19 papers · ranked by Valyu relevance
L Berrie, Kellyn F Arnold, GD Tomova, MS Gilthorpe + 1 more
| ABSTRACT | 3 | | --- | --- | | KEY WORDS | 3 | | INTRODUCTION | 4 | | DEPICTING DETERMINISTIC VARIABLES WITHIN DAGS | 4 | | Figure 1 | 5 | | ALGORITHMIC APPROACHES AND THE BENEFIT OF DAGS | 5 | | UNDERSTANDING TAUTOLOGICAL ASSOCIATIONS | 6 | | Figure 2 | 7 | | DEPICTING AND CONSIDERING COMPOSITIONAL DATA | 7 | |…
Ignophi Hu
This article examines the structural implications of composite variables developed in the field of “ageing”. First, using data from the Balti-more Longitudinal Study of Aging (BLSA), we illustrate how the property of feature convergence arises in practice. Second, we show how this constrains both cross-sectional…
Xi Yu, Florian Schuberth, Jörg Henseler
Composites, which refer to weighted linear combinations of variables, are receiving increasing attention in the field of ecology. In practice, however, researchers relying on the common approaches to study composites encounter limitations in flexibly specifying composites with structural equation modeling (SEM). To…
Gregory R. Hancock, Florian Schuberth
Composites, or linear combinations of variables, play an important role in multivariate behavioral research. They appear in the form of indices, inventories, formative constructs, parcels, and emergent variables. Although structural equation modeling is widely used to study relations between variables, current…
Tamara Schamberger, Florian Schuberth, Jörg Henseler, Yves Rosseel
Structural equation modeling (SEM) is a prevalent approach for studying constructs. Traditionally, these constructs are modeled as reflectively measured latent variables – common factors that account for the variance-covariance structure of their associated indicators. Over the past two decades, there has been growing…
Laurie Berrie, Kellyn F Arnold, Georgia D Tomova, Mark S Gilthorpe + 1 more
'Peter W G Tennant'] Title: Abstract Deterministic variables are variables that are functionally determined by one or more parent variables. They commonly arise when a variable has been functionally created from one or more parent variables, as with derived variables, and in compositional data, where the “whole”…
Florian Schuberth, Tamara Schamberger, Ildikó Kemény, Jörg Henseler
In principle, structural equation modeling (SEM) is capable of emulating all approaches based on the general linear model. Yet, modeling sum scores in a structural equation model is not straightforward. Existing approaches to studying sum scores in a structural equation model are limited in terms either of model…
Tamara Schamberger, Florian Schuberth, Jörg Henseler
Moderated mediation models are crucial in many disciplines, particularly the social sciences. Researchers use them to analyze the conditions under which different variables are related. Structural equation modeling (SEM) is an eminently suitable framework for this endeavor. In fact, several approaches have been…
Florian Schuberth, Jörg Henseler, Theo K. Dijkstra
This article introduces confirmatory composite analysis (CCA) as a structural equation modeling technique that aims at testing composite models. It facilitates the operationalization and assessment of design concepts, so-called artifacts. CCA entails the same steps as confirmatory factor analysis: model specification…
Sarah F. Schoch, Reto Huber, Malcolm Kohler, Salome Kurth
Sleep is ubiquitous during infancy and important for the well-being of both infant and parent. Therefore, there is large interest to characterize infant sleep with reliable tools, for example by means of combining actigraphy with 24-h-diaries. However, it is critical to select the right variables to characterize sleep.…
Davide Ferrari, Chao Zheng
Composite likelihood estimation has an important role in the analysis of multivariate data for which the full likelihood function is intractable. An important issue in composite likelihood inference is the choice of the weights associated with lowerdimensional data sub-sets, since the presence of incompatible…
Manuela Cattelan, Nicola Sartori
Composite likelihood inference has gained much popularity thanks to its computational manageability and its theoretical properties. Unfortunately, performing composite likelihood ratio tests is inconvenient because of their awkward asymptotic distribution. There are many proposals for adjusting composite likelihood…
Amy Ko, Rasmus Nielsen
Pedigrees contain information about the genealogical relationships among individuals and are of fundamental importance in many areas of genetic studies. However, pedigrees are often unknown and must be inferred from genetic data. Despite the importance of pedigree inference, existing methods are limited to inferring…
Mohammad Hamidul Islam, Shaila Afroj, Nazmul Karim
High-performance fibre reinforced polymer (FRP) composites offer excellent specific strength and stiffness when compared to high-density metallic materials. However, their inherent brittleness leads to sudden and catastrophic failure without sufficient pre-warning, rendering them unsuitable for many applications. To…
Lauren Alpert Sugden, Elizabeth G. Atkinson, Annie P. Fischer, Stephen Rong + 2 more
Statistical methods for identifying adaptive mutations from population-genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce…
Thomas Lamy, Nathan I. Wisnoski, Riley Andrade, Max C.N. Castorani + 10 more
There is increasing interest in measuring ecological stability to understand how communities and ecosystems respond to broad-scale global changes. One of the most common approaches is to quantify the variation through time in community or ecosystem aggregate attributes (e.g., total biomass), referred to as aggregate…
S Mangiola, A Schulze, M Trussart, E Zozaya + 6 more
Cell omics such as single-cell genomics, proteomics and microbiomics allow the characterisation of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to unveiling markers of disease progression such as cancer and pathogen…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Authors not listed
Discovery-oriented research is a fundamental pursuit in chemical and materials science, especially when objective-free or purpose-ambiguous exploration can yield unexpected novel compounds or materials. Recently, data-driven objective-free exploration methods have emerged to support such discovery in materials science.…