scholarly journals Linear and nonlinear associations between general intelligence and personality in Project TALENT.

2014 ◽  
Vol 106 (4) ◽  
pp. 638-654 ◽  
Author(s):  
Jason T. Major ◽  
Wendy Johnson ◽  
Ian J. Deary
Keyword(s):  
General Intelligence ◽  
Nonlinear Associations ◽  
Linear And Nonlinear

Examining Linear and Nonlinear Associations Between Negative Emotional Reactivity to Daily Events and Depression Among Adolescents

2021 ◽  
pp. 216770262110456
Author(s):  
Angela C. Santee ◽  
Lisa R. Starr
Keyword(s):  
Emotional Reactivity ◽  
Severe Depression ◽  
Daily Hassles ◽  
Depression Symptoms ◽  
Daily Events ◽  
Nonlinear Associations ◽  
Linear And Nonlinear ◽  
Competing Hypotheses

Existing research supports competing hypotheses about the link between negative emotional (NE) reactivity to daily events (e.g., hassles and uplifts) and depression. Some have suggested that depression is associated with blunted reactivity, and others have suggested that depression is associated with heightened reactivity. In this study, we tested linear and nonlinear associations, cross-sectionally and longitudinally, between NE reactivity and depression among a sample of 232 adolescents. Participants completed lab-based assessments of depression then rated their experience of emotions, daily hassles, and uplifts three times per day for 7 days. Interviews were readministered 1.5 years later. Results show a significant U-shaped relationship between NE reactivity to hassles and depression symptoms cross-sectionally, which suggests that depression is more severe at the extremes of NE reactivity. NE reactivity to daily uplifts showed significant linear associations, but not quadratic associations, with depression such that heightened reactivity to uplifts was associated with more severe depression symptoms concurrently and predicted worsening of depression longitudinally.

scholarly journals Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection

Genetics ◽  
2020 ◽  
Vol 215 (3) ◽  
pp. 597-607
Author(s):  
Juho A. J. Kontio ◽  
Marko J. Rinta-aho ◽  
Mikko J. Sillanpää
Keyword(s):  
Regulatory Networks ◽  
Selection Procedure ◽  
Critical Issue ◽  
Small Sample ◽  
Estimation Methods ◽  
Gene Coexpression ◽  
Neighborhood Selection ◽  
Nonlinear Associations ◽  
Linear And Nonlinear ◽  
Coexpression Networks

Whereas nonlinear relationships between genes are acknowledged, there exist only a few methods for estimating nonlinear gene coexpression networks or gene regulatory networks (GCNs/GRNs) with common deficiencies. These methods often consider only pairwise associations between genes, and are, therefore, poorly capable of identifying higher-order regulatory patterns when multiple genes should be considered simultaneously. Another critical issue in current nonlinear GCN/GRN estimation approaches is that they consider linear and nonlinear dependencies at the same time in confounded form nonparametrically. This severely undermines the possibilities for nonlinear associations to be found, since the power of detecting nonlinear dependencies is lower compared to linear dependencies, and the sparsity-inducing procedures might favor linear relationships over nonlinear ones only due to small sample sizes. In this paper, we propose a method to estimate undirected nonlinear GCNs independently from the linear associations between genes based on a novel semiparametric neighborhood selection procedure capable of identifying complex nonlinear associations between genes. Simulation studies using the common DREAM3 and DREAM9 datasets show that the proposed method compares superiorly to the current nonlinear GCN/GRN estimation methods.

scholarly journals Linear and Nonlinear Associations between Sleep and Adjustment in Adolescence

2019 ◽  
Vol 18 (5) ◽  
pp. 690-704 ◽  
Author(s):  
Mina Shimizu ◽  
Brian T. Gillis ◽  
Joseph A. Buckhalt ◽  
Mona El-Sheikh
Keyword(s):  
Nonlinear Associations ◽  
Linear And Nonlinear

176 Linear and Nonlinear Associations Between Sleep and Academic Achievement in Middle Childhood: The Role of Early Life SES

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A71-A72
Author(s):  
Gianna Rea-Sandin ◽  
Reagan Breitenstein ◽  
Leah Doane ◽  
Emily Vakulskas ◽  
Carlos Valiente ◽  
...  
Keyword(s):  
Academic Achievement ◽  
Academic Performance ◽  
Sleep Duration ◽  
Early Life ◽  
Sleep Problems ◽  
Poor Sleep ◽  
Sleep Habits ◽  
Nonlinear Associations ◽  
Linear And Nonlinear ◽  
Children’S Sleep

Abstract Introduction Elementary-aged children in low socioeconomic environments are more likely to experience poor sleep, which can negatively impact academic performance. However, it is unknown whether early-life socioeconomic status (SES) influences associations between sleep and academic achievement later in childhood. Using a demographically diverse sample of children followed longitudinally from 1 to 8 years, we tested linear and nonlinear associations between actigraphy-based sleep duration, midpoint time, sleep duration variability, and parent-reported sleep problems with academic achievement. In addition, we examined whether these associations varied by early SES. Methods The sample comprised 707 twins (52% female; Mage=8.44 years; 28.7% Hispanic/Latinx; 29.7% at or below the poverty line). SES was ascertained at 1 and 8 years, and children wore actigraph watches to assess sleep for 7 nights (Mnights=6.79) and completed the Applied Math, Picture Vocabulary, and Passage Comprehension subtests of the Woodcock-Johnson IV Tests of Achievement. Primary caregivers also reported on their children’s sleep and academic performance (Children’s Sleep Habits Questionnaire and Health and Behavior Questionnaire, respectively). Results Sleep was not linearly related to academic achievement, but there was a significant quadratic association between sleep midpoint with Picture Vocabulary (b=0.28, p<.01) and Passage Comprehension (b=0.17, p<.05). More parent-reported sleep problems were negatively related to Applied Problems performance for lower (b=-1.16, p<.001) and positively associated for higher early SES (b = 1.00, p < .01). More parent-reported sleep problems predicted lower Passage Comprehension for lower (b = -0.59, p < .05), but not higher early SES. Longer sleep duration predicted higher parent-reported academic achievement for lower early SES (b=0.14, p<.01) and lower achievement for higher early SES (b=-0.23, p<.001). Conclusion Our findings illustrate the complex, sometimes nonlinear associations between children’s sleep and academic performance. Many associations varied by early-life SES, suggesting that early childhood environments have long-lasting implications for child functioning, over and above the effect of concurrent SES. Increasing the quantity and quality of children’s sleep could improve academic outcomes, particularly for children who have experienced socioeconomic disadvantage. Support (if any) This research was supported by grants from NICHD (R01HD079520 and R01HD086085) and ASU T. Denny Sanford School of Social and Family Dynamics.

scholarly journals Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 440
Author(s):  
Nezamoddin N. Kachouie ◽  
Wejdan Deebani
Keyword(s):  
Machine Learning ◽  
Robust Method ◽  
Pearson’S Correlation ◽  
Distance Correlation ◽  
Linear Relationships ◽  
Nonlinear Associations ◽  
Pearson's Correlation ◽  
Noisy Conditions ◽  
Linear And Nonlinear ◽  
Nonlinear Correlations

Background: In data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson’s correlation coefficient has been widely used, its value is reliable only for linear relationships and Distance correlation was introduced to address this shortcoming. Methods: Distance correlation can identify linear and nonlinear correlations. However, its performance drops in noisy conditions. In this paper, we introduce the Association Factor (AF) as a robust method for identification and quantification of linear and nonlinear associations in noisy conditions. Results: To test the performance of the proposed Association Factor, we modeled several simulations of linear and nonlinear relationships in different noise conditions and computed Pearson’s correlation, Distance correlation, and the proposed Association Factor. Conclusion: Our results show that the proposed method is robust in two ways. First, it can identify both linear and nonlinear associations. Second, the proposed Association Factor is reliable in both noiseless and noisy conditions.

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