Results 81 to 90 of about 1,128,323 (301)

Simultaneous Inference for Time Series Functional Linear Regression

, 2022
We consider the problem of joint simultaneous confidence band (JSCB) construction for regression coefficient functions of time series scalar-on-function linear regression when the regression model is estimated by roughness penalization approach with flexible choices of orthonormal basis functions.
Cui, Yan, Zhou, Zhou
openaire   +2 more sources

Estimating causal effects in linear regression models with observational data: The instrumental variables regression model.

Psychological methods, 2020
Instrumental variable methods are an underutilized tool to enhance causal inference in psychology. By way of incorporating predictors of the predictors (called "instruments" in the econometrics literature) into the model, instrumental variable regression
Albert Maydeu-Olivares, Dexin Shi, Amanda J. Fairchild   +2 more
semanticscholar   +1 more source

Structural instability impairs function of the UDP‐xylose synthase 1 Ile181Asn variant associated with short‐stature genetic syndrome in humans

FEBS Letters, EarlyView.
The Ile181Asn variant of human UDP‐xylose synthase (hUXS1), associated with a short‐stature genetic syndrome, has previously been reported as inactive. Our findings demonstrate that Ile181Asn‐hUXS1 retains catalytic activity similar to the wild‐type but exhibits reduced stability, a looser oligomeric state, and an increased tendency to precipitate ...
Tuo Li, Pedro A. Sánchez‐Murcia, Bernd Nidetzky   +2 more
wiley   +1 more source

Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction

Journal of Causal Inference, 2019
Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional
Luo Wei, Wu Wenbo, Zhu Yeying
doaj   +1 more source

Comparative analysis of various modelling techniques for emission prediction of diesel engine fueled by diesel fuel with nanoparticle additives

European Mechanical Science, 2017
In this study, emissions of compression ignition engine fueled by diesel fuel with nanoparticleadditives was modeled by regression analysis, artificial neural network (ANN) and adaptiveneuro fuzzy inference system (ANFIS) methods.
Erdi Tosun, Tayfun Ozgur, Ceyla Ozgur, Mustafa Ozcanli, Hasan Serin, Kadir Aydin   +5 more
doaj   +1 more source

Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques

Sensors, 2020
The foot strike pattern performed during running is an important variable for runners, performance practitioners, and industry specialists. Versatile, wearable sensors may provide foot strike information while encouraging the collection of diverse ...
Stephanie R. Moore, Christina Kranzinger, Julian Fritz, Thomas Stӧggl, Josef Krӧll, Hermann Schwameder   +5 more
doaj   +1 more source

A tutorial on Bayesian multi-model linear regression with BAS and JASP

Behavior Research Methods, 2020
Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the first stage, a single ‘best’ model is defined by a specific selection of relevant predictors; in the second stage, the regression coefficients of the ...
D. V. D. Bergh, M. Clyde, A. R. K. N. Gupta, T. de Jong, Q. Gronau, M. Marsman, A. Ly, E. Wagenmakers   +7 more
semanticscholar   +1 more source

Patient‐specific pharmacogenomics demonstrates xCT as predictive therapeutic target in colon cancer with possible implications in tumor connectivity

Molecular Oncology, EarlyView.
This study integrates transcriptomic profiling of matched tumor and healthy tissues from 32 colorectal cancer patients with functional validation in patient‐derived organoids, revealing dysregulated metabolic programs driven by overexpressed xCT (SLC7A11) and SLC3A2, identifying an oncogenic cystine/glutamate transporter signature linked to ...
Marco Strecker, Keren Zohar, Martin Böttcher, Thomas Wartmann, Henry Freudenstein, Maximilian Doelling, Mihailo Andric, Wenjie Shi, Or Kakhlon, Katrin Hippe, Beatrix Jahnke, Dimitrios Mougiakakos, Franziska Baenke, Daniel Stange, Roland S. Croner, Michal Linial, Ulf D. Kahlert   +16 more
wiley   +1 more source

Improved Uncertainty Quantification for Neural Networks With Bayesian Last Layer

IEEE Access, 2023
Uncertainty quantification is an important task in machine learning - a task in which standard neural networks (NNs) have traditionally not excelled. This can be a limitation for safety-critical applications, where uncertainty-aware methods like Gaussian
Felix Fiedler, Sergio Lucia
doaj   +1 more source

Reduced vascular leakage correlates with breast carcinoma T regulatory cell infiltration but not with metastatic propensity

Molecular Oncology, EarlyView.
A mouse model for vascular normalization and a human breast cancer cohort were studied to understand the relationship between vascular leakage and tumor immune suppression. For this, endothelial and immune cell RNAseq, staining for vascular function, and immune cell profiling were employed.
Liqun He, Chiara Testini, Neda Hekmati, Altea Bonello, Aglaia Schiza, Emmanuel Nwadozi, Mia Phillipson, Carina Strell, Michael Welsh   +8 more
wiley   +1 more source