Archive for the R Category

reliable ABC model choice via random forests

Posted in pictures, R, Statistics, University life with tags , , , , , , , on October 29, 2014 by xi'an

human_ldaAfter a somewhat prolonged labour (!), we have at last completed our paper on ABC model choice with random forests and submitted it to PNAS for possible publication. While the paper is entirely methodological, the primary domain of application of ABC model choice methods remains population genetics and the diffusion of this new methodology to the users is thus more likely via a media like PNAS than via a machine learning or statistics journal.

When compared with our recent update of the arXived paper, there is not much different in contents, as it is mostly an issue of fitting the PNAS publication canons. (Which makes the paper less readable in the posted version [in my opinion!] as it needs to fit the main document within the compulsory six pages, relegated part of the experiments and of the explanations to the Supplementary Information section.)

Feller’s shoes and Rasmus’ socks [well, Karl's actually...]

Posted in Books, Kids, R, Statistics, University life with tags , , , , on October 24, 2014 by xi'an

Yesterday, Rasmus Bååth [of puppies' fame!] posted a very nice blog using ABC to derive the posterior distribution of the total number of socks in the laundry when only pulling out orphan socks and no pair at all in the first eleven draws. Maybe not the most pressing issue for Bayesian inference in the era of Big data but still a challenge of sorts!

Rasmus set a prior on the total number m of socks, a negative Binomial Neg(15,1/3) distribution, and another prior of the proportion of socks that come by pairs, a Beta B(15,2) distribution, then simulated pseudo-data by picking eleven socks at random, and at last applied ABC (in Rubin’s 1984 sense) by waiting for the observed event, i.e. only orphans and no pair [of socks]. Brilliant!

The overall simplicity of the problem set me wondering about an alternative solution using the likelihood. Cannot be that hard, can it?! After a few computations rejected by opposing them to experimental frequencies, I put the problem on hold until I was back home and with access to my Feller volume 1, one of the few [math] books I keep at home… As I was convinced one of the exercises in Chapter II would cover this case. After checking, I found a partial solution, namely Exercice 26:

A closet contains n pairs of shoes. If 2r shoes are chosen at random (with 2r<n), what is the probability that there will be (a) no complete pair, (b) exactly one complete pair, (c) exactly two complete pairs among them?

This is not exactly a solution, but rather a problem, however it leads to the value

p_j=\binom{n}{j}2^{2r-2j}\binom{n-j}{2r-2j}\Big/\binom{2n}{2r}

as the probability of obtaining j pairs among those 2r shoes. Which also works for an odd number t of shoes:

p_j=2^{t-2j}\binom{n}{j}\binom{n-j}{t-2j}\Big/\binom{2n}{t}

as I checked against my large simulations. socksSo I solved Exercise 26 in Feller volume 1 (!), but not Rasmus’ problem, since there are those orphan socks on top of the pairs. If one draws 11 socks out of m socks made of f orphans and g pairs, with f+2g=m, the number k of socks from the orphan group is an hypergeometric H(11,m,f) rv and the probability to observe 11 orphan socks total (either from the orphan or from the paired groups) is thus the marginal over all possible values of k:

\sum_{k=0}^{11} \dfrac{\binom{f}{k}\binom{2g}{11-k}}{\binom{m}{11}}\times\dfrac{2^{11-k}\binom{g}{11-k}}{\binom{2g}{11-k}}

so it could be argued that we are facing a closed-form likelihood problem. Even though it presumably took me longer to achieve this formula than for Rasmus to run his exact ABC code!

a bootstrap likelihood approach to Bayesian computation

Posted in Books, R, Statistics, University life with tags , , , , , , , , on October 16, 2014 by xi'an

This paper by Weixuan Zhu, Juan Miguel Marín [from Carlos III in Madrid, not to be confused with Jean-Michel Marin, from Montpellier!], and Fabrizio Leisen proposes an alternative to our 2013 PNAS paper with Kerrie Mengersen and Pierre Pudlo on empirical likelihood ABC, or BCel. The alternative is based on Davison, Hinkley and Worton’s (1992) bootstrap likelihood, which relies on a double-bootstrap to produce a non-parametric estimate of the distribution of a given estimator of the parameter θ. Including a smooth curve-fitting algorithm step, for which not much description is available from the paper.

“…in contrast with the empirical likelihood method, the bootstrap likelihood doesn’t require any set of subjective constrains taking advantage from the bootstrap methodology. This makes the algorithm an automatic and reliable procedure where only a few parameters need to be specified.”

The spirit is indeed quite similar to ours in that a non-parametric substitute plays the role of the actual likelihood, with no correction for the substitution. Both approaches are convergent, with similar or identical convergence speeds. While the empirical likelihood relies on a choice of parameter identifying constraints, the bootstrap version starts directly from the [subjectively] chosen estimator of θ. For it indeed needs to be chosen. And computed.

“Another benefit of using the bootstrap likelihood (…) is that the construction of bootstrap likelihood could be done once and not at every iteration as the empirical likelihood. This leads to significant improvement in the computing time when different priors are compared.”

This is an improvement that could apply to the empirical likelihood approach, as well, once a large enough collection of likelihood values has been gathered. But only in small enough dimensions where smooth curve-fitting algorithms can operate. The same criticism applying to the derivation of a non-parametric density estimate for the distribution of the estimator of θ. Critically, the paper only processes examples with a few parameters.

In the comparisons between BCel and BCbl that are produced in the paper, the gain is indeed towards BCbl. Since this paper is mostly based on examples and illustrations, not unlike ours, I would like to see more details on the calibration of the non-parametric methods and of regular ABC, as well as on the computing time. And the variability of both methods on more than a single Monte Carlo experiment.

I am however uncertain as to how the authors process the population genetic example. They refer to the composite likelihood used in our paper to set the moment equations. Since this is not the true likelihood, how do the authors select their parameter estimates in the double-bootstrap experiment? The inclusion of Crakel’s and Flegal’s (2013) bivariate Beta, is somewhat superfluous as this example sounds to me like an artificial setting.

In the case of the Ising model, maybe the pre-processing step in our paper with Matt Moores could be compared with the other algorithms. In terms of BCbl, how does the bootstrap operate on an Ising model, i.e. (a) how does one subsample pixels and (b)what are the validity guarantees?

A test that would be of interest is to start from a standard ABC solution and use this solution as the reference estimator of θ, then proceeding to apply BCbl for that estimator. Given that the reference table would have to be produced only once, this would not necessarily increase the computational cost by a large amount…

randomness in coin tosses and last digits of prime numbers

Posted in Books, Kids, R, Statistics, University life with tags , , , on October 7, 2014 by xi'an

A rather intriguing note that was arXived last week: it is essentially one page long and it compares the power law of the frequency range for the Bernoulli experiment with the power law of the frequency range for the distribution of the last digits of the first 10,000 prime numbers to conclude that the power is about the same. With a very long introduction about the nature of randomness that is unrelated with the experiment. And a call to a virtual coin toss website, instead of using R uniform generator… Actually the exact distribution is available, at least asymptotically, for the Bernoulli (coin tossing) case. Among other curiosities, a constant typo in the sign of the coefficient β for the power law. A limitation of the Bernoulli experiment to 10⁴ simulations, rather than the 10⁵ used for the prime numbers. And a conclusion that the distribution of the end digits is truly uniform which relates only to this single experiment!

The winds of Winter [Bayesian prediction]

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , , , , , on October 7, 2014 by xi'an

A surprising entry on arXiv this morning: Richard Vale (from Christchurch, NZ) has posted a paper about the characters appearing in the yet hypothetical next volume of George R.R. Martin’s Song of ice and fire series, The winds of Winter [not even put for pre-sale on amazon!]. Using the previous five books in the series and the frequency of occurrence of characters’ point of view [each chapter being told as from the point of view of one single character], Vale proceeds to model the number of occurrences in a given book by a truncated Poisson model,

x_{it} \sim \mathcal{P}(\lambda_i)\text{ if }|t-\beta_i|<\tau_i

in order to account for [most] characters dying at some point in the series. All parameters are endowed with prior distributions, including the terrible “large” hyperpriors familiar to BUGS users… Despite the code being written in R by the author. The modelling does not use anything but the frequencies of the previous books, so knowledge that characters like Eddard Stark had died is not exploited. (Nonetheless, the prediction gives zero chapter to this character in the coming volumes.) Interestingly, a character who seemingly died at the end of the last book is still given a 60% probability of having at least one chapter in  The winds of Winter [no spoiler here, but many in the paper itself!]. As pointed out by the author, the model as such does not allow for prediction of new-character chapters, which remains likely given Martin’s storytelling style! Vale still predicts 11 new-character chapters, which seems high if considering the series should be over in two more books [and an unpredictable number of years!].

As an aside, this paper makes use of the truncnorm R package, which I did not know and which is based on John Geweke’s accept-reject algorithm for truncated normals that I (independently) proposed a few years later.

Monte Carlo simulation and resampling methods for social science [book review]

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , on October 6, 2014 by xi'an

Monte Carlo simulation and resampling methods for social science is a short paperback written by Thomas Carsey and Jeffrey Harden on the use of Monte Carlo simulation to evaluate the adequacy of a model and the impact of assumptions behind this model. I picked it in the library the other day and browse through the chapters during one of my métro rides. Definitely not an in-depth reading, so be warned!

Overall, I think the book is doing a good job of advocating the use of simulation to evaluate the pros and cons of a given model (rephrased as data generating process) when faced with data. And doing it in R. After some rudiments in probability theory and in R programming, it briefly explains the use of resident random generators if not of how to handle new distributions and then spend a large part of the book on simulation around generalised and regular linear models. For instance, in the linear model, the authors test the impact of heterocedasticity, multicollinearity, measurement error, omitted variable(s), serial correlation, clustered data, and heavy-tailed errors. While this is a perfect way of exploring those semi-hidden hypotheses behind the linear model, I wonder at the impact on students of this exploration. On the one hand, they will perceive the importance of those assumptions and hopefully remember them. On the other hand, and this is a very recurrent criticism of mine, this implies a lot of maturity from the students, i.e., they have to distinguish the data, the model [maybe] behind the data, the finite if large number of hypotheses one can test, and the interpretation of the outcome of a simulation test… Given that they were introduced to basic probability just a few chapters before, this expectation [from the students] may prove unrealistic. (And a similar criticism applies to the following chapters, from GLM to jackknife and bootstrap.)

At the end of the book, the authors ask the question as to how could a reader use the information in this book towards one’s work. Drafting a generic protocol for this reader, who is supposed to consider “alterations to the data generating process” (p.272) and to “identify a possible problem or assumption violation” (p.271). Thus requiring a readership “who has some training in quantitative methods” (p.1). And then some more. But I definitely sympathise with the goal of confronting models and theory with the harsh reality of simulation output!

future of computational statistics

Posted in Books, pictures, R, Statistics, University life with tags , , , , , , , , , , , , , , on September 29, 2014 by xi'an

I am currently preparing a survey paper on the present state of computational statistics, reflecting on the massive evolution of the field since my early Monte Carlo simulations on an Apple //e, which would take a few days to return a curve of approximate expected squared error losses… It seems to me that MCMC is attracting more attention nowadays than in the past decade, both because of methodological advances linked with better theoretical tools, as for instance in the handling of stochastic processes, and because of new forays in accelerated computing via parallel and cloud computing, The breadth and quality of talks at MCMski IV is testimony to this. A second trend that is not unrelated to the first one is the development of new and the rehabilitation of older techniques to handle complex models by approximations, witness ABC, Expectation-Propagation, variational Bayes, &tc. With a corollary being an healthy questioning of the models themselves. As illustrated for instance in Chris Holmes’ talk last week. While those simplifications are inevitable when faced with hardly imaginable levels of complexity, I still remain confident about the “inevitability” of turning statistics into an “optimize+penalize” tunnel vision…  A third characteristic is the emergence of new languages and meta-languages intended to handle complexity both of problems and of solutions towards a wider audience of users. STAN obviously comes to mind. And JAGS. But it may be that another scale of language is now required…

If you have any suggestion of novel directions in computational statistics or instead of dead ends, I would be most interested in hearing them! So please do comment or send emails to my gmail address bayesianstatistics

Follow

Get every new post delivered to your Inbox.

Join 677 other followers