Results 41 to 50 of about 1,474,584 (283)
, 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
Prediction of Gene Expression Patterns With Generalized Linear Regression Model
Frontiers in Genetics, 2019 Cell reprogramming has played important roles in medical science, such as tissue repair, organ reconstruction, disease treatment, new drug development, and new species breeding.
Shuai Liu +6 more
doaj +1 more source
Solving matrix models using holomorphy [PDF]
, 2003 We investigate the relationship between supersymmetric gauge theories with moduli spaces and matrix models. Particular attention is given to situations where the moduli space gets quantum corrected. These corrections are controlled by holomorphy.
D. Berenstein +41 more
core +3 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
Text Data Analysis Using Generalized Linear Mixed Model and Bayesian Visualization
Axioms, 2022 Many parts of big data, such as web documents, online posts, papers, patents, and articles, are in text form. So, the analysis of text data in the big data domain is an important task.
Sunghae Jun
doaj +1 more source
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. +9 more
core +1 more source
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
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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 ...
+6 more sources
A generalized linear model of dynamics of thin elastic shells
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 A generalized linear model of the dynamics of a thin elastic shell of constant thickness, which takes into account the rotation and compression of the fiber sheath normal to the middle surface, has been proposed.
E.Yu. Mihajlova +2 more
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