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A Review on Linear Regression Comprehensive in Machine Learning
, 2020 Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors.
Dastan Maulud, A. Abdulazeez
semanticscholar +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Children with acute lymphoblastic leukemia (ALL) are at risk of severe outcomes from SARS‐CoV‐2 (SCV2). In the post‐pandemic context, where most children have been infected with SCV2, there are limited data on whether vaccination remains beneficial in children with ALL.
Janna R. Shapiro +11 more
wiley +1 more source
Comparitive Study Between Two Ways Least Squares and Adaptive Regression In Regression linear model [PDF]
المجلة العراقية للعلوم الاحصائية, 2007 In this research, we use the ( ) and ( ) two criterions to compare between the estimators of Adaptive Regression model and the estimators of Ordinary and General Least Squares. We found that, Adaptive Regression estimators are more accurate and efficient
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Wilms tumor (WT) treatment imposes a significant time burden on patients and their families. Time toxicity is a patient‐centered metric that quantifies the burden of healthcare interaction. We sought to define time toxicity in the first year after diagnosis of WT and hypothesized that it would increase as tumor stage and treatment ...
Caleb Q. Ashbrook +6 more
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MATHEMATICAL MODELING IN THE CONTENT OF STUDENTS-ECOLOGISTS’ TRAINING OF MATHEMATICS
Статистика и экономика, 2018 According to modern studies in the field of didactics, the content of education, such as mathemati-cal, should be represented by two components of knowledge: information and methodological.The purpose of current research is to identify the potential of ...
S. I. Toropova
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Connecting model-based and model-free approaches to linear least squares regression
, 2020 In a regression setting with response vector $\mathbf{y} \in \mathbb{R}^n$ and given regressor vectors $\mathbf{x}_1,\ldots,\mathbf{x}_p \in \mathbb{R}^n$, a typical question is to what extent $\mathbf{y}$ is related to these regressor vectors ...
Davies, Laurie, Duembgen, Lutz
core
Local Adaptive Grouped Regularization and its Oracle Properties for Varying Coefficient Regression [PDF]
, 2014 Varying coefficient regression is a flexible technique for modeling data where the coefficients are functions of some effect-modifying parameter, often time or location in a certain domain.
Brooks, Wesley, Lu, Zudi, Zhu, Jun
core
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Pediatric sarcomas are a heterogeneous group of tumors that contribute disproportionately to cancer mortality in children. Although congenital anomalies are among the strongest known risk factors for childhood cancer, the risk of specific sarcoma subtypes among affected individuals has not yet been thoroughly evaluated. Procedure We
Russ Wolters +17 more
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Identification of Influential Points in a Linear Regression Model [PDF]
Statistika: Statistics and Economy Journal, 2011 The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independent)datasets.
Jan Grosz
doaj
Comparison Study Between Two Ways Least Squares and Adaptive Regression In Regression linear model [PDF]
المجلة العراقية للعلوم الاحصائية, 2007 In this research, we use on ( ) and ( ) two criterions to compare between the estimators of Adaptive Regression model and the estimators of Ordinary and General Least Squares, we found that, Adaptive Regression estimator is the best by comparing with ...
doaj +1 more source