Semiparametric inference for the recurrent event process by means of a single-index model

In this paper, we introduce new parametric and semiparametric regression techniques for a recurrent event process subject to random right censoring. We develop models for the cumula- tive mean function and provide asymptotically normal estimators. Our semiparametric model which relies on a single-index assumption can be seen as a dimension reduction technique that, contrary to a fully nonparametric approach, is not stroke by the curse of dimensional- ity when the number of covariates is high. We discuss data-driven techniques to choose the parameters involved in the estimation procedures and provide a simulation study to support our theoretical results

A penalized algorithm for event-specific rate models for recurrent events

We introduce a covariate-specific total variation penalty in two semiparametric models for the rate function of recurrent event process. The two models are a stratified Cox model, introduced in Prentice et al. (1981), and a stratified Aalen's additive model. We show the consistency and asymptotic normality of our penalized estimators. We demonstrate, through a simulation study, that our estimators outperform classical estimators for small to moderate sample sizes. Finally an application to the bladder tumour data of Byar (1980) is presented

Lattice strain measurements using synchrotron diffraction to calibrate a micromechanical modeling in a ferrite–cementite steel

In situ tensile tests were performed at room temperature on a ferrite–cementite steel specifically designed for this study. The evolution of the average stress in ferrite during loading was analyzed by X-ray diffraction.Lattice strain measurements were performed with synchrotron ring diffraction in both ferrite and cementite.These in situ tests were complemented by macroscopic tensile and reversible tensile-compression tests to study the Bauschinger effect. In order to reproduce stresses in ferrite and cementite particles,a recently developed micromechanical Internal Length Mean Field (ILMF) model based on a generalized self-consistent scheme is applied. In this designed ferrite–cementite steel,the third ‘‘phase’’of the model represents finite intermediate‘‘layers’’in ferrite due to large geometrically necessary dislocation (GND) densities around cementite particles. The assumed constant thickness of the layers is calibrated thanks to the obtained experimental data.The ILMF model is validated by realistic estimates of the Bauschinger stress and the large difference between mean stresses in ferrite and in cementite phases.This difference cannot be reproduced by classic two-phase homogenization schemes without intermediate GND layers

Toward a new interpretation of the mechanical behaviour of As-quenched low alloyed martensitic steels

International audienceThough as-quenched martensite exhibits a low uniform elongation in tension, it is highlighted that this phase has a very high strain-hardening which increases with carbon content and a large Bauschinger effect. Because usual dislocation storage can not explain reasonably this particular behaviour, an approach based on a continuum composite view of martensite (CCA) is developed suitable to capture all the experimental features

Spline Regression with Automatic Knot Selection

In this paper we introduce a new method for automatically selecting knots in spline regression. The approach consists in setting a large number of initial knots and fitting the spline regression through a penalized likelihood procedure called adaptive ridge. The proposed method is similar to penalized spline regression methods (e.g. P-splines), with the noticeable difference that the output is a sparse spline regression with a small number of knots. We show that our method called A-spline, for adaptive splines yields sparse regression models with high interpretability, while having similar predictive performance similar to penalized spline regression methods. A-spline is applied both to simulated and real dataset. A fast and publicly available implementation in R is provided along with this paper

A Comprehensive Framework for Evaluating Time to Event Predictions using the Restricted Mean Survival Time

The restricted mean survival time (RMST) is a widely used quantity in survival analysis due to its straightforward interpretation. For instance, predicting the time to event based on patient attributes is of great interest when analyzing medical data. In this paper, we propose a novel framework for evaluating RMST estimations. Our criterion estimates the mean squared error of an RMST estimator using Inverse Probability Censoring Weighting (IPCW). A model-agnostic conformal algorithm adapted to right-censored data is also introduced to compute prediction intervals and to evaluate variable importance. Our framework is valid for any RMST estimator that is asymptotically convergent and works under model misspecification