariate case, the first equivalent condition for multivariate normality is less restrictive: it is sufficient to verify that countably many distinct linear combinations of X and Y are normal in order to conclude that the vector [*X*,*Y*]^{T} is bivariate normal.

### Properties

#### Normally distributed and independent

If *X* and *Y* are *normally distributed* and *independent*, this implies they are "jointly normally distributed", hence, the pair (*X*, *Y*) must have bivariate normal distribution. However, a pair of jointly normally distributed variables need not be independent - they could be correlated.

#### Two normally distributed random variables need not be jointly bivariate normal

The fact that two random variables *X* and *Y* both have a normal distribution does not imply that the pair (*X*, *Y*) has a joint normal distribution. A simple example is provided below:

- Let X ~ N(0,1).
- Let

Then, both X and Y are individually Normally distributed; however, the pair (X,Y) is **not** jointly bivariate Normal distributed (of course, the constant c=1.33 is not special, any other non-trivial constant also works).

Furthermore, as X and Y are not independent, the sum Z = X+Y is not guaranteed to be a (univariate) Normal variable. In this case, it's clear that Z is not Normal:

### Applications

### Problems

### References

- Gut, A. (2009): An Intermediate Course in Probability, Springer 2009, chapter 5, ISBN 9781441901613.

- SOCR Home page: http://www.socr.ucla.edu

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