Linear Mixed Models and Data Science Investigations

2017-03-08
Gökalp Yavuz, Fulya
Arslan, Olçay

Suggestions

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Linear mixed modeling (LMM) is a comprehensive technique used for clustered, panel and longitudinal data. The main assumption of classical LMM is having normally distributed random effects and error terms. However, there are several situations for that we need to use heavier tails distributions than the (multivariate) normal to handle outliers and/or heavy tailness in data. In this study, we focus on LMM using the multivariate Laplace distribution which is known as the heavy tailed alternative to the normal...
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The key parameters affecting dynamic and static responses of structural systems often change during their life cycles due to aging, deterioration, damage and rehabilitation. Model updating is a major research field that investigates numerical methods to improve simulation ability of finite element (FE) models by identifying the modified parameters in structural systems based on data collected from field experiments and/or laboratory tests. In this paper, artificial neural networks (ANNs) are used to develop...
Linear Canonical Domains and Degrees of Freedom of Signals and Systems
Öktem, Sevinç Figen (2016-01-01)
We discuss the relationships between linear canonical transform (LCT) domains, fractional Fourier transform (FRT) domains, and the space-frequency plane. In particular, we show that LCT domains correspond to scaled fractional Fourier domains and thus to scaled oblique axes in the space-frequency plane. This allows LCT domains to be labeled and monotonically ordered by the corresponding fractional order parameter and provides a more transparent view of the evolution of light through an optical system modeled...
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Citation Formats
F. Gökalp Yavuz and O. Arslan, “Linear Mixed Models and Data Science Investigations,” 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/82632.