Results 41 to 50 of about 1,274,599 (258)
, 2002 Abstract While this model is important for normally distributed data, it is less useful for other distributions such as the binomial, Poisson and gamma. The context in which such distributions are used often means that we need to model E [Y ] as a non-linear function of Xβ .
Paul Garthwaite +2 more
openaire +4 more sources
Partitioned conditional generalized linear models for categorical data [PDF]
, 2014 In categorical data analysis, several regression models have been proposed for hierarchically-structured response variables, e.g. the nested logit model. But they have been formally defined for only two or three levels in the hierarchy.
Guédon, Yann +2 more
core +4 more sources
Mixtures of g-priors in Generalized Linear Models
, 2018 Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to Generalized Linear Models (GLMs)
Clyde, Merlise A., Li, Yingbo
core +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background B‐cell lymphoblastic lymphoma (B‐LBL) represents a rare variety of non‐Hodgkin lymphoma, with limited research on its biology, progression, and management. Methods A retrospective analysis was performed on the clinical characteristics of 256 patients aged ≤18 years who received treatment under the China Net Childhood Lymphoma (CNCL)‐
Zhijuan Liu +20 more
wiley +1 more source
D-optimal Factorial Designs under Generalized Linear Models [PDF]
, 2013 Generalized linear models (GLMs) have been used widely for modelling the mean response both for discrete and continuous random variables with an emphasis on categorical response.
Mandal, Abhyuday, Yang, Jie
core
Design Issues for Generalized Linear Models: A Review
, 2006 Generalized linear models (GLMs) have been used quite effectively in the modeling of a mean response under nonstandard conditions, where discrete as well as continuous data distributions can be accommodated.
Ghosh, Malay +3 more
core +3 more sources
Sparsifying Generalized Linear Models
Proceedings of the 56th Annual ACM Symposium on Theory of ComputingWe consider the sparsification of sums $F : \mathbb{R}^n \to \mathbb{R}$ where $F(x) = f_1(\langle a_1,x\rangle) + \cdots + f_m(\langle a_m,x\rangle)$ for vectors $a_1,\ldots,a_m \in \mathbb{R}^n$ and functions $f_1,\ldots,f_m : \mathbb{R} \to \mathbb{R}_+$.
Arun Jambulapati +3 more
openaire +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Bone tumours present significant challenges for affected patients, as multimodal therapy often leads to prolonged physical limitations. This is particularly critical during childhood and adolescence, as it can negatively impact physiological development and psychosocial resilience.
Jennifer Queisser +5 more
wiley +1 more source
, 2015 AbstractGeneralized Linear Modeling (GLM) unifies several statistical techniques, providing a stable and modular foundation on which to build a useful working knowledge of statistical modeling. GLMs enable a re-interpretation of previously learned ANOVA and regression techniques and integrate well with more advanced modeling techniques introduced in ...
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Introduction Cognitive impairment and exercise intolerance are common in dialysis patients. Cerebral perfusion and oxygenation play a major role in both cognitive function and exercise execution; HD session per se aggravates cerebral ischemia in this population. This study aimed to compare cerebral oxygenation and perfusion at rest and in mild
Marieta P. Theodorakopoulou +10 more
wiley +1 more source