robust-statistics

Fitting robust non-Gaussian models in Stan and R-INLA

Rafael Cabral, David Bolin and Håvard Rue / 2023-01-19

Traditionally the excitation noise of spatial and temporal models is Gaussian. Take, for instance, an AR1 (autoregressive of order 1) process, where the increments \(x_{i+1}-\rho x_i, \ \ |\rho|<1\) are assumed to follow a Gaussian distribution. However, it is easy to find datasets that contain…

youngstats

Merry Christmas and Happy New Year 2023!

YoungStatS Editorial / 2022-12-21

Dear Followers of the YoungStatS project, Dear All! It has been an exciting year for our project, including 7 One World YoungStatS webinars and blogposts from leading authors in various areas of statistics, probability and econometrics. In particular, we wish to thank our supporters: The Federation…

copula-models

Weighted residual empirical processes in semi-parametric copula adjusted for regression

Yue Zhao, Irène Gijbels and Ingrid Van Keilegom / 2022-12-07

In this post we first review the concept of semi-parametric copula and the accompanying estimation procedure of pseudo-likelihood estimation (PLE). We then generalize the estimation problem to the setting where the copula signal is hidden in a semi- or non-parametric regression model. Under this…

survival-analysis

Some Recent Developments in Mixture Cure Model Methodology for Survival Analysis

Ross Maller, Sidney Resnick, Soudabeh Shemehsavar and Muzhi Zhao / 2022-10-30

The mixture cure model in survival analysis has received large and growing attention in the last few decades. Here we present an overview drawing together early results and some recent new developments, and pointing out areas where further work is needed. […] In certain clinical trials or…

graphical-models

Graphical modeling of stochastic processes driven by correlated noise

Søren Wengel Mogensen and Niels Richard Hansen / 2022-10-27

Complex systems are difficult to understand. We need good tools to study the interactions that define such systems. In this blog post, we describe how the framework in Mogensen and Hansen (2022) provides such a tool. This summary of the paper is meant to be accessible for readers with some…

machine-learning

Inference on Adaptively Collected Data

Ruohan Zhan / 2022-10-11

It is increasingly common for data to be collected adaptively, where experimental costs are reduced progressively by assigning promising treatments more frequently. However, adaptivity also poses great challenges on post-experiment inference, since observations are dependent, and standard estimates…