Sum of Chi-Squared Random Variables
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Theorem
Let $n_1, n_2, \ldots, n_k$ be strictly positive integers which sum to $N$.
Let $X_i \sim {\chi^2}_{n_i}$ for $1 \le i \le k$, where ${\chi^2}_{n_i}$ is the chi-squared distribution with $n_i$ degrees of freedom.
Then:
- $\ds X = \sum_{i \mathop = 1}^k X_i \sim {\chi^2}_N$
Proof
Let $Y \sim {\chi^2}_N$.
By Moment Generating Function of Chi-Squared Distribution, the moment generating function of $X_i$ is given by:
- $\map {M_{X_i} } t = \paren {1 - 2 t}^{-n_i / 2}$
Similarly, the moment generating function of $Y$ is given by:
- $\map {M_Y} t = \paren {1 - 2 t}^{-N / 2}$
By Moment Generating Function of Linear Combination of Independent Random Variables, the moment generating function of $X$ is given by:
- $\ds \map {M_X} t = \prod_{i \mathop = 1}^k \map {M_{X_i} } t$
We aim to show that:
- $\map {M_X} t = \map {M_Y} t$
By Moment Generating Function is Unique, this ensures $X = Y$.
We have:
\(\ds \map {M_X} t\) | \(=\) | \(\ds \prod_{i \mathop = 1}^k \paren {1 - 2 t}^{-n_i / 2}\) | ||||||||||||
\(\ds \) | \(=\) | \(\ds \paren {1 - 2 t}^{-\paren {n_1 + n_2 + \ldots + n_k} / 2}\) | ||||||||||||
\(\ds \) | \(=\) | \(\ds \paren {1 - 2 t}^{-N / 2}\) | ||||||||||||
\(\ds \) | \(=\) | \(\ds \map {M_Y} t\) |
$\blacksquare$