AP Statistics Curriculum 2007 MultivariateNormal
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 \(X = (X<sub>1</sub>, \cdots, X<sub>k</sub>)\) 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 = a1X1 + … + akXk is normally distributed. In other words, for any constant vector Template:Nowrap, the linear combination (which is univariate random variable) \(Y = a′X = \sum_{i=1,\cdots,k}{a_iX_i}\) 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 matrix A, such that Template:Nowrap. Here ℓ is the rank of the covariance matri
- There is a k-vector μ and a symmetric, nonnegative-definite k×k matrix Σ, such that the characteristic function of X is
\[ \varphi_X(u) = \exp\Big( iu'\mu - \tfrac{1}{2} u'\Sigma u \Big). \]
- When the support of X is the entire space Rk, 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
\[ f_X(x) = \frac{1}{ (2\pi)^{k/2}|\Sigma|^{1/2} } \exp\!\Big( {-\tfrac{1}{2}}(x-\mu)'\Sigma^{-1}(x-\mu) \Big), \] 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 Xi 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 \[ f(x,y) = \frac{1}{2 \pi \sigma_x \sigma_y \sqrt{1-\rho^2}} \exp\left( -\frac{1}{2(1-\rho^2)}\left[ \frac{(x-\mu_x)^2}{\sigma_x^2} + \frac{(y-\mu_y)^2}{\sigma_y^2} - \frac{2\rho(x-\mu_x)(y-\mu_y)}{\sigma_x \sigma_y} \right] \right), \] where ρ is the correlation between X and Y. In this case, \[ \mu = \begin{pmatrix} \mu_x \\ \mu_y \end{pmatrix}, \quad \Sigma = \begin{pmatrix} \sigma_x^2 & \rho \sigma_x \sigma_y \\ \rho \sigma_x \sigma_y & \sigma_y^2 \end{pmatrix}. \]
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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