F. Javier Rubio

GitHub - FJRubio67/PTCMGH: Promotion Time Cure Models with a General Hazard structure Promotion Time Cure Models with a General Hazard structure - FJRubio67/PTCMGH

New R package PTCMGH: The PTCMGH R package implements promotion time cure models with a general hazard structure. The package, along with a tutorial for simulating and fitting these models, can be found at:

github.com/FJRubio67/PT...

rpubs.com/FJRubio/PTCMGH

#rstats #survival

The new Bayesian Social Sciences section of @isba-bayesian.bsky.social has just been created: bss-isba.github.io. The committee is myself as chair, @robinryder.bsky.social, chair elect from 2027, @nialfriel.bsky.social, program chair, @monjalexander.bsky.social, Treasurer, EJWagenmakers, Secretary.

YouTube video by The Julia Programming Language A Deep Dive Into DifferentialEquations.jl | JuliaCon Global 2025 | Rackauckas, Smith

DifferentialEquations.jl is many things, and lots of people only use a small portion of it. Check out the JuliaCon 2025 workshop: introduces many aspects of the packages that the developers feel are underutilized and under-understood!

#julialang #sciml

www.youtube.com/watch?v=lSGF...

4-panel-comic by War and Peas Panel 1: Jim, a man in a yellow jacket, excitedly approaches a woman in a pink dress. He says, "Honey, I finally finished the prediction machine!" while pointing at a prediction machine on a small table. The machine displays an unclear message. Panel 2: The woman, standing next to the prediction machine, says, "I'm leaving you, Jim." Panel 3: The machine's screen reads, "Everyone you love will leave you." Jim, looking at the machine, appears shocked. Panel 4: Jim, with a confident pose, says, "What a success!" The prediction machine now displays a garbled message, "Everyone love will have you," and a "SLAM" sound effect indicates the woman has left, shutting the door behind her.

Handbook of Markov Chain Monte Carlo This thoroughly revised and expanded second edition of the Handbook of Markov Chain Monte Carlo reflects the dramatic evolution of MCMC methods since the publication of the first edition. With the add...

Handbook of Markov Chain Monte Carlo, 2nd Edition. Radu V. Craiu, Dootika Vats, Galin Jones, Steve Brooks, Andrew Gelman, Xiao-Li Meng (eds.) Chapman & Hall 2026, 680 Pages. www.routledge.com/Handbook-of-...

Model Uncertainty and Missing Data: An Objective Bayesian Perspective The interplay between missing data and model uncertainty—two classic statistical problems—leads to primary questions that we formally address from an objective Bayesian perspective. For the general regression problem, we discuss the probabilistic justification of Rubin’s rules applied to the usual components of Bayesian variable selection, arguing that prior predictive marginals should be central to the pursued methodology. In the regression settings, we explore the conditions of prior distributions that make the missing data mechanism ignorable, provided that it is missing at random or completely at random. Moreover, when comparing multiple linear models, we provide a complete methodology for dealing with special cases, such as variable selection or uncertainty regarding model errors. In numerous simulation experiments, we demonstrate that our method outperforms or equals others, in consistently producing results close to those obtained using the full dataset. In general, the difference increases with the percentage of missing data and the correlation between the variables used for imputation. Finally, we summarize possible directions for future research.

The subsequent webinar will be on:

📅 November 5, 2025 (4:00 PM UTC | 11:00 AM EST | 5:00 PM CET)
“Model Uncertainty and Missing Data: An Objective Bayesian Perspective”
by G. García-Donato, M. Eugenia Castellanos, S. Cabras, A. Quirós, and A. Forte
doi.org/10.1214/25-B...

The 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐨𝐜𝐢𝐞𝐭𝐲 𝐟𝐨𝐫 𝐁𝐚𝐲𝐞𝐬𝐢𝐚𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐈𝐒𝐁𝐀) was founded in 1992 to promote the development and application of Bayesian analysis.

𝘐𝘚𝘉𝘈 𝘱𝘳𝘰𝘷𝘪𝘥𝘦𝘴 𝘢𝘯 𝘪𝘯𝘵𝘦𝘳𝘯𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘵𝘺 𝘧𝘰𝘳 𝘵𝘩𝘰𝘴𝘦 𝘪𝘯𝘵𝘦𝘳𝘦𝘴𝘵𝘦𝘥 𝘪𝘯 𝘉𝘢𝘺𝘦𝘴𝘪𝘢𝘯 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘢𝘯𝘥 𝘪𝘵𝘴 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴.

Find us across the web:
linktr.ee/ISBAbayesian

Most cancer patients face comorbidities that complicate survival. We use Bayesian machine learning (BART) in a relative survival framework to estimate excess hazard, uncover vulnerable subgroups, and identify drivers of inequalities in colon cancer survival.

New paper, with P. Basak, A.R. Linero, and C. Maringe, accepted in JASA A&CS

"Understanding Inequalities in Cancer Survival Using Bayesian Machine Learning"

doi.org/10.1080/0162...

#inequalities #cancer #survival #Bayesian #MachineLearning @icon-lshtm.bsky.social @statisticsucl.bsky.social

The effect of the shape (skewness) parameter in skew-symmetric models: Part II

rpubs.com/FJRubio/Disc...

Based on a recently proposed discrepancy measures, it shows that in certain models, such as the skew-normal, the influence of the shape parameter is negligible across a broad interval around 0.

Reminder that all three books I've co-authored are freely available online for non-commercial use (and the fourth will be, too)

Approaches for modelling survival time in groups with very low risk I'm currently working on a study with a group of hematologists. Patients with PV (Polycythemia vera) have very low risk of future thromboembolism after diagnosis due to disease management. Patients

I've got 1 event in one group in which we know the risk of the outcome is very low.

This is creating enormous confidence intervals. Can I use firth penalization? Should I get more data? All ideas welcome

stats.stackexchange.com/questions/66...

4-panel comic. (1) [Person 1 with ponytail flanked by person with short hair and another person speaking into microphone at podium] PERSON 1: In the early 2010s, researchers found that many major scientific results couldn’t be reproduced. (2) PERSON 1: Over a decade into the replication crisis, we wanted to see if today’s studies have become more robust. (3) PERSON 1: Unfortunately, our replication analysis has found exactly the same problems that those 2010s researchers did. (4) [newspaper with image of speakers from previous panels] Headline: Replication Crisis Solved

Replication Crisis

xkcd.com/3117/

Conformalized Regression for Continuous Bounded Outcomes Regression problems with bounded continuous outcomes frequently arise in real-world statistical and machine learning applications, such as the analysis of rates and proportions. A central challenge in...

New preprint with Fabrizio Leisen and our PhD student Zhanli Wu:

"Conformalized Regression for Bounded Outcomes"

arxiv.org/abs/2507.14023

#rstats #conformal #prediction

R code and data are also available at:

github.com/ZWU-001/CPBo...

Research Studentships

🚨3 Departmental PhD Studentships have now become available. The deadline for applications will be 31 of July. Available for overseas and home students.

www.ucl.ac.uk/statistics/p...

The latest issue of the ISBA bulletin, containing a call for contributed discussions for two papers:

5-panel comic. (1) [teacher with long hair next to whiteboard] TEACHER: I’m supposed to give you the tools to do good science. (2) [teacher addressing students] But what *are* those tools? Methodology is hard and there are so many ways to get incorrect results. What is the magic ingredient that makes for good science? (3) TEACHER: To figure it out, I ran a regression with all the factors people say are important: [embedded list in sub-panel, cut off at end] Outcome variable: correct scientific results. Predictors: collaboration; skepticism of others’ claims; questioning your own beliefs; trying to falsify hypotheses; checking citations; statistical rigor; blinded analysis; financial disclosure; open data (4) TEACHER: The regression says two ingredients are the most crucial: 1) genuine curiosity about the answer to a question, and 2) ammonium hydroxide. (5) STUDENT: Wait, why did *ammonia* score so high? How did it even get on the list? LONG HAIR: ...And now you’re doing good science!

Good Science

xkcd.com/3101/

Call for discussion papers 2025: Innovative usages of natural experiments and causal inference in statistics and data science👇

rss.org.uk/news-publica...

a novel discrepancy measure My friend EJ Wagenmakers, along with his colleague  Raoul Grasman, have proposed a novel measure of discrepancy between two distributions that they formulate through a basic Bayesian lens as an exp…

A novel discrepancy measure:

xianblog.wordpress.com/2025/06/01/a...

GitHub - FJRubio67/HazReg: Parametric hazard-based regression models (R package) Parametric hazard-based regression models (R package) - FJRubio67/HazReg

Not a proper ML guy, but I have used this trick a few times:

1. In the log-likelihood of relative survival models.

github.com/FJRubio67/Ha...

2. Some “intractable likelihood” models:

rpubs.com/FJRubio/AMLE...

A nice overview of GAMs:

"Generalized Additive Models" by Simon N. Wood

doi.org/10.1146/annu...

Interesting to see cancer epidemiology papers in the top 10:

3 year PDRA position in the OCEAN project (https://oceanerc.com/) to work at Warwick with Gareth Roberts, Adam Johansen and other OCEAN Researchers on a range of topics around scalable distributed computation. Deadline 10th April 2025 at 11.55pm UK time. Details at
warwick-careers.tal....

Or, to clarify notation:

Archive Request xkcd.com/3052

🎉 Happy 199th birthday, UCL! 🎉

We’re just one year away from celebrating 200 years of innovation, discovery, and impact.

UCL has always been a place of firsts. Now, we look ahead to an exciting bicentenary year!

Learn more: www.ucl.ac.uk/news/2025/fe...

#UCL200 #UCLFoundationDay #UCLBirthday

Hi #stats and #datascience community, we are compiling a list of resources for teaching multivariable thinking. Please feel free to share any resources that you might have developed and/or use in your own teaching. I'd appreciate it if you can help me spread the word.

I'm really looking forward to the CRiSM 2.0 Conference warwick.ac.uk/fac/sci/stat... from 21st-23rd May. We've been lucky enough to get a really nice list of speakers; registrations open now if anyone is in the market for some interesting talks in May.

Principal Investigator positions at ELLIS Institute Finland | ELLIS Institute Finland Now recruiting new PIs in artificial intelligence and machine learning

I have big news: @ellis.eu has launched its 2nd major research center, @ellisfinland.bsky.social! I have agreed to start as founding director & the first call for PI positions is open. This is a major opportunity for outstanding researchers, join us! ellisinstitute.fi/PI-recruit

Problems caused by grade inflation
statmodeling.stat.columbia.edu/2025/01/19/p...