Search Results for author:
Found 9 papers, 3 papers with code
An Overview of Automated Vehicle Platooning Strategies
Automated vehicle (AV) platooning has the potential to improve the safety, operational, and energy efficiency of surface transportation systems by limiting or eliminating human involvement in the driving tasks.
Error-Covariance Analysis of Monocular Pose Estimation Using Total Least Squares
First, the optimization framework is formulated for the pose estimation problem with observation vectors extracted from unit vectors from the camera center-of-projection, pointing towards the image features.
Model-based recursive partitioning for discrete event times
We propose MOB for discrete Survival data (MOB-dS) which controls the type I error rate of the test used for data splitting and therefore the rate of identifying subgroups although none is present.
An update on statistical boosting in biomedicine
Statistical boosting algorithms have triggered a lot of research during the last decade.
Stability selection for component-wise gradient boosting in multiple dimensions
We apply this new algorithm to a study to estimate abundance of common eider in Massachusetts, USA, featuring excess zeros, overdispersion, non-linearity and spatio-temporal structures.
Boosting Joint Models for Longitudinal and Time-to-Event Data
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times.
On the use of Harrell's C for clinical risk prediction via random survival forests
In simulation studies and with the help of two medical data sets we demonstrate that the accuracy of RSF predictions, as measured by Harrell's C, can be improved if the log-rank statistic is replaced by the C index for node splitting.
The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling
This review article attempts to highlight this evolution of boosting algorithms from machine learning to statistical modelling.
Methodology
Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations
The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics.
