Results 41 to 50 of about 1,934,800 (291)

A Comparison of Logistic Regression Models for DIF Detection in Polytomous Items: The Effect of Small Sample Sizes and Non-Normality of Ability Distributions

open access: yesInternational Journal of Assessment Tools in Education, 2016
This study investigated the effectiveness of logistic regression models to detect uniform and non-uniform DIF in polytomous items across small sample sizes and non-normality of ability distributions.
M. David Miller   +2 more
doaj   +2 more sources

Spatio-Temporal Forecasting of Global Horizontal Irradiance Using Bayesian Inference

open access: yesApplied Sciences, 2022
Accurate global horizontal irradiance (GHI) forecasting promotes power grid stability. Most of the research on solar irradiance forecasting has been based on a single-site analysis.
Caston Sigauke   +2 more
doaj   +1 more source

Dynamics of the real exchange rate in European emerging economies: Evidence from quantile regression [PDF]

open access: yesPanoeconomicus, 2020
The sustainability of purchasing power parity (PPP) theory is examined within the quantile autoregression model for the monthly data of the euro and the US dollar-based real exchange rate (RER) in selected European economies (the Czech Republic, Hungary,
Mladenović Zorica, Bodor Slađana
doaj   +1 more source

Intravitreal GD2‐Specific Chimeric Antigen Receptor T‐Cell Therapy for Refractory Retinoblastoma

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Effective treatments for advanced, treatment‐resistant retinoblastoma (RB) remain limited. GD2‐specific chimeric antigen receptor (CAR) T cells show potent antitumor activity with minimal toxicity but have not previously been evaluated in RB.
Subongkoch Subhadhirasakul   +13 more
wiley   +1 more source

Sparse reduced-rank regression for imaging genetics studies: models and applications [PDF]

open access: yes, 2012
We present a novel statistical technique; the sparse reduced rank regression (sRRR) model which is a strategy for multivariate modelling of high-dimensional imaging responses and genetic predictors.
Vounou, Maria, Vounou, Maria
core   +1 more source

Pulmonary Dysfunction Is Associated With Sleep Study Abnormalities in Children With Sickle Cell Disease: A Multicenter Study

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction Pulmonary dysfunction and sleep abnormalities are common in children with sickle cell disease (SCD) and are associated with worse clinical outcomes. Whether spirometry abnormalities are associated with polysomnography (PSG) findings remains unclear.
Ammar Saadoon Alishlash   +4 more
wiley   +1 more source

Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model

open access: yesEnergies, 2023
Uncertainty in wind power is often unacceptably large and can easily affect the proper operation, quality of generation, and economics of the power system.
Jiawei Zhang   +6 more
doaj   +1 more source

Long-range, critical-point dynamics in oil field flow rate data [PDF]

open access: yes, 2006
Earthquake triggering data exhibit long-range spatio-temporal correlations of the power-law form C(l) ∼ l−α and anomalously-slow temporal diffusion of the mean triggering distance of the form: 〈l〉 ∼ tH, with H < 0.5.
Barton   +32 more
core   +1 more source

Therapeutic Apheresis for Intravenous Methylprednisolone‐Refractory Neuromyelitis Optica Spectrum Disorder: Clinical and Radiological Outcomes in a Single‐Center Case Series

open access: yesTherapeutic Apheresis and Dialysis, EarlyView.
ABSTRACT Background Neuromyelitis optica spectrum disorder (NMOSD) is a relapsing autoimmune disease of the central nervous system. High‐dose intravenous methylprednisolone (IVMP) is the standard first‐line therapy for acute attacks, although some patients remain refractory.
Wataru Horiguchi   +5 more
wiley   +1 more source

Creating Powerful and Interpretable Models with Regression Networks

open access: yes, 2021
As the discipline has evolved, research in machine learning has been focused more and more on creating more powerful neural networks, without regard for the interpretability of these networks. Such "black-box models" yield state-of-the-art results, but we cannot understand why they make a particular decision or prediction. Sometimes this is acceptable,
O'Neill, Lachlan   +4 more
openaire   +2 more sources

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