# Simulating Convergence in Probability to a constant

Asymptotic results cannot be proven by computer simulation, because they are statements involving the concept of infinity. But we should be able to obtain a sense that things do indeed march the way theory tells us.

Consider the theoretical result $$\lim_{n\rightarrow\infty}P(|X_n|>\epsilon) = 0, \qquad \epsilon >0$$

where $X_n$ is a function of $n$ random variables, say identically and independently distributed. This says that $X_n$ converges in probability to zero. The archetypal example here I guess is the case where $X_n$ is the sample mean minus the common expected value of the i.i.d. r.v.'s of the sample,

$$X_n = \frac 1n\sum_{i=1}^nY_i - E[Y_1]$$

QUESTION: How could we convincingly show to somebody that the above relation "materializes in the real world", by using computer simulation results from necessarily finite samples?

Please do note that I specifically chose convergence to a constant.

I provide below my approach as an answer, and I hope for better ones.

UPDATE: Something in the back of my head bothered me -and I found out what. I dug up an older question where a most interesting discussion went on in the comments to one of the answers. In there, @Cardinal provided an example of an estimator that it is consistent but its variance remains non-zero and finite asymptotically. So a tougher variant of my question becomes: how do we show by simulation that a statistic converges in probability to a constant, when this statistic maintains non-zero and finite variance asymptotically?

• @Glen_b Coming from you, this is the equivalent of a badge. Thanks. Jun 29, 2014 at 21:59
• Been thinking about this every now and then and all I've come up with is that 'concentration around the mean'-argument; I hope some of the clever people here have time to write something interesting! (+1 of course!)
– KOE
Jun 29, 2014 at 23:47

I think of $P()$ as a distribution function (a complementary one in the specific case). Since I want to use computer simulation to exhibit that things tend the way the theoretical result tells us, I need to construct the empirical distribution function of $|X_n|$, or the empirical relative frequency distribution, and then somehow show that as $n$ increases, the values of $|X_n|$ concentrate "more and more" to zero.

To obtain an empirical relative frequency function, I need (much) more than one sample increasing in size, because as the sample size increases, the distribution of $|X_n|$ changes for each different $n$.

So I need to generate from the distribution of the $Y_i$'s, $m$ samples "in parallel", say $m$ ranging in the thousands, each of some initial size $n$, say $n$ ranging in the tens of thousands. I need then to calculate the value of $|X_n|$ from each sample (and for the same $n$), i.e. obtain the set of values $\{|x_{1n}|, |x_{2n}|,...,|x_{mn}|\}$.

These values can be used to construct an empirical relative frequency distribution. Having faith in the theoretical result, I expect that "a lot" of the values of $|X_n|$ will be "very close" to zero -but of course, not all.

So in order to show that the values of $|X_n|$ do indeed march towards zero in greater and greater numbers, I would have to repeat the process, increasing the sample size to say $2n$, and show that now the concentration to zero "has increased". Obviously to show that it has increased, one should specify an empirical value for $\epsilon$.

Would that be enough? Could we somehow formalize this "increase in concentration"? Could this procedure, if performed in more "sample-size increase" steps, and the one being closer to the other, provide us with some estimate about the actual rate of convergence, i.e. something like "empirical probability mass that moves below the threshold per each $n$-step" of, say, one thousand?

Or, examine the value of the threshold for which, say $90$% of the probability lies below, and see how this value of $\epsilon$ gets reduced in magnitude?

AN EXAMPLE

Consider the $Y_i$'s to be $U(0,1)$ and so

$$|X_n| = \left|\frac 1n\sum_{i=1}^nY_i - \frac 12\right|$$

We first generate $m=1,000$ samples of $n=10,000$ size each. The empirical relative frequency distribution of $|X_{10,000}|$ looks like

and we note that $90.10$% of the values of $|X_{10,000}|$ are smaller then $0.0046155$.

Next I increase the sample size to $n=20,000$. Now the empirical relative frequency distribution of $|X_{20,000}|$ looks like and we note that $91.80$% of the values of $|X_{20,000}|$ are below $0.0037101$. Alternatively, now $98.00$% of values fall below $0.0045217$.

Would you be persuaded by such a demonstration?

• No, I would not be persuaded by any such demonstration, if that were all that is offered. It is unable to distinguish between the claimed result and a result in which there is a very small amount of contamination from a nonzero distribution. Any computer simulation, to be truly persuasive, must be accompanied by reasoning that would rule out such phenomena. (I recently conducted a series of simulations that went out to a sample size of $10^{1000}$--that's not a typo--but was still not persuaded by the results, although they were very suggestive!)
– whuber
Jun 29, 2014 at 20:12
• @whuber What you write sounds very interesting. Were these simulations you mention based on some initial real data, from which distributions where estimated and then additional artificial data were generated? Or it was artificial from the very beginning? If confidentiality is not an issue, and time permits, I personally would very much want to see an answer of yours providing some glimpse on how these simulations evolved and why the doubt remained. Jun 29, 2014 at 20:57
• It was artificial data. I performed these simulations to support a comment at stats.stackexchange.com/questions/104875/…. You will see immediately how such a large simulation can be performed: to generate a sample of $N$ from a Bernoulli$(1/2)$ distribution you just draw a single value from a Binomial$(N,1/2)$ distribution. When $N$ is sufficiently large, you might as well draw a value from a Normal$(N/2, \sqrt{N}/2)$ distribution. The main trick is doing this with $1000$ digit precision :-).
– whuber
Jun 30, 2014 at 1:12
• @Whuber Thanks, I will work on it. By the way, the question you mention, the answer therein and your comments, have set me to investigate more deeply both the asymptotic distribution of the sample variance from non-normal samples, as well as the applicability of Slutsky's theorem in the way that is used in the answer. I hope I will eventually have some results to share. Jun 30, 2014 at 1:21