scholarly journals Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects

2017 ◽  
Author(s):  
P. Richard Hahn ◽  
Jared S. Murray ◽  
Carlos M. Carvalho
Keyword(s):  
Causal Inference ◽  
Regression Tree ◽  
Bayesian Regression ◽  
Heterogeneous Effects ◽  
Tree Models

scholarly journals Restoring steppe landscapes: patterns, drivers and implications in Russia’s steppes

2020 ◽  
Author(s):  
Robert Pazur ◽  
Alexander V. Prishchepov ◽  
Ksenya Myachina ◽  
Peter H. Verburg ◽  
Sergey Levykin ◽  
...  
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Landsat Imagery ◽  
Regression Tree ◽  
Agricultural Land Use ◽  
Virgin Land ◽  
Human Footprint ◽  
Steppe Belt ◽  
Tree Models ◽  
Southern Russia

Abstract Context Agricultural land abandonment across the steppe belt of Eurasia has provided an opportunity for the restoration of steppe landscapes in recent decades. However, global food demands are about to revert this trajectory and put restored steppe landscapes at risk. Objectives We analysed steppe development in southern Russia in the last 40 years, assessed its spatial patterns and drivers of change for several periods. Methods Using Landsat imagery, we mapped the permanent steppe and steppe restoration from 1990 to 2018. Based on regression tree models, we evaluate and explain its dynamics. Results were compared with district-level trends in land-use intensities of cropland. Results We found 70% of the steppe in 2018 represented permanent steppe and 30% of former cropland dominantly abandoned in the postsocialism (1990–2000). The permanent steppe and steppe restored in the postsocialism (1990–2000) were located far from settlements, on rough terrain and in districts of the Virgin Land Campaign (1954–1963). In recent decades, the patterns of steppe restoration (2000–2018) were mostly determined by unfavourable agroclimatic conditions and distance from grain storage facilities. The restoration pattern reflects regional differences in land-use intensities, e.g., isolated steppe patches mostly appeared in areas of intensive agricultural land-use. Conclusions Steppe restoration has appeared in areas marginal for agricultural production, with poor natural conditions and little human footprint. Consequently, the permanent steppe became less fragmented and a more continuous steppe landscape resulted. The remaining isolated steppe patches require attention in restoration programs as they are mostly located in areas of intensive agricultural land-use.

scholarly journals Prediction of strontium bromide laser efficiency using cluster and decision tree analysis

2018 ◽  
Vol 167 ◽  
pp. 04002
Author(s):  
Iliycho Iliev ◽  
Snezhana Gocheva-Ilieva ◽  
Chavdar Kulin
Keyword(s):  
Electrical Power ◽  
Regression Tree ◽  
Biological Tissues ◽  
Maximum Efficiency ◽  
Laser Efficiency ◽  
Tree Models ◽  
Regression Techniques ◽  
Minimal Damage ◽  
Output Characteristics ◽  
Cluster Methods

Subject of investigation is a new high-powered strontium bromide (SrBr2) vapor laser emitting in multiline region of wavelengths. The laser is an alternative to the atom strontium lasers and electron free lasers, especially at the line 6.45 μm which line is used in surgery for medical processing of biological tissues and bones with minimal damage. In this paper the experimental data from measurements of operational and output characteristics of the laser are statistically processed by means of cluster analysis and tree-based regression techniques. The aim is to extract the more important relationships and dependences from the available data which influence the increase of the overall laser efficiency. There are constructed and analyzed a set of cluster models. It is shown by using different cluster methods that the seven investigated operational characteristics (laser tube diameter, length, supplied electrical power, and others) and laser efficiency are combined in 2 clusters. By the built regression tree models using Classification and Regression Trees (CART) technique there are obtained dependences to predict the values of efficiency, and especially the maximum efficiency with over 95% accuracy.

Abstraction Regression & Tree Models

2021 ◽  
pp. 5-36
Author(s):  
Shuai Huang ◽  
Houtao Deng
Keyword(s):  
Regression Tree ◽  
Tree Models

P4-044: IDENTIFICATION OF PHENOTYPIC CLUSTERS OF AD PATIENTS AND CONTROLS USING CLASSIFICATION AND REGRESSION TREE MODELS

2014 ◽  
Vol 10 ◽  
pp. P798-P799
Author(s):  
Charlotte Teunissen ◽  
Niki S.M. Schoonenboom ◽  
Pieter Jelle Visser ◽  
Wiesje M. Van der Flier ◽  
Dirk Knol ◽  
...  
Keyword(s):  
Regression Tree ◽  
Classification And Regression Tree ◽  
Tree Models ◽  
Classification And Regression
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