Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees

2017 ◽  
Vol 27 (2) ◽  
pp. e1-e12 ◽  
Author(s):  
S. H. C. M. van Veen ◽  
R. C. van Kleef ◽  
W. P. M. M. van de Ven ◽  
R. C. J. A. van Vliet
Keyword(s):  
Predictive Power ◽  
Regression Trees ◽  
Risk Equalization ◽  
Interaction Terms

scholarly journals EARLY DETECTION OF FACTORS, INCLUDING PANDEMICS AND DISASTERS, LEADING TO LANGUAGE ENDANGERMENT: THINKING STATISTICALLY

2021 ◽  
Vol 11 (1) ◽  
pp. 31-35
Author(s):  
Deepak Sharma
Keyword(s):  
Factor Analysis ◽  
Predictive Power ◽  
Research Work ◽  
Statistical Technique ◽  
Endangered Languages ◽  
Language Endangerment ◽  
Future Studies ◽  
Interaction Terms ◽  
Two Factors ◽  
Significant Factors

The target of this research work is to use a statistical technique on different languages to identify significant factors of endangered languages with similar characteristics to build a model for language endangerment. Factor analysis is used to identify factors. The factors are used to construct a model with and without interaction terms. First three variables (i.e. speakers, longitude and latitude) are analyzed to identify two factors and then these three variables and three interaction terms are used to construct the model. Different variables were identified and a model with and without interaction terms is built using the identified factors. The result shows that the model has significant predictive power. The predictors were retrieved from the dataset. The outcome encourages future studies towards defining techniques of language endangerment prediction for analyzing factors of language endangerment.

Prediction in Medicine by Integrating Regression Trees into Regression Analysis with Optimal Scaling

2001 ◽  
Vol 40 (05) ◽  
pp. 403-409 ◽  
Author(s):  
J. J. Meulman ◽  
E. Dusseldorp
Keyword(s):  
Regression Analysis ◽  
Regression Trees ◽  
Relative Increase ◽  
Optimal Scaling ◽  
Model Interaction ◽  
Interaction Terms ◽  
Interaction Variables ◽  
Analysis Strategy ◽  
The One ◽  
Prediction Problems

Summary Objectives: A new data-analysis strategy is proposed to solve the problems of selecting interaction terms in linear regression on the one hand, and of statistically testing the significance of regression trees on the other hand. Methods: The proposed strategy combines two data mining techniques: regression trees and regression analysis with optimal scaling (CATREG). The method traces small regression trees using the bootstrap and integrates the results as interaction variables (called “trunk variables”) into CATREG. Results: An application to data from cardiac patients shows a relative increase of 19% variance accounted for (16% cross-validated variance), by the CATREG model including the trunk variables compared to the model excluding these variables. Conclusions: This study indicates that trunk variables can be useful to model interaction effects in prediction problems.

How much do hazard mitigation plans cost? An analysis of federal grant data

2017 ◽  
Vol 11 (4) ◽  
pp. 271
Author(s):  
Andrea M. Jackman, PhD ◽  
Mario G. Beruvides, PhD, PE
Keyword(s):  
Local Governments ◽  
Predictive Power ◽  
Hazard Mitigation ◽  
Disaster Mitigation ◽  
Matrix Analysis ◽  
Federal Grant ◽  
Interaction Terms ◽  
Final Rule ◽  
The Cost

Under the Disaster Mitigation Act of 2000 and Federal Emergency Management Agency’s subsequent Interim Final Rule, the requirement was placed on local governments to author and gain approval for a Hazard Mitigation Plan (HMP) for the areas under their jurisdiction. Low completion percentages for HMPs—less than one-third of eligible governments—were found by an analysis conducted 3 years after the final deadline for the aforementioned legislation took place. Follow-up studies showed little improvement at 5 and 8 years after the deadline. It was hypothesized that the cost of a HMP is a significant factor in determining whether or not a plan is completed. A study was conducted using Boolean Matrix Analysis methods to determine what, if any, characteristics of a certain community will most influence the cost of a HMP. The frequency of natural hazards experienced by the planning area, the number of jurisdictions participating in the HMP, the population, and population density were found to significantly affect cost. These variables were used in a regression analysis to determine their predictive power for cost. It was found that along with two interaction terms, the variables explain approximately half the variation in HMP cost.

Communicating the predictive power of selection and admissions measures

2008 ◽  
Author(s):  
Sara Cooper ◽  
Nathan Kuncel ◽  
Kara Siegert
Keyword(s):  
Predictive Power

Does the embedded atom model have predictive power?

2020 ◽  
Vol 190 (12) ◽  
pp. 1233-1260
Author(s):  
David K. Belashchenko
Keyword(s):  
Predictive Power ◽  
Atom Model ◽  
Embedded Atom
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