Results 291 to 300 of about 840,245 (337)

Predicting neurodevelopmental outcomes in Australian First Nations infants: The transdiagnostic utility of early screening tools

Developmental Medicine &Child Neurology, EarlyView.
A culturally adapted developmental screening program (LEAP‐CP) improved First Nations families' engagement with neonatal follow‐up by almost 40%. The Motor Optimality Score‐Revised (MOS‐R), Hammersmith Infant Neurological Examination (HINE), and the Australian First Nations adaptation of the Ages and Stages Questionnaire (ASQ‐TRAK), distinguished ...
Carly Luke, Katherine A. Benfer, Leeann Mick‐Ramsamy, Robert S. Ware, Margot Bosanquet, Natasha Reid, Arend F. Bos, Roslyn N. Boyd, Queensland State‐wide LEAP Clinical Group, Shaneen Leishman, Anya Gordon, Hailey Williams, Chloe Taifalos, Maria Smith, Donna Dewis, Ellena Oakes, Lynda McNamara, Megan Kentish   +17 more
wiley   +1 more source

Multinomial logistic regressions predicting class membership in the monthly events panel study. Reported coefficients are average marginal effects (AMEs), representing the average change in the predicted probability of each outcome category associated with a one-unit change in a given predictor, holding all other variables constant.

Htay-Wah Saw (11027552), Arie Kapteyn (7239449)   +1 more
openalex   +1 more source

Early‐Life Secondhand Smoke Exposure and Development of Atopic Dermatitis up to Adulthood

Clinical &Experimental Allergy, EarlyView.
Anna Zettergren, Niklas Andersson, Anne‐Sophie Merritt, Inger Kull, Petter Ljungman, Erik Melén, Göran Pershagen, Susanne Lundin, Emma K. Johansson, Natalia Ballardini, Sandra Ekström, Anna Bergström   +11 more
wiley   +1 more source

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Multinomial and ordinal logistic regression

PM&R, 2021
Standard logistic regression handles binary outcomes such as disease/no disease. Researchers sometimes collapse outcomes with more than two groups into binary variables to enable use of this familiar model. However, this practice risks losing information
Kristin L. Sainani
openaire   +3 more sources

Multinomial Principal Component Logistic Regression on Shape Data

Journal of Classification, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Meisam Moghimbeygi, Anahita Nodehi
openaire   +4 more sources

Simultaneous Dimension Reduction and Variable Selection for Multinomial Logistic Regression

INFORMS journal on computing, 2023
Multinomial logistic regression is a useful model for predicting the probabilities of multiclass outcomes. Because of the complexity and high dimensionality of some data, it is challenging to fit a valid model with high accuracy and interpretability.
Canhong Wen, Zhenduo Li, Ruipeng Dong, Yijin Ni, Wenliang Pan   +4 more
semanticscholar   +1 more source

Vehicle collisions analysis on highways based on multi-user driving simulator and multinomial logistic regression model on US highways in Michigan

International Journal of Crashworthiness, 2022
Traffic collision on the highway has become a serious issue because they delay the sustainable development of society. Highway accidents on I-69 have one of the highest crash rates in the U.S. in Michigan and a higher fatality rate than average.
Abdulla I. M. Almadi, Rabia Emhamed Al Mamlook, Irfan Ullah, Odey Alshboul, N. Bandara, Ali Shehadeh   +5 more
semanticscholar   +1 more source

Hyperspectral Image Classification Powered by Khatri-Rao Decomposition based Multinomial Logistic Regression

IEEE Transactions on Geoscience and Remote Sensing, 2022
Multinomial logistic regression (MLR) is of great significance in hyperspectral image (HSI) classification within remote sensing community. It seeks the optimal regressors with logistic loss function. In the past, spatial information has been widely used
Xiaotao Wang
semanticscholar   +1 more source

Kronecker Factorization-Based Multinomial Logistic Regression for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters, 2022
Multinomial logistic regression (MLR) has become prevailing for supervised learning within hyperspectral images (HSIs) community. It seeks the optimal regressors with the given training data.
Xiaotao Wang
semanticscholar   +1 more source