[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
State of the art in selection of variables and functional forms in multivariable analysis—outstanding issues
W Sauerbrei, A Perperoglou, M Schmid… - … and prognostic research, 2020 - Springer
Background How to select variables and identify functional forms for continuous variables is
a key concern when creating a multivariable model. Ad hoc 'traditional'approaches to …
a key concern when creating a multivariable model. Ad hoc 'traditional'approaches to …
Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models
SN Wood - Journal of the Royal Statistical Society Series B …, 2011 - academic.oup.com
Recent work by Reiss and Ogden provides a theoretical basis for sometimes preferring
restricted maximum likelihood (REML) to generalized cross-validation (GCV) for smoothing …
restricted maximum likelihood (REML) to generalized cross-validation (GCV) for smoothing …
[图书][B] Generalized additive models: an introduction with R
SN Wood - 2017 - taylorfrancis.com
The first edition of this book has established itself as one of the leading references on
generalized additive models (GAMs), and the only book on the topic to be introductory in …
generalized additive models (GAMs), and the only book on the topic to be introductory in …
Practical variable selection for generalized additive models
The problem of variable selection within the class of generalized additive models, when
there are many covariates to choose from but the number of predictors is still somewhat …
there are many covariates to choose from but the number of predictors is still somewhat …
The evolution of boosting algorithms
Background: The concept of boosting emerged from the field of machine learning. The basic
idea is to boost the accuracy of a weak classifying tool by combining various instances into a …
idea is to boost the accuracy of a weak classifying tool by combining various instances into a …
Boosting algorithms: Regularization, prediction and model fitting
P Bühlmann, T Hothorn - 2007 - projecteuclid.org
We present a statistical perspective on boosting. Special emphasis is given to estimating
potentially complex parametric or nonparametric models, including generalized linear and …
potentially complex parametric or nonparametric models, including generalized linear and …
[图书][B] Regressionsmodelle
Alle im vorigen Kapitel beschriebenen Problemstellungen besitzen eine wesentliche
Gemeinsamkeit: Eigenschaften einer Zielvariablen y sollen in Abhängigkeit von Kovariablen …
Gemeinsamkeit: Eigenschaften einer Zielvariablen y sollen in Abhängigkeit von Kovariablen …
Machine learning models for classification and identification of significant attributes to detect type 2 diabetes
Abstract Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood
glucose levels due to insulin resistance and reduced pancreatic insulin production. The …
glucose levels due to insulin resistance and reduced pancreatic insulin production. The …
[图书][B] Modeling discrete time-to-event data
G Tutz, M Schmid - 2016 - Springer
In recent years, a large variety of textbooks dealing with time-to-event analysis has been
published. Most of these books focus on the statistical analysis of observations in continuous …
published. Most of these books focus on the statistical analysis of observations in continuous …