Freeform Fabrication of Layered Halide Perovskite Nanowire Heterojunctions
‘All‐at‐once’ freeform fabrication of layered perovskite nanowire heterojunctions is realized by 3D printing employing meniscus‐guided crystallization. The integration of nanoscale additive manufacturing with layered halide perovskites will provide a versatile platform to freely design and realize highly stable perovskite‐based optoelectronic ...
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Specific GPCRs elicit unique extracellular vesicle miRNA array signatures. [PDF]
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tvsfglasso: Time-varying scale-free graphical lasso for network estimation from time-series data. [PDF]
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Deconfounded and debiased estimation for high-dimensional linear regression under hidden confounding with application to omics data. [PDF]
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A Robust Adaptive Filtering Framework for Smartphone GNSS/PDR-Integrated Positioning. [PDF]
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It is known that multicollinearity inflates the variance of the maximum likelihood estimator in logistic regression. Especially, if the primary interest is in the coefficients, the impact of collinearity can be very serious. To deal with collinearity, a ridge estimator was proposed by Schaefer et al. The primary interest of this article is to introduce
Deniz Inan, Birsen E. Erdogan
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Liu-type estimator in semiparametric regression models
Journal of Statistical Computation and Simulation, 2010In this paper, we introduced a Liu-type estimator for the vector of parameters β in a semiparametric regression model. We also obtained the semiparametric restricted Liu-type estimator for the parametric component in a semiparametric regression model. The ideas in the paper are illustrated in a real data example and in a Monte Carlo simulation study.
Akdeniz F., Duran E.A.
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Using Liu-Type Estimator to Combat Collinearity
Communications in Statistics - Theory and Methods, 2003Linear regression model and least squares method are widely used in many fields of natural and social sciences. In the presence of collinearity, the least squares estimator is unstable and often gives misleading information. Ridge regression is the most common method to overcome this problem.
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