ThmDex – An index of mathematical definitions, results, and conjectures.
Central limit theorem for I.I.D. sample mean series
Formulation 0
Let $X_1, X_2, X_3, \dots \in \text{Random}(\mathbb{R})$ each be a D3161: Random real number such that
(i) $X_1, X_2, X_3, \dots$ is an D3358: I.I.D. random collection
(ii) \begin{equation} \mathbb{E} |X_1|^2 \in (0, \infty) \end{equation}
(iii) \begin{equation} \mu : = \mathbb{E} X_1 \end{equation}
(iv) \begin{equation} \sigma^2 : = \text{Var} X_1 \end{equation}
(v) \begin{equation} \overline{X}_N : = \frac{1}{N} \sum_{n = 1}^N X_n \end{equation}
Then \begin{equation} \sqrt{N} \left( \frac{\overline{X}_N - \mu}{\sigma} \right) \overset{d}{\longrightarrow} \text{Gaussian}(0, 1) \quad \text{ as } \quad N \to \infty \end{equation}
Proofs
Proof 0
Let $X_1, X_2, X_3, \dots \in \text{Random}(\mathbb{R})$ each be a D3161: Random real number such that
(i) $X_1, X_2, X_3, \dots$ is an D3358: I.I.D. random collection
(ii) \begin{equation} \mathbb{E} |X_1|^2 \in (0, \infty) \end{equation}
(iii) \begin{equation} \mu : = \mathbb{E} X_1 \end{equation}
(iv) \begin{equation} \sigma^2 : = \text{Var} X_1 \end{equation}
(v) \begin{equation} \overline{X}_N : = \frac{1}{N} \sum_{n = 1}^N X_n \end{equation}
We can write \begin{equation} \begin{split} \sum_{n = 1}^N \frac{X_n - \mu}{\sqrt{\sigma^2 N}} & = \frac{1}{\sigma} \frac{1}{\sqrt{N}} \left( \sum_{n = 1}^N X_n - \sum_{n = 1}^N \mu \right) \\ & = \frac{1}{\sigma} \frac{\sqrt{N}}{N} \left( \sum_{n = 1}^N X_n - \sum_{n = 1}^N \mu \right) \\ & = \sqrt{N} \left( \frac{\overline{X}_N - \mu}{\sigma} \right) \\ \end{split} \end{equation} Hence, this result is a reformulation of R3843: I.I.D. real central limit theorem with the identity index sequence. $\square$