# Equivalent Variance-Stabilizing Function

I've been reading the book "Generalized Linear Models with Examples in R" by Dunn and Smyth.

In chapter 5, they claim:

Variance-stabilizing transformations h(y) used with linear regression models are roughly equivalent to fitting a glm with variance function V (μ) = 1/h (μ)2 and link function g(μ) = h(μ)

So, is it the same using the GLMs with its respective link functions than transforming Y in linear regression? Can you please explain this in simple and practical terms to a person without a very good foundation in stats theory like me?

The details are mostly explained a couple of pages earlier, at the start of sec 5.8, and that in turn refers back to chapter 3 (in particular, see sec 3.9, especially 3.9.2).

Consider $$\text{Var}(y)=\phi V(\mu)$$ and the transformation $$y^*=h(\mu)$$. What effect does that have on the variance, approximately?

Under suitable conditions, we can obtain an approximation of the moments of transformed random variables via Taylor expansion.

Using a one-term approximation as given at that Wikipedia link:

$$\text{Var}(y^*)=\text{Var}\!\left(h(y)\right)\approx \left(h'(\mu _{y})\right)^{2}\sigma _{y}^{2}=\left(h'(\mu _{y})\right)^{2}.\phi V(\mu _{y})$$

How do we stabilize variance? That is, how do we choose $$h$$ to make $$\text{Var}(y^*)$$ approximately constant? By making $$\left(h'(\mu _{y})\right)^{2}\propto V(\mu _{y})^{-1}$$ $$\ldots\:{(1)}$$, so that the two functions of $$\mu$$ in the expression $$\left(h'(\mu _{y})\right)^{2}\cdot\phi\cdot V(\mu _{y})$$ cancel, so that this approximation to the variance is then a constant function of $$\mu$$.

So now back to Sec 5.8. What GLM specification for $$y$$ approximately corresponds (in terms of mean and variance) to taking the transformation $$y^*=h(y)$$ and then fitting a constant-variance linear regression?

If $$E(y^*)=E[h(y)]$$ is linear in $$\eta = β_0 + β_1x_1 + ··· + β_px_p=X\beta$$ (e.g. see sec 1.6 between eqn 1.5 and 1.6 and the first equation in 4.6.1), then the mean as a function of the $$x$$'s needs to be such that $$g(\mu)=β_0 + β_1x_1 + ··· + β_px_p$$ (see sec 1.6 p 13)

But now consider just the first term in the Taylor expansion about the mean; $$E[h(y)] \approx h(\mu)$$ (see the section on the first moment in the Wikipedia link); so we will need that the link function $$g(\mu)\approx h(\mu)$$.

In the transformed regression we also had $$\text{Var}(y^*)=c$$, so if $$h$$ was a variance-stabilizing transformation, then $$\text{Var}(y)\propto 1/h'(μ)^2$$, just as in eqn $$(1)$$ above.