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In probability theory, Lindeberg's condition is a sufficient condition (and under certain conditions also a necessary condition) for the central limit theorem to hold for a sequence of independent random variables. Unlike the classical central limit theorem, which requires that the random variables in question have finite mean and variance and be both independent and identically distributed, it only requires that they have finite mean and variance and be independent. It is named after the Finnish mathematician Jarl Waldemar Lindeberg.

Statement

Let $(\mathrm{\Omega },\mathcal{ℱ},\mathbb{\mathbb{P}})$ be a probability space, and $X_k:\mathrm{\Omega }\to \mathbb{ℝ},k\in \mathbb{\mathbb{N}}$, be independent random variables defined on that space. Assume the expected values $\mathbb{𝔼}{X}_k={\mu }_k$ and variances $\mathrm{V}\mathrm{a}\mathrm{r}{X}_k={\sigma }_k^2$ exist and are finite. Also let $s_n^2:=\sum _{k=1}^n{\sigma }_k^2.$

If this sequence of independent random variables $X_k$ satisfies the Lindeberg's condition:

$\underset{n\to \mathrm{\infty }}{\lim }\frac{1}{s_n^2}\sum _{k=1}^n\underset{\{|X_k-{\mu }_k|>\epsilon s_n\}}{\int }(X_k-{\mu }_k{)}^2\mathrm{d}\mathbb{\mathbb{P}}=0,\text{ for all }\epsilon >0,$

(where the integral is a Lebesgue integral over the set $\{|X_k-{\mu }_k|>\epsilon s_n\}:=\{\omega \in \mathrm{\Omega }:|X_k(\omega )-{\mu }_k|>\epsilon s_n\}$), then the central limit theorem holds, i.e. the random variables

$Z_n:=\frac{\sum _{k=1}^n(X_k-{\mu }_k)}{s_n}$

converge in distribution to a standard normal random variable as $n\to \mathrm{\infty }.$

Lindeberg's condition is sufficient, but not in general necessary (i.e. the inverse implication does not hold in general). However, if the sequence of independent random variables in question satisfies

${\max }_{k=1,\dots ,n}\frac{{\sigma }_k^2}{s_n^2}\to 0,\text{ as }n\to \mathrm{\infty },$

then Lindeberg's condition is both sufficient and necessary, i.e. it holds if and only if the result of central limit theorem holds.

Interpretation

Because the Lindeberg condition implies ${\max }_{k=1,\dots ,n}\frac{{\sigma }_k^2}{s_n^2}\to 0$ as $n\to \mathrm{\infty }$, it guarantees that the contribution of any individual random variable $X_k$ ($1\le k\le n$) to the variance $s_n^2$ is arbitrarily small, for sufficiently large values of $n$.

See also

• Lyapunov condition
• Central limit theorem

References

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• 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.
  
  My blog: http://www.primaboinca.com/view_profile.php?userid=5889534

• 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.
  
  My blog: http://www.primaboinca.com/view_profile.php?userid=5889534

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