# AP Statistics Curriculum 2007 MultivariateNormal

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

- | In k-dimensions, a random vector <math>X = ( | + | 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 <ref>Gut, Allan: An Intermediate Course in Probability, Springer 2009, chapter 5, http://books.google.com/books?id=ufxMwdtrmOAC, ISBN 9781441901613</ref>: |

- | * 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 | + | * 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. |

- | * There exists a random ''ℓ''-vector ''Z'', whose components are independent normal random variables, a ''k''-vector ''μ'', and a ''k×ℓ'' | + | * 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 <math>X = AZ + \mu</math>. 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 | * There is a ''k''-vector ''μ'' and a symmetric, nonnegative-definite ''k×k'' matrix Σ, such that the characteristic function of ''X'' is | ||

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

- | \varphi_X(u) = \exp\Big( iu | + | \varphi_X(u) = \exp\Big( iu^T\mu - \tfrac{1}{2} u^T\Sigma u \Big). |

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

## Revision as of 05:17, 14 December 2010

## 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 <ref>Gut, Allan: An Intermediate Course in Probability, Springer 2009, chapter 5, http://books.google.com/books?id=ufxMwdtrmOAC, ISBN 9781441901613</ref>:

- 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 (Template:Nowrap), the probability density function of a (bivariate) vector Template:Nowrap 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 Template:Nowrap 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 one in which X has a normal distribution with expected value 0 and variance 1, and *Y* = *X* if |*X*| > *c* and *Y* = −*X* if |*X*| < *c*, where *c* is about 1.54.

### Problems

### References

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

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