Blackwell-Rosenbluth Awards 2021

Congratulations to the winners of the newly created award! This j-ISBA award is intended for junior researchers in different areas of Bayesian statistics. And named after David Blackwell and Arianna  Rosenbluth. They will present their work at the newly created JB³ seminars on 10 and 12 November, both at 1pm UTC. (The awards are broken into two time zones, corresponding to the Americas and the rest of the World.)

UTC+0 to UTC+13

Marta Catalano, Warwick University
Samuel Livingstone, University College London
Dootika Vats, Indian Institute of Technology Kanpur

UTC-12 to UTC-1

Trevor Campbell, University of British Columbia
Daniel Kowal, Rice University
Yixin Wang, University of Michigan

RB4MCMC@ISR

Our survey paper on Rao-Blackwellisation (and the first Robert&Roberts published paper!) just appeared on-line as part of the International Statistical Review mini-issue in honour of C.R. Rao on the occasion of his 100th birthday. (With an unfortunate omission of my affiliation with Warwick!). While the papers are unfortunately beyond a paywall, except for a few weeks!, the arXiv version is still available (and presumably with less typos!).

Rao-Blackwellisation in the MCMC era

A few months ago, as indicated on this blog, I was contacted by ISR editors to write a piece on Rao-Blackwellisation, towards a special issue celebrating Calyampudi Radhakrishna Rao’s 100th birthday. Gareth Roberts and I came up with this survey, now on arXiv, discussing different aspects of Monte Carlo and Markov Chain Monte Carlo that pertained to Rao-Blackwellisation, one way or another. As I discussed the topic with several friends over the Fall, it appeared that the difficulty was more in setting the boundaries. Than in finding connections. In a way anything conditioning or demarginalising or resorting to auxiliary variates is a form of Rao-Blackwellisation. When re-reading the JASA Gelfand and Smith 1990 paper where I first saw the link between the Rao-Blackwell theorem and simulation, I realised my memory of it had drifted from the original, since the authors proposed there an approximation of the marginal based on replicas rather than the original Markov chain. Being much closer to Tanner and Wong (1987) than I thought. It is only later that the true notion took shape. [Since the current version is still a draft, any comment or suggestion would be most welcomed!]

Rao-Blackwellisation, a review in the making

Recently, I have been contacted by a mainstream statistics journal to write a review of Rao-Blackwellisation techniques in computational statistics, in connection with an issue celebrating C.R. Rao’s 100th birthday. As many many techniques can be interpreted as weak forms of Rao-Blackwellisation, as e.g. all auxiliary variable approaches, I am clearly facing an abundance of riches and would thus welcome suggestions from Og’s readers on the major advances in Monte Carlo methods that can be connected with the Rao-Blackwell-Kolmogorov theorem. (On the personal and anecdotal side, I only met C.R. Rao once, in 1988, when he came for a seminar at Purdue University where I was spending the year.)

best unbiased estimator of θ² for a Poisson model

A mostly traditional question on X validated about the “best” [minimum variance] unbiased estimator of θ² from a Poisson P(θ) sample leads to the Rao-Blackwell solution

and a similar estimator could be constructed for θ³, θ⁴, … With the interesting limitation that this procedure stops at the power equal to the number of observations (minus one?). But,  since the expectation of a power of the sufficient statistics S [with distribution P(nθ)] is a polynomial in θ, there is de facto no limitation. More interestingly, there is no unbiased estimator of negative powers of θ in this context, while this neat comparison on Wikipedia (borrowed from the great book of counter-examples by Romano and Siegel, 1986, selling for a mere $180 on amazon!) shows why looking for an unbiased estimator of exp(-2θ) is particularly foolish: the only solution is (-1) to the power S [for a single observation]. (There is however a first way to circumvent the difficulty if having access to an arbitrary number of generations from the Poisson, since the Forsythe – von Neuman algorithm allows for an unbiased estimation of exp(-F(x)). And, as a second way, as remarked by Juho Kokkala below, a sample of at least two Poisson observations leads to a more coherent best unbiased estimator.)