Results 21 to 30 of about 311,144 (289)

Just Another Gibbs Additive Modeler: Interfacing JAGS and mgcv

Journal of Statistical Software, 2016
The BUGS language offers a very flexible way of specifying complex statistical models for the purposes of Gibbs sampling, while its JAGS variant offers very convenient R integration via the rjags package.
Simon N. Wood
doaj   +1 more source

Oceanographic indicators for the occurrence of anchovy eggs inferred from generalized additive models

Fisheries and Aquatic Sciences, 2020
Three generalized additive models were applied to the distribution of anchovy eggs and oceanographic factors to determine the occurrence of anchovy spawning grounds in Korean waters and to identify the indicators of their occurrence using ...
Jin Yeong Kim, Jae Bong Lee, Young-Sang Suh   +2 more
doaj   +1 more source

Association between air pollution and death from respiratory diseases in Wuhan from 2014 to 2019

Shanghai yufang yixue, 2022
ObjectiveTo determine the association between air pollutants （PM2.5， PM10， SO2， NO2） and death from respiratory diseases in Wuhan.MethodsDaily air pollutants， meteorological data and mortality from respiratory disease between 2014 and 2019 were collected
ZHAO Yuanyuan, YANG Niannian, DAI Juan, ZHANG Xiaoxia, YAN Yaqiong   +4 more
doaj   +1 more source

The Extent of Gender Gap in Citations in Ophthalmology Literature

Frontiers in Medicine, 2022
PurposeTo investigate the severity and causes of gender imbalance in the counts of ophthalmology citations.MethodsThe PubMed database was searched to identify cited papers that were published in four journals (Prog Retin Eye Res, Ophthalmology, JAMA ...
Suqi Cao, Yue Xiong, Wenhua Zhang, Jiawei Zhou, Zhifen He   +4 more
doaj   +1 more source

cgam: An R Package for the Constrained Generalized Additive Model

Journal of Statistical Software, 2019
The cgam package contains routines to fit the generalized additive model where the components may be modeled with shape and smoothness assumptions. The main routine is cgam and nineteen symbolic routines are provided to indicate the relationship between ...
Xiyue Liao, Mary C. Meyer
doaj   +1 more source

Trajectory-based differential expression analysis for single-cell sequencing data [PDF]

, 2020
Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the ...
Cannoodt, Robrecht, Clement, Lieven, Dudoit, Sandrine, Roux de Bézieux, Hector, Saelens, Wouter, Saeys, Yvan, Street, Kelly, Van den Berge, Koen   +7 more
core   +2 more sources

Mapping wind erosion hazard with regression-based machine learning algorithms

Scientific Reports, 2020
Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network ...
Hamid Gholami, Aliakbar Mohammadifar, Dieu Tien Bui, Adrian L. Collins   +3 more
doaj   +1 more source

A Brief Analysis of Key Machine Learning Methods for Predicting Medicare Payments Related to Physical Therapy Practices in the United States

Information, 2021
Background and objectives: Machine learning approaches using random forest have been effectively used to provide decision support in health and medical informatics. This is especially true when predicting variables associated with Medicare reimbursements.
Shrirang A. Kulkarni, Jodh S. Pannu, Andriy V. Koval, Gabriel J. Merrin, Varadraj P. Gurupur, Ayan Nasir, Christian King, Thomas T. H. Wan   +7 more
doaj   +1 more source

Generalized additive modelling with implicit variable selection by likelihood based boosting [PDF]

, 2004
The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters.
Binder, Harald, Tutz, Gerhard
core   +2 more sources

Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models

, 2007
We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model, and whether they enter linearly or flexibly.
Berger J. O., David J Nott, Davidian M., Hastie T., Remy Cottet, Rigby R., Robert J Kohn, Smyth G., Wild C., Yee T.   +9 more
core   +1 more source