Gradient boosting for linear mixed models

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Mixed Models based on Likelihood Boosting

Regularization for generalized additive mixed models by likelihood-based boosting.

OBJECTIVE With the emergence of semi- and nonparametric regression the generalized linear mixed model has been extended to account for additive predictors. However, available fitting methods fail in high dimensional settings where many explanatory variables are present. We extend the concept of boosting to generalized additive mixed models and present an appropriate algorithm that uses two diff...

Gradient Boosting for Sequence Alignment

Sequence alignment is a common subtask in many applications such as genetic matching and music information retrieval. Crucial to the performance of any sequence alignment algorithm is an accurate model of the reward of transforming one sequence into another. Using this model, we can find the optimal alignment of two sequences or perform query-based selection from a database of target sequences ...

Gradient Boosting With Piece-Wise Linear Regression Trees

Gradient boosting using decision trees as base learners, so called Gradient Boosted Decision Trees (GBDT), is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, various GDBT construction algorithms and implementation have been designed and heavily optimized in some very popular open sourced toolkits such as XGBoost and LightGBM. In this paper, ...