Zobrazeno 1 - 10
of 826
pro vyhledávání: '"Kneib, Thomas"'
Publikováno v:
Tutorials in Quantitative Methods for Psychology, Vol 19, Iss 4, Pp 333-346 (2023)
This paper provides a tutorial for analyzing psychological research data with GAMLSS, an R package that uses the family of generalized additive models for location, scale, and shape. These models extend the capacities of traditional parametric and no
Externí odkaz:
https://doaj.org/article/2adf67064db948e7bc547c20193331c2
Autor:
Gutiérrez-Botella, Jesús, Armero, Carmen, Kneib, Thomas, Pata, María P., García-Seara, Javier
Competing risk models are survival models with several events of interest acting in competition and whose occurrence is only observed for the event that occurs first in time. This paper presents a Bayesian approach to these models in which the issue
Externí odkaz:
http://arxiv.org/abs/2409.16080
Autor:
Semnani, Parastoo, Bogojeski, Mihail, Bley, Florian, Zhang, Zizheng, Wu, Qiong, Kneib, Thomas, Herrmann, Jan, Weisser, Christoph, Patcas, Florina, Müller, Klaus-Robert
The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to complex interactions, catal
Externí odkaz:
http://arxiv.org/abs/2407.18935
Penalized transformation models (PTMs) are a novel form of location-scale regression. In PTMs, the shape of the response's conditional distribution is estimated directly from the data, and structured additive predictors are placed on its location and
Externí odkaz:
http://arxiv.org/abs/2404.07440
Topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in Natural Language Processing, however, several successful models that rely on straightforward clustering approaches in transform
Externí odkaz:
http://arxiv.org/abs/2403.03737
Spatial confounding is a fundamental issue in regression models for spatially indexed data. It arises because spatial random effects, included to approximate unmeasured spatial variation, are typically not independent of the covariates in the model.
Externí odkaz:
http://arxiv.org/abs/2309.16861
Laser-scanned point clouds of forests make it possible to extract valuable information for forest management. To consider single trees, a forest point cloud needs to be segmented into individual tree point clouds. Existing segmentation methods are us
Externí odkaz:
http://arxiv.org/abs/2309.08471
Joint models for longitudinal and time-to-event data have seen many developments in recent years. Though spatial joint models are still rare and the traditional proportional hazards formulation of the time-to-event part of the model is accompanied by
Externí odkaz:
http://arxiv.org/abs/2302.07020
Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this success, the inner workings of DNNs are often not transparent, making
Externí odkaz:
http://arxiv.org/abs/2301.11862
Liesel is a probabilistic programming framework focusing on but not limited to semi-parametric regression. It comprises a graph-based model building library, a Markov chain Monte Carlo (MCMC) library with support for modular inference algorithms comb
Externí odkaz:
http://arxiv.org/abs/2209.10975