Results 101 to 110 of about 2,609,891 (296)
Ordinal regression on degree of behavioral change.
Ordinal regression on degree of behavioral change.
Tai-Yi Yu (5897078) +2 more
core +1 more source
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang +2 more
wiley +1 more source
SAS Macro BDM for Fitting the Dale Regression Model to Bivariate Ordinal Response Data [PDF]
A SAS macro for fitting an extension of the Dale (1986) regression model to bivariate ordinal data is provided. The macro is described in detail and examples from Dale (1986) and McMillan, Hanson, Bedrick, and Lapham (2005) are discussed.
Garnett McMillan, Timothy Hanson
core
Ordinal regression analysis of monitor knowledge variables.
Ordinal regression analysis of monitor knowledge variables.
Alistair Anderson (5892266)
core +1 more source
Ordinal logistic regression for COVID-19 risk (N = 3,106).
Ordinal logistic regression for COVID-19 risk (N = 3,106).
Tse-Chuan Yang (798891) +3 more
core +1 more source
A rare de novo IFT122‐A773E variant is identified in idiopathic pediatric uveitis and shown to exacerbate retinal inflammation and barrier dysfunction. Mechanistically, the variant enhances IFT43 interaction, elevates calcium signaling, and activates the MEK/ERK/FRA1 axis, revealing a previously unrecognized cilia‐associated pathway that may increase ...
Qian Zhou +18 more
wiley +1 more source
Ordinal regression Part 2: Multiple ordinal regression
In this video, Dr Heini Väisänen explores multiple ordinal regression models with more than one exponential variable. She also looks at statistical significance and how it can be determined in such a model.
Väisänen, Heini
core +1 more source
Results of ordinal logistic regression models.
Results of ordinal logistic regression models.
Victoria Simms (25753) +12 more
core +1 more source
Any multiple Criteria Decision Aiding (MCDA) method needs some preference parameters. The Decision Maker (DM) could be asked to provide directly all these parameters; however, because it needs a great cognitive effort, the indirect preference information is more used in practice. Starting from the indirect preference information, usually there could be
Corrente S +3 more
openaire +3 more sources
An integrated proteogenomic analysis of 44,137 predominantly European‐ancestry UK Biobank participants aged 40–69 years identifies 12 robust proteins associated with idiopathic pulmonary arterial hypertension. These proteins define a high‐mortality molecular endotype, support early detection and mortality prediction, reveal sex‐differential proteomic ...
Xinjie Lin +18 more
wiley +1 more source

