Regression Splines for Multivariate Additive Modeling

Abstract This paper introduces the generalized additive mixed model (GAMM) and the quantile generalized additive mixed model (QGAMM) through reanalyses of bilinguals’ lexical decision data from Dijkstra et al. (2010) and Miwa et al. (2014). We illustrate how regression splines can be used to test for nonlinear effects of cross-language similarity in form as well as for controlling experimental trial effects. We further illustrate the tensor product smooth for a nonlinear interaction between cross-language semantic similarity and word frequency. Finally, we show how the QGAMM helps clarify whether the effect of a particular predictor is constant across distributions of RTs.

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New Statistical Methods Inspired by Data Collected from Alcohol and Substance Abuse Research

10.1093/oso/9780190676001.003.0021 ◽

2018 ◽

Author(s):

Anne Buu ◽

Runze Li

Keyword(s):

Risk Factors ◽

Substance Abuse ◽

Longitudinal Data ◽

Developmental Trajectories ◽

Regression Splines ◽

Simulation Studies ◽

Process Data ◽

Measurement Reactivity ◽

Longitudinal Association ◽

Highly Correlated

This chapter provides a nontechnical review of new statistical methodology for longitudinal data analysis that has been published in statistical journals in recent years. The methodology has applications in four important areas: (1) conducting variable selection among many highly correlated risk factors when the outcome measure is zero-inflated count; (2) characterizing developmental trajectories of symptomatology using regression splines; (3) modeling the longitudinal association between risk factors and substance use outcomes as time-varying effects; and (4) testing measurement reactivity and predictive validity using daily process data. The excellent statistical properties of the methods introduced have been supported by simulation studies. The applications in alcohol and substance abuse research have also been demonstrated by graphs on real longitudinal data.

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Estimation of modified expansive soil CBR with multivariate adaptive regression splines, random forest and gradient boosting machine

Innovative Infrastructure Solutions ◽

10.1007/s41062-021-00568-z ◽

2021 ◽

Vol 6 (4) ◽

Author(s):

Chijioke Christopher Ikeagwuani

Keyword(s):

Random Forest ◽

Expansive Soil ◽

Multivariate Adaptive Regression Splines ◽

Gradient Boosting ◽

Regression Splines ◽

Gradient Boosting Machine ◽

Adaptive Regression ◽

Adaptive Regression Splines

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Agro-Environmental Determinants of Leptospirosis: A Retrospective Spatiotemporal Analysis (2004−2014) in Mahasarakham Province (Thailand)

Tropical Medicine and Infectious Disease ◽

10.3390/tropicalmed6030115 ◽

2021 ◽

Vol 6 (3) ◽

pp. 115

Author(s):

Jaruwan Viroj ◽

Julien Claude ◽

Claire Lajaunie ◽

Julien Cappelle ◽

Anamika Kritiyakan ◽

...

Keyword(s):

Public Health ◽

Animal Health ◽

Spatiotemporal Analysis ◽

Health Concern ◽

Intersectoral Collaboration ◽

The Public ◽

Additive Modeling ◽

The One ◽

And Control ◽

Northeastern Thailand

Leptospirosis has been recognized as a major public health concern in Thailand following dramatic outbreaks. We analyzed human leptospirosis incidence between 2004 and 2014 in Mahasarakham province, Northeastern Thailand, in order to identify the agronomical and environmental factors likely to explain incidence at the level of 133 sub-districts and 1,982 villages of the province. We performed general additive modeling (GAM) in order to take the spatial-temporal epidemiological dynamics into account. The results of GAM analyses showed that the average slope, population size, pig density, cow density and flood cover were significantly associated with leptospirosis occurrence in a district. Our results stress the importance of livestock favoring leptospirosis transmission to humans and suggest that prevention and control of leptospirosis need strong intersectoral collaboration between the public health, the livestock department and local communities. More specifically, such collaboration should integrate leptospirosis surveillance in both public and animal health for a better control of diseases in livestock while promoting public health prevention as encouraged by the One Health approach.

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