# Distribution of Sum of Sample Mean and Sample Variance from a Normal Population.

Let $$X_i\sim^{iid} N(\mu, \sigma^2)$$. Let $$\bar X$$ and $$S^2$$ denote the usual, resp, sample mean and sample variance.

What is the distribution of $$\bar X+S^2$$?

Since we know that the sample mean and sample variance are independent in a Normal population, I guess my question would be equivalent to asking what is the resulting distribution of independent Normal distribution and a Chi-Squared distribution.

• By sample variance, do you mean $\ \frac{1}{n}\sum_\limits{i=1}^n\big(X_i-\overline{X}\big)^2\$ or the unbiassed estimate, $\ \frac{1}{n-1}\sum_\limits{i=1}^n\big(X_i-\overline{X}\big)^2\$, of the variance? Aug 31, 2021 at 2:03
• @lonzaleggiera which one you prefer. ;) Aug 31, 2021 at 8:05
• $\ \frac{1}{n-1}\sum_\limits{i=1}^n\big(X_i-\overline{X}\big)^2\$ is an unbiassed estimate of $\ \sigma^2\$, so it's what I would normally use. However, the term "sample variance" is not entirely unambiguous, since I have seen it used to refer to $\ \frac{1}{n}\sum_\limits{i=1}^n\big(X_i-\overline{X}\big)^2\$ as well. Aug 31, 2021 at 9:32

Let $$n$$ be the sample size. Since $$S^2$$ is calculated from the sample generated by the $$X_i$$ we know that

$$X_i \sim N(\mu, \sigma^2)$$ $$\frac{(n-1)S^2}{\sigma^2}\sim \chi^2(n-1)$$

The distribution of the sum of a Gaussian rv and a Chi-Squared rv is an instance of the Generalized Chi-Squared Distribution. A variable $$\xi$$ with the Generalized Chi-Squared Distribution can be defined as follows:

$$\xi = x +\sum_1^n w_i y_i \text { where } x\sim N(m,s),\;\; w_i \in \mathbb{R},\;\;y_i \sim \chi'^2(k_i,\lambda_i)\text{ and } y_i \text { independent}$$

Note that $$\chi'^2(k_i,\lambda_i)$$ is the non-central Chi-squared distribution, it is related to the Chi-squared as follows:

$$\chi^2(n) = \chi'^2(n,0)$$

In your case, we want the sum of a single Chi-Squared variable and a Normal, where the Chi-Squared is based on the sample variance of the sample from the Normal random variable:

$$\xi = x + wy \;\;\text{ where } x\sim N\left(\mu,\frac{\sigma^2}{n}\right), \;y \sim \chi'^2(n-1,0)$$

What about the weight variable $$w$$? If $$w=1$$ then $$\xi$$ represents the following sum:

$$\bar{X} + \frac{(n-1)S^2}{\sigma^2}$$

But we want:

$$\bar{X} + S^2$$

Therefore, to get to this sum, we need to alter the weight applied to the chi-squared variable $$y$$:

$$w = \frac{\sigma^2}{n-1}$$

With this we have what we need:

$$\bar{X} + S^2 \sim \xi = x + wy \;\;\\\text{ where } x\sim N\left(\mu,\frac{\sigma^2}{n}\right), \;y \sim \chi'^2(n-1,0),\;w = \frac{\sigma^2}{n-1}$$

In summary

$$\bar{X} + S^2 \sim \tilde{\chi}^2\left(\frac{\sigma^2}{n-1},n-1,0,\mu,\sigma\right)$$