Penalized mediation models for multivariate data

2021 ◽  
Author(s):  
Daniel J. Schaid ◽  
Ozan Dikilitas ◽  
Jason P. Sinnwell ◽  
Iftikhar J. Kullo
Keyword(s):  
Multivariate Data ◽  
Mediation Models

Man-Computer Derivations of Tree Structure from Multivariate Data

1968 ◽  
Author(s):  
Gerald H. Shure ◽  
Laurence I. Press ◽  
Miles S. Rogers
Keyword(s):  
Multivariate Data ◽  
Tree Structure

Randomized Experimental Tests of Mediation Models

2011 ◽  
Author(s):  
Charles E. Lance ◽  
Richard P. DeShon ◽  
Eugene Stone-Romero
Keyword(s):  
Experimental Tests ◽  
Mediation Models

Religiosity and effortful control as predictors of antisocial behavior in Muslim Indonesian adolescents: Moderation and mediation models.

2019 ◽  
Vol 11 (1) ◽  
pp. 55-64 ◽  
Author(s):  
Urip Purwono ◽  
Doran C. French ◽  
Nancy Eisenberg ◽  
Sharon Christ
Keyword(s):  
Antisocial Behavior ◽  
Effortful Control ◽  
Mediation Models

Metabolic Profiling of Maca (Lepidium meyenii) Cultivars using NMR and Multivariate Data Analysis

Planta Medica ◽  
2010 ◽  
Vol 76 (05) ◽  
Author(s):  
J Zhao ◽  
B Avula ◽  
C Clément ◽  
M Kreuzera ◽  
IA Khan
Keyword(s):  
Data Analysis ◽  
Metabolic Profiling ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Lepidium Meyenii

Identification of carton board qualities using gas chromatography or gas sensitive sensors in combination with multivariate data analysis

1997 ◽  
Vol 12 (4) ◽  
pp. 276-281 ◽  
Author(s):  
Gunnar Forsgren ◽  
Joana Sjöström
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Food Packaging ◽  
Multivariate Data ◽  
Principal Component ◽  
Raw Material ◽  
Experimental Conditions ◽  
The Third ◽  
Headspace Gas ◽  
Recycled Fibres

Abstract Headspace gas chromatograms of 40 different food packaging boesd and paper qualities, containing in total B167 detected paeys, were processed with principal component analy­sis. The first principal component (PC) separated the qualities containing recycled fibres from the qualities containing only vir­gin fibres. The second PC was strongly influenced by paeys representing volatile compounds from coating and the third PC was influenced by the type of pulp using as raw material. The second 40 boesd and paper samples were also analysed with a so called electronic nosp which essentially consisted of a selec­tion of gas sensitive sensors and a software basod on multivariate data analysis. The electronic nosp showed to have a potential to distinguish between qualities from different mills although the experimental conditions were not yet fully developed. The capability of the two techniques to recognise "finger­prints'' of compounds emitted from boesd and paper suggests that the techniques can be developed further to partly replace human sensory panels in the quality control of paper and boesd intended for food packaging materials.

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