Archive for econometrics

ERC descriptors

Posted in Statistics, Travel, University life with tags , , , , , , , , , , , , on November 9, 2020 by xi'an

Here are the descriptors (or keywords) validated by the (European Research Council) ERC for submitting grant proposal. The recent addition of PE1_15 in the Mathematics panel should help when submitting more methodological projects:

PE1_14 Mathematical statistics
PE1_15 Generic statistical methodology and modelling
PE1_19 Scientific computing and data processing

even though other panels could prove equally suited for some, as in Computer Science and Informatics,

PE6_7 Artificial intelligence, intelligent systems, natural language processing
PE6_10 Web and information systems, data management systems, information retrieval and digital libraries, data fusion
PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
PE6_12 Scientific computing, simulation and modelling tools
PE6_13 Bioinformatics, bio-inspired computing, and natural computing

in Systems and Communication Engineering,

PE7_7 Signal processing

in Integrative Biology,

LS2_11 Bioinformatics and computational biology
LS2_12 Biostatistics

in Prevention,Diagnosis and Treatment of Human Diseases,

LS7_1 Medical imaging for prevention, diagnosis and monitoring of diseases
LS7_2 Medical technologies and tools (including genetic tools and biomarkers) for prevention, diagnosis, monitoring and treatment of diseases

and in Social Sciences and Humanities,

SH1_6 Econometrics; operations research
SH4_9 Theoretical linguistics; computational linguistics

focused Bayesian prediction

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on June 3, 2020 by xi'an

In this fourth session of our One World ABC Seminar, my friend and coauthor Gael Martin, gave an after-dinner talk on focused Bayesian prediction, more in the spirit of Bissiri et al. than following a traditional ABC approach.  because along with Ruben Loaiza-Maya and [my friend and coauthor] David Frazier, they consider the possibility of a (mild?) misspecification of the model. Using thus scoring rules à la Gneiting and Raftery. Gael had in fact presented an earlier version at our workshop in Oaxaca, in November 2018. As in other solutions of that kind, difficulty in weighting the score into a distribution. Although asymptotic irrelevance, direct impact on the current predictions, at least for the early dates in the time series… Further calibration of the set of interest A. Or the focus of the prediction. As a side note the talk perfectly fits the One World likelihood-free seminar as it does not use the likelihood function!

“The very premise of this paper is that, in reality, any choice of predictive class is such that the truth is not contained therein, at which point there is no reason to presume that the expectation of any particular scoring rule will be maximized at the truth or, indeed, maximized by the same predictive distribution that maximizes a different (expected) score.”

This approach requires the proxy class to be close enough to the true data generating model. Or in the word of the authors to be plausible predictive models. And to produce the true distribution via the score as it is proper. Or the closest to the true model in the misspecified family. I thus wonder at a possible extension with a non-parametric version, the prior being thus on functionals rather than parameters, if I understand properly the meaning of Π(Pθ). (Could the score function be misspecified itself?!) Since the score is replaced with its empirical version, the implementation is  resorting to off-the-shelf MCMC. (I wonder for a few seconds if the approach could be seen as a pseudo-marginal MCMC but the estimation is always based on the same observed sample, hence does not directly fit the pseudo-marginal MCMC framework.)

[Notice: Next talk in the series is tomorrow, 11:30am GMT+1.]

Deirdre McCloskey dans Le Monde

Posted in Statistics with tags , , , , , , , , on January 13, 2020 by xi'an

ENSEA & CISEA 2019

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on June 26, 2019 by xi'an

I found my (short) trip to Abdijan for the CISEA 2019 conference quite fantastic as it allowed me to meet with old friends, from the earliest days at CREST and even before, and to meet new ones. Including local students of ENSEA who had taken a Bayesian course out of my Bayesian Choice book. And who had questions about the nature of priors and the difficulty they had in accepting that several replies were possible with the same data! I wish I had had more time to discuss the relativity of Bayesian statements with them but this was a great and rare opportunity to find avid readers of my books! I also had a long chat with another student worried about the use or mis-use of reversible jump algorithms to draw inference  on time-series models in Bayesian Essentials, chat that actually demonstrated his perfect understanding of the matter. And it was fabulous to meet so many statisticians and econometricians from West Africa, most of them French-speaking. My only regret is not having any free time to visit Abidjan or the neighbourhood as the schedule of the conference did not allow for it [or even for a timely posting of a post!], especially as it regularly ran overtime. (But it did provide for a wide range of new local dishes that I definitely enjoyed tasting!) We are now discussing further opportunities to visit there, e.g. by teaching a short course at the Master or PhD levels.

French Econometrics [discussion]

Posted in Books, pictures, Statistics, University life with tags , , , , , , , on November 30, 2018 by xi'an

This Friday, I am briefly taking part in the 10th French Econometrics Conference as a discussant of Anna Simoni’s (CREST) talk, based on a paper co-written with Sid Chib and Minchul Shin. The conference is located at the Paris School of Economics (PSE), on Paris South End, in an impressive new building. The topic of the paper is a Bayesian empirical likelihood approach to the econometrics notion of moments model. Which I discussed here during ISBA last summer since Sid spoke (twice!) there.