## more concentration, everywhere

Posted in R, Statistics with tags , , , , , , , , , , on January 25, 2019 by xi'an Although it may sound like an excessive notion of optimality, one can hope at obtaining an estimator δ of a unidimensional parameter θ that is always closer to θ that any other parameter. In distribution if not almost surely, meaning the cdf of (δ-θ) is steeper than for other estimators enjoying the same cdf at zero (for instance ½ to make them all median-unbiased). When I saw this question on X validated, I thought of the Cauchy location example, where there is no uniformly optimal estimator, albeit a large collection of unbiased ones. But a simulation experiment shows that the MLE does better than the competition. At least than three (above) four of them (since I tried the Pitman estimator via Christian Henning’s smoothmest R package). The differences to the MLE empirical cd make it clearer below (with tomato for a score correction, gold for the Pitman estimator, sienna for the 38% trimmed mean, and blue for the median): I wonder at a general theory along these lines. There is a vague similarity with Pitman nearness or closeness but without the paradoxes induced by this criterion. More in the spirit of stochastic dominance, which may be achievable for location invariant and mean unbiased estimators…

## maximal spacing around order statistics

Posted in Books, R, Statistics, University life with tags , , , , , , , on May 17, 2018 by xi'an The riddle from the Riddler for the coming weeks is extremely simple to express in mathematical terms, as it summarises into characterising the distribution of $\Delta_n=\max_i\,\min_j\,|X_{i}-X_{j}|$

when the n-sample is made of iid Normal variates. I however had a hard time finding a result connected with this quantity since most available characterisations are for either Uniform or Exponential variates. I eventually found a 2017 arXival by Nagaraya et al.  covering the issue. Since the Normal distribution belongs to the Gumbel domain of attraction, the extreme spacings, that is the spacings between the most extreme orders statistics [rescaled by nφ(Φ⁻¹{1-n⁻¹})] are asymptotically independent and asymptotically distributed as (Theorem 5, p.15, after correcting a typo): $(\xi_1,\xi_2/2,...)$

where the ξ’s are Exp(1) variates. A crude approximation is thus to consider that the above Δ is distributed as the maximum of two standard and independent exponential distributions, modulo the rescaling by  nφ(Φ⁻¹{1-n⁻¹})… But a more adequate result was pointed out to me by Gérard Biau, namely a 1986 Annals of Probability paper by Paul Deheuvels, my former head at ISUP, Université Pierre and Marie Curie. In this paper, Paul Deheuvels establishes that the largest spacing in a normal sample, M¹, satisfies $\mathbb{P}(\sqrt{2\log\,n}\,M^1\le x) \to \prod_{i=1}^{\infty} (1-e^{-ix})^2$

from which a conservative upper bound on the value of n required for a given bound x⁰ can be derived. The simulation below compares the limiting cdf (in red) with the empirical cdf of the above Δ based on 10⁴ samples of size n=10³. The limiting cdf is the cdf of the maximum of an infinite sequence of independent exponentials with scales 1,½,…. Which connects with the above result, in fine. For a practical application, the 99% quantile of this distribution is 4.71. To achieve a maximum spacing of, say 0.1, with probability 0.99, one would need 2 log(n) > 5.29²/0.1², i.e., log(n)>1402, which is a pretty large number…

## variance of an exponential order statistics

Posted in Books, Kids, pictures, R, Statistics, University life with tags , , , , , , , , , , on November 10, 2016 by xi'an

This afternoon, one of my Monte Carlo students at ENSAE came to me with an exercise from Monte Carlo Statistical Methods that I did not remember having written. And I thus “charged” George Casella with authorship for that exercise! Exercise 3.3 starts with the usual question (a) about the (Binomial) precision of a tail probability estimator, which is easy to answer by iterating simulation batches. Expressed via the empirical cdf, it is concerned with the vertical variability of this empirical cdf. The second part (b) is more unusual in that the first part is again an evaluation of a tail probability, but then it switches to find the .995 quantile by simulation and produce a precise enough [to three digits] estimate. Which amounts to assess the horizontal variability of this empirical cdf.

As we discussed about this question, my first suggestion was to aim at a value of N, number of Monte Carlo simulations, such that the .995 x N-th spacing had a length of less than one thousandth of the .995 x N-th order statistic. In the case of the Exponential distribution suggested in the exercise, generating order statistics is straightforward, since, as suggested by Devroye, see Section V.3.3, the i-th spacing is an Exponential variate with rate (N-i+1). This is so fast that Devroye suggests simulating Uniform order statistics by inverting Exponential order statistics (p.220)!

However, while still discussing the problem with my student, I came to a better expression of the question, which was to figure out the variance of the .995 x N-th order statistic in the Exponential case. Working with the density of this order statistic however led nowhere useful. A bit later, after Google-ing the problem, I came upon this Stack Exchange solution that made use of the spacing result mentioned above, namely that the expectation and variance of the k-th order statistic are $\mathbb{E}[X_{(k)}]=\sum\limits_{i=N-k+1}^N\frac1i,\qquad \mbox{Var}(X_{(k)})=\sum\limits_{i=N-k+1}^N\frac1{i^2}$

which leads to the proper condition on N when imposing the variability constraint.

## coupled filters

Posted in Kids, Statistics, University life with tags , , , , , , , , , on July 11, 2016 by xi'an Pierre Jacob, Fredrik Lindsten, and Thomas Schön recently arXived a paper on coupled particle filters. A coupling problem that proves to be much more complicated than expected, due to the discrete nature of particle filters. The starting point of the paper is the use of common (e.g., uniform) random numbers for the generation of each entry in the particle system at each time t, which maximal correlation gets damaged by the resampling steps (even when using the same uniforms). One suggestion for improving the correlation between entries at each time made in the paper is to resort to optimal transport, using the distance between particles as the criterion. A cheaper alternative is inspired from multi-level Monte Carlo. It builds a joint multinomial distribution by optimising the coupling probability. [Is there any way to iterate this construct instead of considering only the extreme cases of identical values versus independent values?] The authors also recall a “sorted sampling” method proposed by Mike Pitt in 2002, which is to rely on the empirical cdfs derived from the particle systems and on the inverse cdf technique, which is the approach I would have first considered. Possibly with a smooth transform of both ecdf’s in order to optimise the inverse cdf move.  Actually, I have trouble with the notion that the ancestors of a pair of particles should matter. Unless one envisions a correlation of the entire path, but I am ensure how one can make paths correlated (besides coupling). And how this impacts likelihood estimation. As shown in the above excerpt, the coupled approximations produce regular versions and, despite the negative bias, fairly accurate evaluations of likelihood ratios, which is all that matters in an MCMC implementation. The paper also proposes a smoothing algorithm based on Rhee and Glynn (2012) debiasing technique, which operates on expectations against the smoothing distribution (conditional on a value of the parameter θ). Which may connect with the notion of simulating correlated paths. The interesting part is that, due to the coupling, the Rhee and Glynn unbiased estimator has a finite (if random) stopping time.