Results 61 to 70 of about 2,907,167 (317)

Robust Minimum Divergence Estimation for the Multinomial Circular Logistic Regression Model

Entropy, 2023
Circular data are extremely important in many different contexts of natural and social science, from forestry to sociology, among many others. Since the usual inference procedures based on the maximum likelihood principle are known to be extremely non ...
Elena Castilla, Abhik Ghosh
doaj   +1 more source

Use of Symptomatic Drug Treatment for Fatigue in Multiple Sclerosis and Patterns of Work Loss

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To describe the use of central stimulants and amantadine for fatigue in MS and evaluate a potential association with reduced work loss in people with MS. Methods We conducted a nationwide, matched, register‐based cohort study in Sweden (2006 to 2023) using national registers with prospective data collection.
Simon Englund, Johan Reutfors, Thomas Frisell, Fredrik Piehl   +3 more
wiley   +1 more source

Financial Literacy and Financial Risk Tolerance of Individual Investors: Multinomial Logistic Regression Approach

, 2020
Financial risk tolerance is one of the important factors affecting the financial investment decisions of individuals and institutional investors and a crucial factor of financial planning and financial counseling.
Yılmaz Bayar, H. Sezgin, Ö. Öztürk, Mahmut Unsal Sasmaz   +3 more
semanticscholar   +1 more source

Heterogeneity of Rheumatoid Arthritis–Associated Interstitial Lung Disease by Longitudinal Forced Vital Capacity Trajectory and Associations With Disease Outcomes

Arthritis Care &Research, EarlyView.
Objective We aimed to identify unique disease trajectories within rheumatoid arthritis–associated interstitial lung disease (RA‐ILD) based on longitudinal forced vital capacity (FVC) values and their associated clinical outcomes. Methods We performed a cohort study of RA‐ILD within the Veterans Health Administration from 1999 to 2021.
Bryant R. England, Yangyuna Yang, Punyasha Roul, Tate M. Johnson, Scott M. Matson, Grant W. Cannon, Brian C. Sauer, Jeffrey R. Curtis, Joshua F. Baker, Ted R. Mikuls   +9 more
wiley   +1 more source

Batch effects correction for microbiome data with Dirichlet‐multinomial regression

Bioinform., 2018
Motivation: Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch
Zhenwei Dai, S. Wong, Jun Yu, Yingying Wei   +3 more
semanticscholar   +2 more sources

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

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, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Urban Noise Analysis Using Multinomial Logistic Regression [PDF]

Journal of Transportation Engineering, 2016
AbstractThe research uses a database of urban noise data collected continually from April 2013 to March 2014 at 10 sites in Dublin, Ireland. The first objective of the paper is to investigate if the morning daily noise level peak is related to transport characteristics of households, such as car ownership levels, the mode by which people travel to work,
O'MAHONY, MARGARET, GERAGHTY, THOMAS
openaire   +3 more sources

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Application of Multinomial Logistic Regression to Model the Impact of Rainfall Genesis on the Performance of Storm Overflows: Case Study [PDF]

, 2022
Bartosz Szeląg, Roman Suligowski, Grzegorz Majewski, Adrian Bralewski, Karolina Bralewska, Ewa Anioł, Wioletta Rogula–Kozłowska, Francesco De Paola   +7 more
openalex   +1 more source

Toward Scalable Solutions for Silver‐Based Gas Diffusion Electrode Fabrication for the Electrochemical Conversion of CO2 – A Perspective

Advanced Functional Materials, EarlyView.
In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken, K. Ashoke Raman, Alexander Bauer, Violeta Karyofylli, Eva Jodat, André Karl, Rüdiger‐A. Eichel   +6 more
wiley   +1 more source