Asymptotic distribution of the Pearson chi-square statistic

I recently learned of a fairly succinct proof for the asymptotic distribution of the Pearson chi-square statistic (from Chapter 9 of Reference 1), which I share below.

First, the set-up: Assume that we have n independent trials, and each trial ends in one of J possible outcomes, which we label (without loss of generality) as 1, 2, \dots, J. Assume that for each trial, the probability of the outcome being j is p_j > 0. Let n_j denote that number of trials that result in outcome j, so that \sum_{j=1}^J n_j = n. Pearson’s \chi^2-statistic is defined as

\begin{aligned} \chi^2 = \sum_{\text{cells}} \dfrac{(\text{obs} - \text{exp})^2}{\text{exp}} = \sum_{j=1}^J \dfrac{(n_j - np_j)^2}{np_j}. \end{aligned}

Theorem. As n \rightarrow \infty, \chi^2 \stackrel{d}{\rightarrow} \chi_{J-1}^2, where \stackrel{d}{\rightarrow} denotes convergence in distribution.

Before proving the theorem, we prove a lemma that we will use:

Lemma. Let \mathbf{X} \in \mathbb{R}^{n \times n} have distribution \mathbf{X} \sim \mathcal{N}(0, \mathbf{\Sigma}). Then \mathbf{X}^\top \mathbf{X} has \chi_r^2 distribution if and only if \mathbf{\Sigma} is idempotent  (i.e. a projection) with rank r.

(Note: We call \mathbf{X} a projection matrix if \mathbf{X} is idempotent.)

Proof of Lemma: Since \mathbf{\Sigma} is real and symmetric, it is orthogonally diagonalizable, i.e. there is an orthogonal matrix \mathbf{U} and a diagonal matrix \mathbf{D} such that \mathbf{D} = \mathbf{U \Sigma U}^\top. Let \mathbf{Y} = \mathbf{UX}. Since \mathbf{X} \sim \mathcal{N}(0, \mathbf{\Sigma}), \mathbf{Y} \sim \mathcal{N}(0, \mathbf{U\Sigma U}^\top) = \mathcal{N}(0, \mathbf{D}). Furthermore, \mathbf{Y}^\top \mathbf{Y} = \mathbf{X}^\top \mathbf{U}^\top \mathbf{U} \mathbf{X} = \mathbf{X}^\top \mathbf{X}. Thus,

\begin{aligned} &\mathbf{\Sigma}^2 = \mathbf{\Sigma} \text{ and } \mathbf{\Sigma} \text{ has rank } r \\  \Leftrightarrow \quad& \mathbf{D}^2 = \mathbf{D} \text{ and } \mathbf{D} \text{ has rank } r \\  \Leftrightarrow \quad& \mathbf{D} \text{ has } r \text{ ones and } n-r \text{ zeros on its diagonal} \\  \Leftrightarrow \quad& \mathbf{Y}^\top \mathbf{Y} \sim \chi_r^2 \\  \Leftrightarrow \quad& \mathbf{X}^\top \mathbf{X} \sim \chi_r^2. \end{aligned}

We are now ready to prove the theorem.

Proof of Theorem (asymptotic distribution of Pearson \chi^2 statistic): For each j \in J, let \mathbf{e}_j denote the vector in \mathbb{R}^J with all zeros except for a one in the jth entry. Let \mathbf{X}_i be equal to \mathbf{e}_j if the ith trial resulted in outcome j. Then \mathbf{X}_1, \dots, \mathbf{X}_n are i.i.d. with the multinomial distribution: \mathbb{E}[\mathbf{X}_i] = \mathbf{p} and \text{Cov}(\mathbf{X}_i) = \mathbf{\Sigma}, where

\begin{aligned} \mathbf{p} &= \begin{pmatrix} p_1 \\ \vdots \\ p_J \end{pmatrix}, \\  \mathbf{\Sigma} &= \begin{pmatrix} p_1 (1-p_1) & -p_1 p_2 & \dots & -p_1 p_J \\ -p_1 p_2 & p_2 (1-p_2) & \dots & -p_2 p_J \\ \vdots & \vdots & & \vdots \\ -p_1 p_J & -p_2 p_J & \dots & p_J (1-p_J) \end{pmatrix} = \text{diag}(\mathbf{p}) - \mathbf{p}\mathbf{p}^T. \end{aligned}

Let \mathbf{P} = \text{diag}(\mathbf{p}). We can rewrite the Pearson \chi^2 statistic as

\begin{aligned} \chi^2 &= \sum_{j=1}^J \dfrac{(n_j - np_j)^2}{np_j} \\  &= n \sum_{j=1}^J \dfrac{((n_j/n) - p_j)^2}{p_j} \\  &= n \left( \overline{\mathbf{X}}_n - \mathbf{p} \right)^\top \mathbf{P}^{-1} \left( \overline{\mathbf{X}}_n - \mathbf{p} \right). \end{aligned}

By the Central Limit Theorem, \sqrt{n} \left( \overline{\mathbf{X}}_n - \mathbf{p} \right) \stackrel{d}{\rightarrow} \mathbf{Y}, where \mathbf{Y} \sim \mathcal{N} (0, \mathbf{\Sigma}). Applying the Continuous Mapping Theorem,

\begin{aligned} \chi^2 = \sqrt{n} \left( \overline{\mathbf{X}}_n - \mathbf{p} \right)^\top \mathbf{P}^{-1} \sqrt{n} \left( \overline{\mathbf{X}}_n - \mathbf{p} \right)  \stackrel{d}{\rightarrow} \mathbf{Y}^T \mathbf{P}^{-1} \mathbf{Y}. \end{aligned}

If we define \mathbf{Z} = \mathbf{P}^{-1/2} \mathbf{Y}, then \mathbf{Z} \sim \mathcal{N}(0, \mathbf{P}^{-1/2} \mathbf{\Sigma} \mathbf{P}^{-1/2}) and \chi^2 \stackrel{d}{\rightarrow} \mathbf{Z}^\top \mathbf{Z}. By the lemma, it remains to show that \mathbf{P}^{-1/2} \mathbf{\Sigma} \mathbf{P}^{-1/2} is a projection matrix of rank J-1. We can write this matrix as

\begin{aligned} \mathbf{P}^{-1/2} \mathbf{\Sigma} \mathbf{P}^{-1/2} &= \mathbf{P}^{-1/2} \left( \mathbf{P}- \mathbf{p}\mathbf{p}^T \right) \mathbf{P}^{-1/2} \\  &= \mathbf{I} - \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2}. \end{aligned}

This matrix is a projection:

\begin{aligned} \left( \mathbf{I} - \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2} \right)^2 &= \mathbf{I} - 2 \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2} + \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2} \\  &= \mathbf{I} - 2 \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2} + \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2} \\  &=  \mathbf{I} - \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2}. \end{aligned}

We can compute the trace of the matrix:

\begin{aligned} \text{tr} \left[ (\mathbf{P}^{-1/2} \mathbf{p})(\mathbf{p}^T \mathbf{P}^{-1/2})\right] &= \text{tr} \left[(\mathbf{p}^T \mathbf{P}^{-1/2})(\mathbf{P}^{-1/2} \mathbf{p})\right] \\  &= \text{tr}(1) = 1, \\  \text{tr} \left( \mathbf{I} - \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2} \right) &= \text{tr}(\mathbf{I}) - \text{tr}\left( \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2} \right) = r-1. \end{aligned}

Since \mathbf{I} - \mathbf{P}^{-1/2} \mathbf{p}\mathbf{p}^T \mathbf{P}^{-1/2} is a projection matrix and can be shown to be symmetric, its rank is equal to its trace (proof here), i.e. its rank is r-1. This completes the proof.


  1. Ferguson, T. S. (1996). A Course in Large Sample Theory.

1 thought on “Asymptotic distribution of the Pearson chi-square statistic

  1. Pingback: General chi-square tests | Statistical Odds & Ends

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