# Intuition for $RSS_2 - RSS_1$ having chi-square distribution in F-test for linear models

In https://en.wikipedia.org/wiki/F-test#Regression_problems, an application of the F-statistic to comparing linear models is given:

Consider two models, 1 and 2, where model 1 is 'nested' within model 2. Model 1 is the restricted model, and model 2 is the unrestricted one. That is, model 1 has p1 parameters, and model 2 has p2 parameters, where p1 < p2, and for any choice of parameters in model 1, the same regression curve can be achieved by some choice of the parameters of model 2. [...] If there are n data points to estimate parameters of both models from, then one can calculate the F statistic, given by

$$\frac{RSS_1-RSS_2}{p_2-p_1} / \frac{RSS_2}{n - p_2}$$

I understand why $$RSS_2$$ is chi-square distributed with degrees of freedom $$n - p_2$$: this is because in linear regression we project the data onto a $$p_2$$ dimensional space, leaving the residual error to reside in an $$n-p_2$$ dimensional space.

My question is why is $$RSS_1 - RSS_2$$ Chi-square distributed with $$p_2-p_1$$ degrees of freedom? It is true that $$RSS_1$$ and $$RSS_2$$ are chi-square distributed, but in general, I think the difference of two chi-squared distributions is not chi-square distributed. So this is a confusing claim to me.

I tried looking at the proof here: Proof that F-statistic follows F-distribution but it went above my head. I'm not looking for a completely rigorous proof, but I am looking for an explanation that I can grok that doesn't also wrongly (?) imply that the difference of any two chi-squared distributions is chi-squared, probably somehow using the fact that the models are nested.

Below we assume we have a model $$y=Xb+e$$ where the components of vector $$e$$ are distributed iid $$N(0, \sigma^2)$$ and that $$H$$ is the projection matrix onto the range of $$X$$. Use a subscript of $$r$$ to denote corresponding quantities of the restricted model and use $$\hat{e} = (I-H)y$$ to mean the estimated value of the unknown $$e$$.

We will make use of some facts about projections shown in the Appendix at the end.

Note that

$$RSS = \hat{e}'\hat{e} = y'(I-H)y = e'(I-H)e$$

where we have used the fact that

$$(I-H)y = (I-H)(Xb+e) = (I-H)e$$

Now applying that to both $$RSS$$ and $$RSS_r$$ we have that

$$RSS_r - RSS = e'(I-H_r)e - e'(I-H)e = e'(H-H_r)e$$

$$H$$ and $$H_r$$ are projections and their difference is a projection too because the space that $$H_r$$ projects onto is nested within the space associated with $$H$$ by our assumptions. Thus $$RSS_r - RSS$$ is of the form $$e'Pe$$ where $$P = H-H_r$$ is an orthogonal projection.

Now it is known that if the components of vector $$x$$ are iid $$N(0, 1)$$, which is the case for $$e/\sigma$$ by assumption, and $$Q$$ is any orthogonal projection that $$x'Qx$$ is chi-squared with degrees of freedom equal to the dimension of the space onto which $$Q$$ projects (and is also equal to the rank of $$Q$$ and also equal to $$trace(Q)$$). Thus $$(RSS-RSS_r)/\sigma^2$$ is chi-squared.

## Appendix

An orthogonal projection matrix $$Q$$ is a matrix which satisfies $$Q = Q' = Q^2$$. That is it is symmetric and idempotent. This implies that $$||Qx||^2 = x'Q'Qx = x'Qx$$ where $$||.||^2$$ means squared length.

If $$Q$$ is an orthogonal projection then so is $$I-Q$$.

The range of a matrix is the set of values it maps to. An orthogonal projection is said to project onto its range.

If an orthogonal projection $$Q$$ projects onto the range of matrix $$M$$ then $$QM=M$$ and $$(I-Q)M = 0$$.

If $$Q$$ and $$Q_0$$ are orthogonal projections such that $$Q_0$$ projects onto a subspace of the set that $$Q$$ projects onto then $$Q$$ and $$Q_0$$ commute and $$Q-Q_0$$ is an orthogonal projection too. Also $$rank(Q-Q_0) = rank(Q) - rank(Q_0)$$.

Since, there is a sense of ambiguity as to how the 'difference' of residuals of the restricted and unrestricted models leads to the $$F$$-statistic, it is deemed to be apt enough to briefly sketch the development in a general setting which would provide a better insight.

Theorem $$[\rm I]:$$ If $$\mathbf x\sim \mathsf{MVN}(\boldsymbol\mu,\mathbf V),$$

$$\mathbf x^\mathsf T\mathbf A\mathbf x\sim{\chi^2}^\prime\left[r(\mathbf A),\frac12\boldsymbol\mu^\mathsf T\mathbf A\boldsymbol \mu\right]\iff \mathbf{AV}~\text{idempotent}.^1$$

Consider the imposition of the constraint $$\mathbf K^\mathsf T\mathbf b = \mathbf m\tag 1$$ on the model $$\mathbf y = \mathbf{Xb} +\mathbf e,$$ where $$\bf b$$ is vector of parameters of order $$k,~\mathbf K^\mathsf T$$ is a matrix of order $$s\times k$$ along with the condition $$r\left(\mathbf K^\mathsf T\right) = s, ~\mathbf m$$ is a constant vector.

What's the effect of $$(1)$$ on the model? How should the estimator and the associated sum of squares look?

In oder to find the estimator of $$\mathbf b,~\left(\tilde{\mathbf b}\right)$$ subject to the constraint, one can employ Lagrange multiplier $$(2\boldsymbol\lambda)$$ in the minimisation of $$\left(\mathbf y -\mathbf X\tilde{\mathbf b}\right)^\mathsf T\left(\mathbf y -\mathbf X\tilde{\mathbf b}\right)+ 2\boldsymbol\lambda^\mathsf T\left(\mathbf K^\mathsf T\tilde{\mathbf b }-\mathbf m\right)\tag 2$$ w.r.t. $$\tilde{\mathbf b}, ~\boldsymbol\lambda.$$ Solving the equations

\begin{align} \mathbf X^\mathsf T\mathbf X\tilde{\mathbf b} + \mathbf K\boldsymbol\lambda &= \mathbf X^\mathsf T\mathbf y\\ \mathbf K^\mathsf T\tilde{\mathbf b } &=\mathbf m, \end{align}

yield

$$\tilde{\mathbf b }=\hat{\mathbf b }-(\mathbf X^\mathsf T\mathbf X)^{-1}\mathbf K\left[\mathbf K^\mathsf T(\mathbf X^\mathsf T\mathbf X)^{-1}\mathbf K\right]^{-1}\left(\mathbf K^\mathsf T\hat{\mathbf b }-\mathbf m\right).\tag 3$$

What would be the residual sum of squares? Computing $$\left(\mathbf y -\mathbf X\tilde{\mathbf b}\right)^\mathsf T\left(\mathbf y -\mathbf X\tilde{\mathbf b}\right)$$ using the fact that $$\mathbf X^\mathsf T(\mathbf y -\mathbf X\hat{\mathbf b})= 0$$ would lead to

\begin{align}&= \left[\mathbf y -\mathbf X\hat{\mathbf b}+ \mathbf X\left(\hat{\mathbf b}-\tilde{\mathbf b}\right)\right]^\mathsf T\left[\mathbf y -\mathbf X\hat{\mathbf b}+ \mathbf X\left(\hat{\mathbf b}-\tilde{\mathbf b}\right)\right]\\&= \left( \mathbf y -\mathbf X\hat{\mathbf b}\right) ^\mathsf T \left( \mathbf y -\mathbf X\hat{\mathbf b}\right)+ \left(\hat{\mathbf b}-\tilde{\mathbf b}\right)^\mathsf T\mathbf X^\mathsf T\mathbf X\left(\hat{\mathbf b}-\tilde{\mathbf b}\right)\\ &\stackrel{(3)}{=} \textrm{SSE} +\underbrace{(\mathbf K^\mathsf T\hat{\mathbf b }-\mathbf m)^\mathsf T\left[\mathbf K^\mathsf T(\mathbf X^\mathsf T\mathbf X)^{-1}\mathbf K\right]^{-1}(\mathbf K^\mathsf T\hat{\mathbf b }-\mathbf m)}_{:= Q};\tag 4 \end{align}

so

$$\textrm{residual(reduced)} = \textrm{residual(full)} + Q. \tag 5^2$$

What is the distribution of $$Q?$$

Notice that $$\hat{\mathbf b}~\sim\mathsf{MVN}\left(\mathbf b, (\mathbf X^\prime\mathbf X) ^{-1}\sigma^2\right)$$ as $$\mathbf y \sim \mathsf{MVN}(\mathbf X\mathbf b,\sigma^2\mathbf I).$$ Therefore $$\mathbf K^\mathsf T\hat{\mathbf b }-\mathbf m\sim\mathsf{MVN}\left(\mathbf K^\mathsf T\mathbf b-\mathbf m,\mathbf K^\mathsf T (\mathbf X^\prime\mathbf X) ^{-1} \mathbf K \sigma^2\right).\tag 6$$ $$Q$$ can be seen to be a quadratic in $$\mathbf K^\mathsf T\hat{\mathbf b}-\mathbf m,$$ with $$\left[\mathbf K^\mathsf T (\mathbf X^\prime\mathbf X) ^{-1} \mathbf K\right]^{-1}$$ as the matrix of the quadratic. Now is the crucial part: apply Theorem $$\rm[I]$$ here (what are $$\bf A, V$$ here?) using $$(6)$$ to conclude that $$\frac Q{\sigma^2}\sim{\chi^2}^\prime\left[s,\frac{(\mathbf K^\mathsf T\hat{\mathbf b }-\mathbf m)^\mathsf T\left[\mathbf K^\mathsf T(\mathbf X^\mathsf T\mathbf X)^{-1}\mathbf K\right]^{-1}(\mathbf K^\mathsf T\hat{\mathbf b }-\mathbf m)}{2\sigma^2}\right].\tag 7$$ This is the desired result in a general framework. For constructing the required $$F$$-statistic, it has to be deduced that $$Q$$ and $$\textrm{SSE}$$ are independent.

## Notes:

$$[1]$$ To prove this, compute the MGF of the quadratic form and use the property of the eigenvalues of an idempotent matrix.

$$[2]$$ It is tempting, perhaps, to interpret $$Q,$$ writing it in the form \begin{align}Q &= \mathbf y^\mathsf T\mathbf y -\text{SSE} -\left[ \mathbf y^\mathsf T\mathbf y - (\text{SSE}+ Q) \right]\\&=\text{SSR}- \left[ \mathbf y^\mathsf T\mathbf y - (\text{SSE}+ Q) \right]\\ &= \textrm{reduction(full)} - \left[ \mathbf y^\mathsf T\mathbf y - (\text{SSE}+ Q) \right],\tag{N1} \end{align} as the "reduction in sum of squares due to fitting of the reduced model" along the line of $$(5);$$ but this is not true, in general. In fact, the term in parenthesis in $$\rm(N1)$$ need not be even a sum of squares.

## Reference:

Linear Models, S. R. Searle, John Wiley & Sons., $$1971.$$