Random vector mean and covariance

In summary, the conversation discusses finding the covariance of Z, a random vector composed of elements Y_1 to Y_m multiplied by corresponding numbers N_1 to N_m. The formula for the covariance of Z is derived using properties of expectations for random vectors.
  • #1
cutesteph
63
0

Homework Statement



Random vector Y = [Y_1 Y_2 Y_3 …. Y_m]' where ' = transpose mean = u and and ∑ = covariance

Z = N_1 * Y_1 + N_2 * Y_2 + …. + N_m*Y_m all N are numbers Find the covariance of Z E[ (Y- E[Y] )(Y - E[Y] ) ] = E[YY'] -E[Y]E[Y]'= [N_1 N_2 .. N_m] [∑ - u^2 ….∑ -u^2] ' This seems incorrect.
 
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  • #2
If you want the covariance of Z why do you look at the covariance of Y?

Think about this: your Z can be written as

[tex]
Z = \begin{pmatrix} N_1 & N_2 & \cdots & N_m \end{pmatrix} %
\begin{pmatrix} Y_1 \\ Y_2 \\ \vdots \\ Y_m \end{pmatrix}
[/tex]

so you should be able to use properties of expectations for random vectors to simplify your work.
 

Related to Random vector mean and covariance

1. What is a random vector mean?

A random vector mean is the average value of a collection of random variables. It represents the central tendency of the random vector and is calculated by taking the sum of all the values and dividing by the number of variables.

2. How is the random vector mean calculated?

The random vector mean is calculated by taking the sum of all the values in the vector and dividing by the number of variables. This can be represented mathematically as mean = (x1 + x2 + ... + xn)/n, where x1, x2, ..., xn are the random variables and n is the number of variables in the vector.

3. What is the significance of the random vector mean?

The random vector mean is an important measure in statistics as it represents the average value of a collection of random variables. It can be used to understand the central tendency of a data set and make predictions about future values.

4. What is a covariance matrix?

A covariance matrix is a square matrix that measures the relationship between two or more random variables. It shows how much two variables vary together, and in which direction. A positive covariance indicates that the variables are positively related, while a negative covariance indicates a negative relationship.

5. How is the covariance matrix used in statistics?

The covariance matrix is used to calculate the covariance between two or more random variables. It is an important tool in statistics as it helps to understand the relationship between variables and can be used to make predictions and in data analysis. It is also used in multivariate analysis and in the calculation of standard errors.

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