Inverse Matrix of X^T X: Necessary and Sufficient Conditions
📂Matrix AlgebraInverse Matrix of X^T X: Necessary and Sufficient Conditions
Theorem
Suppose that the matrix X∈Rm×n is given and m≥n, then the following holds:
∃(XTX)−1⟺rankX=n
In other words, the necessary and sufficient condition for the inverse matrix of XTX to exist is that X has full rank.
Explanation
The reason this fact is important is that in overdetermined systems like y=Xβ in multiple regression analysis, you often need to perform calculations such as
β=(XTX)−1XTy
…not just often, but really a lot. Situations where one encounters problems due to not knowing this fact, or forgetting it during actual analysis, are also quite common.
Proof
(⟹)
If the inverse of XTX∈Rn×n exists, then it is rankXTX=n.
n=≤≤≤rankXTXrankXmin{n,m}n
For the above inequality to hold, it must be rankX=n.
(⟸)
For some u∈Rn, let
XTXu=0
Let us assume that u=0, which is equivalent to the existence of (XTX)−1. We need to show that u is a zero vector. Since Rn is an inner product space, we can calculate the vector dot product of XTXu and 0: ⟨⋅,⋅⟩. As this is the inner product of zero vectors, it is obviously 0, and we obtain
0=====⟨XTXu,u⟩(XTXu)TuuTXTXu(Xu)TXu⟨Xu,Xu⟩
This implies Xu=0. Given that X is assumed to have full rank, it must be u=0.
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See Also