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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore how cerebral hypoxia and Normal‐Appearing White Matter (NAWM) integrity affect MS lesion burden and clinical course. Methods Seventy‐nine MS patients, including 13 clinically isolated syndrome (CIS) patients and 66 relapsing–remitting multiple sclerosis (RRMS) patients, and 44 healthy controls (HCs) were recruited from ...
Xinli Wang +8 more
wiley +1 more source
Quantifying the Impact of Ocrelizumab on Paramagnetic Rim Lesions in Multiple Sclerosis
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Paramagnetic rim lesions (PRLs) are a subset of chronic active multiple sclerosis (MS) lesions marked by iron‐laden microglia and macrophages. Ocrelizumab, a monoclonal antibody targeting CD20+ B cells, suppresses acute MS activity, but its effect on PRLs remains unclear. In a longitudinal study of 29 ocrelizumab‐treated patients with at least
Kimberly H. Markowitz +8 more
wiley +1 more source
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2013
The correlation coefficient discussed in the last chapter is a component of one of the most important techniques in statistics: linear regression modeling. In this section, we introduce this topic and the subject of statistical modeling, in general.
Alfred DeMaris, Steven H. Selman
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The correlation coefficient discussed in the last chapter is a component of one of the most important techniques in statistics: linear regression modeling. In this section, we introduce this topic and the subject of statistical modeling, in general.
Alfred DeMaris, Steven H. Selman
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Censored Multivariate Linear Regression Model
2022Often, real life problems require modelling several response variables together. This work analyses multivariate linear regression model when the data are censored. Censoring distorts the correlation structure of the underlying variables and increases the bias of the usual estimators.
Rodney Sousa +2 more
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Romanian Statistical Review Supplement, 2015 Sometimes, for estimating parameters using other techniques of estimation, which cannot be incremental transformations, linear estimation of parameters is made by numerical methods. Linear regression model is based on the series of data for the two features.
Constantin Anghelache +4 more
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(Non) linear regression modeling [PDF]
, 2004 We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or predict the dependent variables by means of the model. Such
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Combining Linear Regression Models
Journal of the American Statistical Association, 2005Model-combining (i.e., mixing) methods have been proposed in recent years to deal with uncertainty in model selection. Even though advantages of model combining over model selection have been demonstrated in simulations and data examples, it is still unclear to a large extent when model combining should be preferred.
Yuan, Zheng, Yang, Yuhong
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1993
Although this book deals with nonlinear models, a short chapter on linear regression models may be useful, since by comparison with the linear case one can better understand some features of nonlinear models. Moreover, linear regression models are probably the most popular models in applications.
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Although this book deals with nonlinear models, a short chapter on linear regression models may be useful, since by comparison with the linear case one can better understand some features of nonlinear models. Moreover, linear regression models are probably the most popular models in applications.
openaire +1 more source