Population model comparison using multi-locus datasets

2014 ◽  
pp. 217-230
Keyword(s):  
Population Model ◽  
Model Comparison

scholarly journals A Guide for Sparse PCA: Model Comparison and Applications

Psychometrika ◽  
2021 ◽  
Author(s):  
Rosember Guerra-Urzola ◽  
Katrijn Van Deun ◽  
Juan C. Vera ◽  
Klaas Sijtsma
Keyword(s):  
Performance Measures ◽  
Simulation Study ◽  
Empirical Data ◽  
Population Model ◽  
Model Comparison ◽  
Optimization Criterion ◽  
Sparse Pca ◽  
Extensive Simulation ◽  
Explained Variance ◽  
And Performance

AbstractPCA is a popular tool for exploring and summarizing multivariate data, especially those consisting of many variables. PCA, however, is often not simple to interpret, as the components are a linear combination of the variables. To address this issue, numerous methods have been proposed to sparsify the nonzero coefficients in the components, including rotation-thresholding methods and, more recently, PCA methods subject to sparsity inducing penalties or constraints. Here, we offer guidelines on how to choose among the different sparse PCA methods. Current literature misses clear guidance on the properties and performance of the different sparse PCA methods, often relying on the misconception that the equivalence of the formulations for ordinary PCA also holds for sparse PCA. To guide potential users of sparse PCA methods, we first discuss several popular sparse PCA methods in terms of where the sparseness is imposed on the loadings or on the weights, assumed model, and optimization criterion used to impose sparseness. Second, using an extensive simulation study, we assess each of these methods by means of performance measures such as squared relative error, misidentification rate, and percentage of explained variance for several data generating models and conditions for the population model. Finally, two examples using empirical data are considered.

Identifying suboptimalities with factorial model comparison

2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma
Keyword(s):  
Experimental Design ◽  
Model Comparison ◽  
Process Models ◽  
Multiple Forms ◽  
Factorial Experimental Design ◽  
Factorial Model ◽  
Multiple Components ◽  
The Many

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

Numerical Analysis of the Age-Sex-Structured Population Dynamics Taking into Account Spatial Diffusion

2005 ◽  
Vol 10 (4) ◽  
pp. 365-381 ◽  
Author(s):  
Š. Repšys ◽  
V. Skakauskas
Keyword(s):  
Population Dynamics ◽  
Numerical Analysis ◽  
Population Model ◽  
Local Stability ◽  
Deterministic Model ◽  
Random Mating ◽  
Spatial Diffusion ◽  
Neumann Problems ◽  
Structured Population ◽  
Structured Population Dynamics

We present results of the numerical investigation of the homogenous Dirichlet and Neumann problems to an age-sex-structured population dynamics deterministic model taking into account random mating, female’s pregnancy, and spatial diffusion. We prove the existence of separable solutions to the non-dispersing population model and, by using the numerical experiment, corroborate their local stability.

Withdrawal: Model comparison of DBD-PA-induced body force in quiescent air and separated flow over NACA0015

2020 ◽  
Author(s):  
Di Chen ◽  
Kengo Asada ◽  
Satoshi Sekimoto ◽  
Hiroyuki Nishida ◽  
Kozo Fujii
Keyword(s):  
Model Comparison ◽  
Body Force ◽  
Separated Flow
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