Probability theory 2008 Outline of lecture 5 The multivariate normal distribution Characterizing...
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Probability theory 2008
Outline of lecture 5
The multivariate normal distribution
Characterizing properties of the univariate normal distribution Different definitions of normal random vectors Conditional distributions Independence Cochran’s theorem
Probability theory 2008
The univariate normal distribution- defining properties
A distribution is normal if and only if it has the probability density
where R and > 0.
A distribution is normal if and only if the sample mean
and the sample variance
are independent for all n.
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Probability theory 2008
The univariate normal distribution- defining properties
Suppose that X1 and X2 are independent of each other, and that the same is true for the pair
where no coefficient vanishes. Then all four variables are normal.
Special case: rotations other than multiples of 90 degrees
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Probability theory 2008
The univariate normal distribution- defining properties
Let F be a class of distributions such that
X F a + bX F
Can F be comprised of distributions other than the normal distributions?
cf. Cauchy distributions
Probability theory 2008
The multivariate normal distribution- a first definition
A random vector is normal if and only if every linear combination of its components is normal
Immediate consequences:
Every component is normal
The sum of all components is normal
Every marginal distribution is normal
Vectors in which the components are independent normal random variables are normal
Linear transformations of normal random vectors give rise to new normal vectors
Probability theory 2008
Illustrations of independent and dependent normal distributions
http://stat.sm.u-tokai.ac.jp/~yama/graphics/bnormE.html
Probability theory 2008
Parameterization of the multivariatenormal distribution
Is a multivariate normal distribution uniquely determined by the vector of expected values and the covariance matrix?
Is there a multivariate normal distribution for any covariance matrix?
Probability theory 2008
Fundamental results for covariance matrices
Let be a covariance matrix.
Since is symmetric there exists an orthogonal matrix C
(C’C = C C’ = I) such that
C’ C = D and = CD C’
where D is a diagonal matrix.
Since is also nonnegative-definite, there exists a symmetric matrix B such that
B B =
If X has independent components with variance 1, Y = BX has covariance matrix
Probability theory 2008
The multivariate normal distribution- a second definition
A random vector is normal if and only if it has a characteristic function of the form
where is a nonnegative-definite, symmetric matrix and is a vector of constants
Proof of the equivalence of definition I and II:
Let XN( , ) according to definition I, and set Z = t’X. Then E(Z) = t’u and Var(Z) = t’ t, and Z(1) gives the desired expression.
Let XN( , ) according to definition II. Then we can derive the characteristic function of any linear combination of its components and show that it is normally distributed.
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Probability theory 2008
The multivariate normal distribution- a third definition
Let Y be normal with independent standard normal components and set
Then
provided that the determinant is non-zero.
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Probability theory 2008
The multivariate normal distribution- a fourth definition
Let Y be normal with independent standard normal components and set
Then X is said to be a normal random vector.
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Probability theory 2008
The multivariate normal distribution- conditional distributions
All conditional distributions in a multivariate normal vector
are normal
The conditional distribution of each component is equal to that of a linear combination of the other components plus a random error
Probability theory 2008
The multivariate normal distribution- conditional distributions and optimal predictors
For any random vector X it is known that E(Xn | X1, …, Xn-1) is an optimal predictor of Xn based on X1, …, Xn-1 and that
Xn = E(Xn | X1, …, Xn-1) +
where is uncorrelated to the conditional expectation.
For normal random vectors X, the optimal predictor E(Xn | X1, …, Xn-1) is a linear expression in X1, …, Xn-1
Probability theory 2008
The multivariate normal distribution- calculation of conditional distributions
Let XN (0 , ) where
Determine the conditional distribution of X3 given X1 and X2
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Set Z = a X1 + bX2 + c
Minimize the variance of the prediction error Z - X3
Probability theory 2008
The multivariate normal vector- uncorrelated and independent components
The components of a normal random vector are independent if and only if they are uncorrelated
Probability theory 2008
The multivariate normal distribution- orthogonal transformations
Let X be a normal random vector with independent standard normal components, and let C be an orthogonal matrix.
Then
Y = CX
has independent, standard normal components
Probability theory 2008
Quadratic forms of the components of a multivariate normal distribution – one-way analysis
of variance
Let Xijij, i = 1, …, k, j = 1, …, ni , be k samples of observations. Then, the total variation in the X-values can be decomposed as follows:
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Probability theory 2008
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Probability theory 2008
Decomposition theorem for nonnegative-definite quadratic forms
Let
where
Then there exists an orthogonal matrix C such that with x = Cy
(y = C’x)
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Probability theory 2008
Decomposition theorem for nonnegative-definite quadratic forms (Cochran’s theorem)
Let X1, …, Xn be independent and N(0; 2) and suppose that
where
Then there exists an orthogonal matrix C such that with X = CY (Y = C’X)
Furthermore, Q1, …, Qp are independent and 22-distrubuted with r1, …rp degrees of freedom
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Probability theory 2008
Quadratic forms of the components of a multivariate normal distribution – one-way analysis
of variance
Let Xijij, i = 1, …, k, j = 1, …, ni , be independent and N( ,2). Then,
the total sum of squares can be decomposed into three quadratic forms
which are independent and 22-distrubuted with 1, k-1, and n-k degrees of freedom
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