I am stuck at the following exercise:

Let $(X_k)_k$ be a sequence of i.i.d. Bernoulli$(p)$ distributed RVs with $p=1/2$. We want to estimate $Var(X_1) = p(1-p) = p^2 =: \tau_p^2$ by directly plugging in the natural estimator $\widetilde{p} := \frac{1}{n}\sum_{i=1}^n X_i$ of $p$. We call this estimator for the variance $\widetilde{\tau}^2_{p,n}$. Examine the limit distribution of $\widetilde{\tau}^2_{p,n}$.

I do not see how I could do this. I recognise that we are basically looking at a uniform distribution of two events and I see that $\widetilde{\tau}^2_{p,n}$ has a rather nice formula

$$\widetilde{\tau}^2_{p,n} = \frac{1}{n^2} \biggl(\sum_{i=1}^n X_i \biggr) ^2 = \overline{X}^2,$$

but I do not see how to proceed now. Intuitively I would say that $\widetilde{\tau}^2_{p,n}$ should also be uniformly distributed, but how can I prove this?

  • $\begingroup$ To see that your intuition is incorrect, consider that the distribution of $\tilde p$ must be $1/n$ times a Binomial$(n,p)$ distribution, which for $n\gt 1$ is far from uniform, suggesting (correctly) the distribution of $\tilde{\tau}^2_{p,n}$ is nonuniform, too. BTW, the limit distribution of $\tilde{\tau}^2_{p,n}$ is the constant $1/4$ -- but that's a trivial and almost worthless observation. The question likely wants you to standardize the distribution. $\endgroup$
    – whuber
    Nov 10, 2020 at 22:57
  • $\begingroup$ Could you use a Monte Carlo simulation? $\endgroup$ Nov 10, 2020 at 23:03

1 Answer 1


Firstly, let's start by fixing your estimator. Although $p(1-p)=p^2$ in the special case where $p=\tfrac{1}{2}$, in the context of parameter estimation, we don't know the parameter $p$, and so we don't know that $p(1-p)=p^2$. Consequently, the plug-in variance estimator is $\tilde{\tau}^2_{n} = \tilde{p}_n (1-\tilde{p}_n)$, not $\tilde{p}_n^2$. I will show an asymptotic approximation derived using the central limit theorem to approximate the true distribution function for the estimator. This is quite a tricky problem, and it has a few parts, but it leads to quite a useful asymptotic form.

Let's start by writing the variance estimator out in terms of the number of "successes" in the underlying Bernoulli random variables. Let $K_n \equiv \sum_{i=1}^n X_i$ denote the number of successes. Then we can write the plug-in estimator as:

$$\tilde{\tau}^2_{n} = \tilde{p}_n (1-\tilde{p}_n) = \frac{1}{n^2} (n K_n - K_n^2).$$

We will also note that, in the present case where $p=\tfrac{1}{2}$, we can use the central limit theorem to obtain the asymptotic distribution:

$$Z_n \equiv 2 \sqrt{n} \Bigg( \frac{K_n}{n} - \frac{1}{2} \Bigg) \sim \text{N}(0,1).$$

The range of possible values for the variance estimator is $0 \leqslant \tilde{\tau}^2_{n} \leqslant \tfrac{1}{4}$. For all $0 \leqslant t \leqslant \tfrac{1}{4}$, the cumulative distribution function for this estimator is given by:

$$\begin{align} \mathbb{P}(\tilde{\tau}^2_{n} \leqslant t) &= \mathbb{P}(n K_n - K_n^2 \leqslant t n^2) \\[14pt] &= \mathbb{P}(K_n^2 - n K_n + t n^2 \geqslant 0) \\[8pt] &= 1 - \mathbb{P} \Bigg( \frac{1 - \sqrt{1-4t}}{2} < \frac{K_n}{n} < \frac{1 + \sqrt{1-4t}}{2} \Bigg) \\[6pt] &= 1 - \mathbb{P} \Bigg( - \frac{\sqrt{1-4t}}{2} < \frac{K_n}{n} - \frac{1}{2} < \frac{\sqrt{1-4t}}{2} \Bigg) \\[6pt] &= 1 - \mathbb{P} \Bigg( - \sqrt{(1-4t)n} < 2 \sqrt{n} \Bigg( \frac{K_n}{n} - \frac{1}{2} \Bigg) < \sqrt{(1-4t)n} \Bigg) \\[8pt] &= 1 - \mathbb{P} \Big( - \sqrt{(1-4t)n} < Z_n < \sqrt{(1-4t)n} \Big). \\[6pt] \end{align}$$

(The third step in this equation follows from re-arrangement of the quadratic inequality; full working shown below.) When $n$ is large we have the asymptotic approximation $Z_n \sim \text{N}(0,1)$ so we get the asymptotic approximation $\mathbb{P}(\tilde{\tau}^2_{n} \leqslant t) \approx F_\text{asymp}(t)$ given by:

$$F_\text{asymp}(t) = 1 - \frac{\Phi ( \sqrt{(1-4t)n} ) - \Phi ( -\sqrt{(1-4t)n} )}{\Phi (\sqrt{n}) - \Phi (-\sqrt{n})}.$$

(Note that I have added a denominator here to obtain a distribution function that is valid for all $n$ over the range $0 \leqslant t \leqslant \tfrac{1}{4}$. This form ensures that we get zero probability when $t=0$ and unit probability when $t = \tfrac{1}{4}$.) Differentiating then gives the asymptotic density:

$$\begin{align} f_\text{asymp}(t) &= \sqrt{\frac{n}{2 \pi (1-4t)}} \cdot \frac{\exp ( - (1-4t)n/2 )}{\Phi (\sqrt{n}) - \Phi (-\sqrt{n})} \cdot \mathbb{I}(0 \leqslant t \leqslant \tfrac{1}{4}) \\[6pt] &= \frac{\mathbb{I}(0 \leqslant 1-4t \leqslant 1)}{\Phi (\sqrt{n}) - \Phi (-\sqrt{n})} \cdot \text{Ga} \Big( 1-4t \ \Big| \ \text{Shape} = \frac{1}{2}, \ \text{Rate} = \frac{n}{2} \Big). \\[6pt] \end{align}$$

This means that our estimator has an asymptotic distribution that is a truncated gamma distribution:

$$1 - 4 \tilde{\tau}^2_{n} \overset{\text{Asymp}}{\sim} \text{TruncGamma} \Big( \text{Shape} = \frac{1}{2}, \ \text{Rate} = \frac{n}{2}, \ \text{Upper Bound} = 1 \Big).$$

This gives you an asymptotic density function based on approximating the binomial random variable $K_n$ using the central limit theorem. The asymptotic form is a continuous approximation to a discrete distribution, but it should give quite a good approximation to the true distribution when $n$ is large.

Working for the quadratic inequality: Using quadratic formula we can find the roots of the quadratic form shown in the inequality, which are:

$$r = n \cdot \frac{1 \pm \sqrt{1-4t}}{2}.$$

Consequently, we can write the quadratic form as:

$$K_n^2 - n K_n + t n^2 = \Big(K_n - n \cdot \frac{1 - \sqrt{1-4t}}{2} \Big) \Big( K_n - n \cdot \frac{1 + \sqrt{1-4t}}{2} \Big).$$

The quadratic inequality $K_n^2 - n K_n + t n^2 \geqslant 0$ occurs when both terms are non-negative, or both terms are non-positive. This excludes the range:

$$\frac{1 - \sqrt{1-4t}}{2} < \frac{K_n}{n} < \frac{1 + \sqrt{1-4t}}{2}.$$


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