Results 41 to 50 of about 2,939,863 (258)
A Stochastic Restricted Principal Components Regression Estimator in the Linear Model
The Scientific World Journal, 2014 We propose a new estimator to combat the multicollinearity in the linear model when there are stochastic linear restrictions on the regression coefficients.
Daojiang He, Yan Wu
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Molecular Oncology, EarlyView.Pair‐wise comparison of the CellSearch and FETCH enrichment technologies for circulating tumor cells (CTCs) from metastatic breast, prostate, and small cell lung cancer patients shows an increased capture of CTCs using FETCH enrichment. The clinical implementation of circulating tumor cells (CTCs) as a predictive tool for therapy efficacy in the ...
Michiel Stevens +6 more
wiley +1 more source
Two Kinds of Weighted Biased Estimators in Stochastic Restricted Regression Model
Journal of Applied Mathematics, 2014 We consider two kinds of weighted mixed almost unbiased estimators in a linear stochastic restricted regression model when the prior information and the sample information are not equally important.
Chaolin Liu +3 more
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Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method [PDF]
, 2014 BackgroundBiochemical systems with relatively low numbers of components must be simulated stochastically in order to capture their inherent noise. Although there has recently been considerable work on discrete stochastic solvers, there is still a need ...
Barrio Solórzano, Manuel +11 more
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Regression based scenario generation: Applications for performance management
Operations Research Perspectives, 2019 Regression analysis is a common tool in performance management and measurement in industry. Many firms wish to optimise their performance using Stochastic Programming but to the best of our knowledge there exists no scenario generation method for ...
Sovan Mitra +2 more
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Molecular Oncology, EarlyView.We established the avian chorioallantoic membrane (CAM) assay as a scalable in vivo model for studying circulating tumor cells (CTCs). Human gastrointestinal tumors spontaneously released genetically validated CTCs that were detected across multiple platforms, demonstrating that the CAM model provides an accessible tool for investigating early cancer ...
Dennis Roth +17 more
wiley +1 more source
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
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Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Scientific Reports, 2022 Describing the material flow stress and the associated uncertainty is essential for the plastic stochastic structural analysis. In this context, a data-driven approach-heteroscedastic sparse Gaussian process regression (HSGPR) with enhanced efficiency is
Baixi Chen, Luming Shen, Hao Zhang
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