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Related papers: Tree-Structured Modelling of Varying Coefficients

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Recently proposed budding tree is a decision tree algorithm in which every node is part internal node and part leaf. This allows representing every decision tree in a continuous parameter space, and therefore a budding tree can be jointly…

Machine Learning · Computer Science 2014-12-22 Ozan İrsoy , Ethem Alpaydın

Regression trees are becoming increasingly popular as omnibus predicting tools and as the basis of numerous modern statistical learning ensembles. Part of their popularity is their ability to create a regression prediction without ever…

Machine Learning · Statistics 2016-06-17 Will Ruth , Thomas Loughin

Model-based trees are used to find subgroups in data which differ with respect to model parameters. In some applications it is natural to keep some parameters fixed globally for all observations while asking if and how other parameters vary…

Computation · Statistics 2025-10-07 Heidi Seibold , Torsten Hothorn , Achim Zeileis

Tree methods (recursive partitioning) are a popular class of nonparametric methods for analyzing data. One extension of the basic tree methodology is the survival tree, which applies recursive partitioning to censored survival data. There…

Methodology · Statistics 2017-03-01 Wei Fu , Jeffrey S. Simonoff

Auxiliary information can increase the efficiency of survey estimators through an assisting model when the model captures some of the relationship between the auxiliary data and the study variables. Despite their superior properties,…

Methodology · Statistics 2017-12-18 Kelly S. McConville , Daniell Toth

Coupled natural systems are generally modeled at multiple abstraction levels. Both structural scale and behavioral complexity of these models are determinants in the kinds of questions that can be posed and answered. As scale and complexity…

Computational Engineering, Finance, and Science · Computer Science 2018-07-23 Hessam S. Sarjoughian , William A. Boyd , Miguel F. Acevedo

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to 'mine' variables of interest…

Econometrics · Economics 2020-12-22 Mochen Yang , Edward McFowland , Gordon Burtch , Gediminas Adomavicius

Regression trees are a popular machine learning algorithm that fit piecewise constant models by recursively partitioning the predictor space. This paper focuses on statistical inference for a data-dependent model obtained from a fitted…

Methodology · Statistics 2025-12-17 Soham Bakshi , Yiling Huang , Snigdha Panigrahi , Walter Dempsey

Copulas are a powerful tool for modeling multivariate distributions as they allow to separately estimate the univariate marginal distributions and the joint dependency structure. However, known parametric copulas offer limited flexibility…

Machine Learning · Statistics 2021-11-11 Tim Janke , Mohamed Ghanmi , Florian Steinke

A new family of tree models is proposed, which we call "differential trees." A differential tree model is constructed from multiple data sets and aims to detect distributional differences between them. The new methodology differs from the…

Methodology · Statistics 2012-10-01 Yong Wang , Ilze Ziedins , Mark Holmes , Neil Challands

In linear regression problems with related predictors, it is desirable to do variable selection and estimation by maintaining the hierarchical or structural relationships among predictors. In this paper we propose non-negative garrote…

Applications · Statistics 2010-11-03 Ming Yuan , V. Roshan Joseph , Hui Zou

In this paper, we investigate adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an…

Machine Learning · Computer Science 2013-12-30 N. Denizcan Vanli , Suleyman S. Kozat

Coloured probability tree models are statistical models coding conditional independence between events depicted in a tree graph. They are more general than the very important class of context-specific Bayesian networks. In this paper, we…

Statistics Theory · Mathematics 2020-06-16 Eliana Duarte , Christiane Görgen

The paper gives an overview of recent advances in structural equation modeling. A structural equation model is a multivariate statistical model that is determined by a mixed graph, also known as a path diagram. Our focus is on the…

Statistics Theory · Mathematics 2016-12-20 Mathias Drton

The task of distribution generalization concerns making reliable prediction of a response in unseen environments. The structural causal models are shown to be useful to model distribution changes through intervention. Motivated by the…

Methodology · Statistics 2022-06-14 Kang Du , Yu Xiang

This article presents a new way to understand the descriptive ability of tree shape statistics. Where before tree shape statistics were chosen by their ability to distinguish between macroevolutionary models, the ``resolution'' presented in…

Populations and Evolution · Quantitative Biology 2007-05-23 Frederick A. Matsen

Variational inference in probabilistic graphical models aims to approximate fundamental quantities such as marginal distributions and the partition function. Popular approaches are the Bethe approximation, tree-reweighted, and other types…

Machine Learning · Statistics 2025-02-06 Harald Leisenberger , Franz Pernkopf

Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment allocations for a heterogeneous population. A primary line of…

Methodology · Statistics 2023-12-20 Preetam Nandy , Xiufan Yu , Wanjun Liu , Ye Tu , Kinjal Basu , Shaunak Chatterjee

Statistical learning evolves quickly with more and more sophisticated models proposed to incorporate the complicated data structure from modern scientific and business problems. Varying index coefficient models extend varying coefficient…

Statistics Theory · Mathematics 2019-03-05 Li Jialiang , Lv Jing

We propose Partition Tree, a novel tree-based framework for conditional density estimation over general outcome spaces that supports both continuous and categorical variables within a unified formulation. Our approach models conditional…

Machine Learning · Computer Science 2026-05-13 Felipe Angelim , Alessandro Leite
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