Suppose we know the variance of some estimator $\mathbf{\hat{\theta}}=\hat{\theta}{1},...,\hat{\theta}{r}$
But, we want to find the variance of some other estimator which is a function of $\mathbf{\theta}$, say $g(\mathbf{\hat{\theta}})$
Example functions
$g(\mathbf{\hat{\theta}})=\log[\theta]$
$g(\mathbf{\hat{\theta}})=\theta^2$
The Delta Method can help us estimate $\widehat{Var}[g(\mathbf{\hat{\theta}})]$ from $\widehat{Var}[\mathbf{\hat{\theta}}]$ if we can find
$$\frac{dg(\theta)}{d\theta}$$
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$$ Var\left[g(\hat{\theta})\right]\approx \sum_{i=1}^{r} \left[\frac{\partial g(\mathbf{\theta})}{\partial \theta_{i}}\right]^{2} Var(\hat{\theta_{i}})+\sum_{i=1}^r \mathop{\sum^{r}{j=1}}{i\ne j}\left[\frac{\partial g(\mathbf{\theta})}{\partial \theta_{i}}\right]\left[\frac{\partial g(\mathbf{\theta})}{\partial \theta_{j}}\right] Cov(\hat{\theta}{i}, \hat{\theta}{j}) $$
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