Transpose of the product of matrices problem

In summary, the conversation discusses an aspect of matrix algebra in linear regression models and the confusion around the transpose identity in one of the equations. There is a question about why a certain quantity is equal to its transpose, and whether or not it needs to be a symmetric matrix.
  • #1
EdMel
13
0
Hi,

The following equations are from linear regression model notes but there is an aspect of the matrix algebra I do not get.

I have, [itex]\mathbf{y}[/itex] and [itex]\tilde{\beta}[/itex] are a mx1 vectors, and [itex] \mathbf{X}[/itex] is a mxn matrix.

I understand the equation
[tex](\mathbf{y}-\mathbf{X}\tilde{\beta})^{\text{T}}(\mathbf{y}-\mathbf{X}\tilde{\beta})= \mathbf{y}^{\text{T}}\mathbf{y}-\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{y}-\mathbf{y}^{\text{T}}\mathbf{X}\tilde{\beta}+ \tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{X}\tilde{\beta}
[/tex]
, but then it is stated
[tex]\mathbf{y}^{\text{T}}\mathbf{y}-\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{y}-\mathbf{y}^{\text{T}}\mathbf{X}\tilde{\beta}-\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{X}\tilde{\beta}= \mathbf{y}^{\text{T}}\mathbf{y}-2\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{y}+\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{X}\tilde{\beta}\qquad\text{(1)}[/tex]
, and I do not understand why [itex]-\mathbf{y}^{\text{T}}\mathbf{X}\tilde{\beta}=-\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{y}[/itex] in equation (1).

I understand the transpose identity [itex](\mathbf{y}^{\text{T}}\tilde{\beta}\mathbf{X})^{\text{T}}= \mathbf{X}^{\text{T}}\tilde{\beta}^{\text{T}}\mathbf{y}[/itex],
but then (1) would be
[tex]\mathbf{y}^{\text{T}}\mathbf{y}-\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{y}-\mathbf{y}^{\text{T}}\mathbf{X}\tilde{\beta}-\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{X}\tilde{\beta}= \mathbf{y}^{\text{T}}\mathbf{y}-\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{y}-(\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{y})^{\text{T}}+ \tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{X}\tilde{\beta}[/tex],
and (1) would only be true if [itex]\tilde{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{y}[/itex] is s symmetric matrix, which I think it need not be.

What am I missing here?

Thanks in advance,

Ed
 
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  • #2
Hey EdMel.

Hint: Is the quantity a scalar? (If it is then what does this imply about the appropriate transpose?)
 

1. What is the transpose of the product of two matrices?

The transpose of the product of two matrices A and B is equal to the product of the transpose of B and the transpose of A, often written as (AB)^T = B^T A^T. This follows the general rule for transposing a product of matrices, where the order of the matrices is reversed and the individual matrices are also transposed.

2. How is the transpose of a matrix defined?

The transpose of a matrix is obtained by interchanging the rows and columns of the original matrix. This means that the first row of the original matrix becomes the first column of the transpose, the second row becomes the second column, and so on.

3. Can the transpose of a product of matrices be simplified?

Yes, the transpose of a product of matrices can be simplified by using the properties of transpose operations. For example, (AB)^T = B^T A^T and (A + B)^T = A^T + B^T. These properties can be helpful in simplifying complex expressions involving transposes.

4. What is the significance of the transpose of a product of matrices?

The transpose of a product of matrices has several uses in mathematics and applied sciences. It can be used to solve systems of linear equations, find the inverse of a matrix, and perform various transformations in geometry and physics. It is also a crucial operation in matrix algebra and is used in many applications such as data analysis and computer graphics.

5. How is the transpose of a product of matrices calculated?

To calculate the transpose of a product of matrices, first calculate the product of the two matrices. Then, transpose the resulting matrix by switching the rows and columns. Alternatively, you can use the properties of transpose operations to simplify the expression and calculate the transpose of each individual matrix before multiplying them together.

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