[PDF][PDF] Conditional transformation models: interpretable parametrisations and censoring
L Möst - 2015 - core.ac.uk
Most well-known regression models focus on the estimation of the conditional mean given a
set of explanatory variables. Higher moments of the distribution function are usually …
set of explanatory variables. Higher moments of the distribution function are usually …
[PDF][PDF] Conditional transformation models
L Möst - 2015 - edoc.ub.uni-muenchen.de
Most well-known regression models focus on the estimation of the conditional mean given a
set of explanatory variables. Higher moments of the distribution function are usually …
set of explanatory variables. Higher moments of the distribution function are usually …
[PDF][PDF] Conditional transformation models by example
The ultimate goal of regression analysis is to obtain information about the conditional
distribution of a response given a set of explanatory variables. This goal is, however, seldom …
distribution of a response given a set of explanatory variables. This goal is, however, seldom …
Bayesian conditional transformation models
Recent developments in statistical regression methodology shift away from pure mean
regression toward distributional regression models. One important strand thereof is that of …
regression toward distributional regression models. One important strand thereof is that of …
Conditional transformation models
The ultimate goal of regression analysis is to obtain information about the conditional
distribution of a response given a set of explanatory variables. This goal is, however, seldom …
distribution of a response given a set of explanatory variables. This goal is, however, seldom …
Most likely transformations: The mlt package
T Hothorn - Journal of Statistical Software, 2020 - jstatsoft.org
The mlt package implements maximum likelihood estimation in the class of conditional
transformation models. Based on a suitable explicit parameterization of the unconditional or …
transformation models. Based on a suitable explicit parameterization of the unconditional or …
Predicting birth weight with conditionally linear transformation models
L Möst, M Schmid, F Faschingbauer… - Statistical methods in …, 2016 - journals.sagepub.com
Low and high birth weight (BW) are important risk factors for neonatal morbidity and
mortality. Gynecologists must therefore accurately predict BW before delivery. Most …
mortality. Gynecologists must therefore accurately predict BW before delivery. Most …
Linear transformation model with parametric covariate transformations
C Fan, JP Fine - Journal of the American Statistical Association, 2013 - Taylor & Francis
The traditional linear transformation model assumes a linear relationship between the
transformed response and the covariates. However, in real data, this linear relationship may …
transformed response and the covariates. However, in real data, this linear relationship may …
Transformation boosting machines
T Hothorn - Statistics and Computing, 2020 - Springer
The broad class of conditional transformation models includes interpretable and simple as
well as potentially very complex models for conditional distributions. This makes conditional …
well as potentially very complex models for conditional distributions. This makes conditional …
Improved transformation‐based quantile regression
M Geraci, MC Jones - Canadian Journal of Statistics, 2015 - Wiley Online Library
Modelling the quantiles of a random variable is facilitated by their equivariance to monotone
transformations. In conditional modelling, transforming the response variable serves to …
transformations. In conditional modelling, transforming the response variable serves to …