# Linear combinations of normal random variables

One property that makes the normal distribution extremely tractable from an analytical viewpoint is its closure under linear combinations: the linear combination of two independent random variables having a normal distribution also has a normal distribution. The following sections present a multivariate generalization of this elementary property and then discuss some special cases.

## Linear transformation of a multivariate normal random vector

A linear transformation of a multivariate normal random vector also has a multivariate normal distribution, as illustrated by the following proposition.

Proposition Let be a multivariate normal random vector with mean and covariance matrix . Let be an real vector and an full-rank real matrix. Then the random vector defined byhas a multivariate normal distribution with meanand covariance matrix

Proof

This is proved using the formula for the joint moment generating function of the linear transformation of a random vector. The joint moment generating function of is Therefore, the joint moment generating function of iswhich is the moment generating function of a multivariate normal distribution with mean and covariance matrix . Note that needs to be positive definite in order to be the covariance matrix of a proper multivariate normal distribution, but this is implied by the assumption that is full-rank. Therefore, has a multivariate normal distribution with mean and covariance matrix , because two random vectors have the same distribution when they have the same joint moment generating function.

The following examples present some important special cases of the above property.

### Example 1 - Sum of two independent normal random variables

The sum of two independent normal random variables has a normal distribution, as stated in the following:

Example Let be a random variable having a normal distribution with mean and variance . Let be a random variable, independent of , having a normal distribution with mean and variance . Then, the random variable defined as:has a normal distribution with mean and variance

Proof

First of all, we need to use the fact that mutually independent normal random variables are jointly normal: the random vector defined ashas a multivariate normal distribution with mean and covariance matrix

We can writewhere

Therefore, according to the above proposition on linear transformations, has a normal distribution with meanand variance

### Example 2 - Sum of more than two mutually independent normal random variables

The sum of more than two independent normal random variables also has a normal distribution, as shown in the following example.

Example Let be mutually independent normal random variables, having means and variances . Then, the random variable defined ashas a normal distribution with mean and variance

Proof

This can be obtained, either generalizing the proof of the proposition in Example 1, or using the proposition in Example 1 recursively (starting from the first two components of , then adding the third one and so on).

### Example 3 - Linear combinations of mutually independent normal random variables

The properties illustrated in the previous two examples can be further generalized to linear combinations of mutually independent normal random variables.

Example Let be mutually independent normal random variables, having means and variances . Let be constants. Then, the random variable defined ashas a normal distribution with mean and variance

Proof

First of all, we need to use the fact that mutually independent normal random variables are jointly normal: the random vector defined ashas a multivariate normal distribution with mean and covariance matrix

We can writewhere

Therefore, according to the above proposition on linear transformations, has a (multivariate) normal distribution with meanand variance

### Example 4 - Linear transformation of a normal random variable

A special case of the above proposition obtains when has dimension (i.e., it is a random variable).

Example Let be a normal random variable with mean and variance . Let and be two constants (with ). Then the random variable defined byhas a normal distribution with meanand variance

Proof

This is just a special case () of the above proposition on linear transformations.

### Example 5 - Linear combinations of mutually independent normal random vectors

The property illustrated in Example 3 can be generalized to linear combinations of mutually independent normal random vectors.

Example Let be mutually independent normal random vectors, having means and covariance matrices . Let be real full-rank matrices. Then, the random vector defined ashas a normal distribution with mean and covariance matrix

Proof

This is a consequence of the fact that mutually independent normal random vectors are jointly normal: the random vector defined ashas a multivariate normal distribution with mean and covariance matrix

Therefore, we can apply the above proposition on linear transformations to the vector .

## Solved exercises

Below you can find some exercises with explained solutions.

### Exercise 1

Let be a multivariate normal random vector with mean and covariance matrixFind the distribution of the random variable defined as

Solution

We can writewhere Being a linear transformation of a multivariate normal random vector, is also multivariate normal. Actually, it is univariate normal, because it is a scalar. Its mean isand its variance is

### Exercise 2

Let , ..., be mutually independent standard normal random variables. Let be a constant. Find the distribution of the random variable defined as

Solution

Being a linear combination of mutually independent normal random variables, has a normal distribution with meanand variance

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