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07/05/2023
A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables
High-cardinality categorical variables are variables for which the numbe...
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06/04/2021
varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models
Gaussian processes (GPs) are well-known tools for modeling dependent dat...
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05/19/2021
Latent Gaussian Model Boosting
Latent Gaussian models and boosting are widely used techniques in statis...
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01/06/2021
Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models
Spatially varying coefficient (SVC) models are a type of regression mode...
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04/06/2020
Gaussian Process Boosting
In this article, we propose a novel way to combine boosting with Gaussia...
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01/22/2020
Maximum Likelihood Estimation of Spatially Varying Coefficient Models for Large Data with an Application to Real Estate Price Prediction
In regression models for spatial data, it is often assumed that the marg...
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02/11/2019
KTBoost: Combined Kernel and Tree Boosting
In this article, we introduce a novel boosting algorithm called `KTBoost...
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08/09/2018
Gradient and Newton Boosting for Classification and Regression
Boosting algorithms enjoy large popularity due to their high predictive ...
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11/23/2017