scholarly journals Isotonic distributional regression

2021 ◽  
Author(s):  
Alexander Henzi ◽  
Johanna F. Ziegel ◽  
Tilmann Gneiting
Keyword(s):  
Distributional Regression

Effect Selection and Regularization in Structured Additive Distributional Regression

2021 ◽  
pp. 271-296
Author(s):  
Paul Wiemann ◽  
Thomas Kneib ◽  
Helga Wagner
Keyword(s):  
Distributional Regression

scholarly journals Flexible instrumental variable distributional regression

2020 ◽  
Vol 183 (4) ◽  
pp. 1553-1574
Author(s):  
Guillermo Briseño Sanchez ◽  
Maike Hohberg ◽  
Andreas Groll ◽  
Thomas Kneib
Keyword(s):  
Instrumental Variable ◽  
Distributional Regression

scholarly journals Bivariate Gaussian models for wind vectors in a distributional regression framework

2019 ◽  
Vol 5 (2) ◽  
pp. 115-132 ◽  
Author(s):  
Moritz N. Lang ◽  
Georg J. Mayr ◽  
Reto Stauffer ◽  
Achim Zeileis
Keyword(s):  
Wind Direction ◽  
Wind Component ◽  
Smooth Functions ◽  
Ensemble Forecasts ◽  
Alpine Valley ◽  
Explanatory Variables ◽  
Predictive Skill ◽  
Regression Framework ◽  
Distribution Parameters ◽  
Distributional Regression

Abstract. A new probabilistic post-processing method for wind vectors is presented in a distributional regression framework employing the bivariate Gaussian distribution. In contrast to previous studies, all parameters of the distribution are simultaneously modeled, namely the location and scale parameters for both wind components and also the correlation coefficient between them employing flexible regression splines. To capture a possible mismatch between the predicted and observed wind direction, ensemble forecasts of both wind components are included using flexible two-dimensional smooth functions. This encompasses a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction. The performance of the new method is tested for stations located in plains, in mountain foreland, and within an alpine valley, employing ECMWF ensemble forecasts as explanatory variables for all distribution parameters. The rotation-allowing model shows distinct improvements in terms of predictive skill for all sites compared to a baseline model that post-processes each wind component separately. Moreover, different correlation specifications are tested, and small improvements compared to the model setup with no estimated correlation could be found for stations located in alpine valleys.

scholarly journals Bayesian Effect Selection in Structured Additive Distributional Regression Models

2020 ◽  
Author(s):  
Nadja Klein ◽  
Manuel Carlan ◽  
Thomas Kneib ◽  
Stefan Lang ◽  
Helga Wagner
Keyword(s):  
Regression Models ◽  
Distributional Regression

scholarly journals Latent class distributional regression for the estimation of non-linear reference limits from contaminated data sources

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Tobias Hepp ◽  
Jakob Zierk ◽  
Manfred Rauh ◽  
Markus Metzler ◽  
Andreas Mayr
Keyword(s):  
Latent Class ◽  
Data Sources ◽  
Medical Decision ◽  
Healthy Population ◽  
Test Results ◽  
Contaminated Data ◽  
Latent Heterogeneity ◽  
Reference Limits ◽  
Non Linear ◽  
Distributional Regression

Abstract Background Medical decision making based on quantitative test results depends on reliable reference intervals, which represent the range of physiological test results in a healthy population. Current methods for the estimation of reference limits focus either on modelling the age-dependent dynamics of different analytes directly in a prospective setting or the extraction of independent distributions from contaminated data sources, e.g. data with latent heterogeneity due to unlabeled pathologic cases. In this article, we propose a new method to estimate indirect reference limits with non-linear dependencies on covariates from contaminated datasets by combining the framework of mixture models and distributional regression. Results Simulation results based on mixtures of Gaussian and gamma distributions suggest accurate approximation of the true quantiles that improves with increasing sample size and decreasing overlap between the mixture components. Due to the high flexibility of the framework, initialization of the algorithm requires careful considerations regarding appropriate starting weights. Estimated quantiles from the extracted distribution of healthy hemoglobin concentration in boys and girls provide clinically useful pediatric reference limits similar to solutions obtained using different approaches which require more samples and are computationally more expensive. Conclusions Latent class distributional regression models represent the first method to estimate indirect non-linear reference limits from a single model fit, but the general scope of applications can be extended to other scenarios with latent heterogeneity.

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