Results 221 to 230 of about 2,678,398 (281)

Correction: A Two-way Crossed Effects Fuzzy Panel Linear Regression Model

International Journal of Computational Intelligence Systems
Gholamreza Hesamian, Arne Johannssen
doaj   +1 more source

Diffusion Spectrum Imaging Maps Early Axonal Loss and a Unique Progressive Signal in Neuronal Intranuclear Inclusion Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To delineate specific in vivo white matter pathology in neuronal intranuclear inclusion disease (NIID) using diffusion spectrum imaging (DSI) and define its clinical relevance. Methods DSI was performed on 42 NIID patients and 38 matched controls.
Kaiyan Jiang, Yixiu Pei, Xiaobao Hu, Ying Xiong, Fang Wang, Yanyan Yu, Min Zhu, Dandan Tan, Meihong Zhou, Daojun Hong, Fuqing Zhou   +10 more
wiley   +1 more source

Impact of Asymptomatic Intracranial Hemorrhage on Outcome After Endovascular Stroke Treatment

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Endovascular treatment (EVT) achieves high rates of recanalization in acute large‐vessel occlusion (LVO) stroke, but functional recovery remains heterogeneous. While symptomatic intracranial hemorrhage (sICH) has been well studied, the prognostic impact of asymptomatic intracranial hemorrhage (aICH) after EVT is less certain ...
Shihai Yang, Chengsong Yue, Zhuang Li, Xiaolei Shi, Haoxuan Zhu, Jie Yang, Linyu Li, Jiaxing Song, Shitao Fan, Gaoming Li, Wenzhe Sun, Zhixian Ye, Jiacheng Huang, Dahong Yang, Jinfu Ma, Xu Xu, Zhixi Wang, Lilan Wang, Guojian Liu, Fengli Li, Changwei Guo, Li Qi, Wenjie Zi   +22 more
wiley   +1 more source

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Linear Regression Models

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
openaire   +1 more source

Censored Multivariate Linear Regression Model

2022
Often, 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, Isabel Pereira, Maria Eduarda Silva   +2 more
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Linear Regression Model [PDF]

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, Radu Titus Marinescu, Emanuela Ionescu, Ligia Prodan, Alexandru Ursache   +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, 2005
Model-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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Linear regression models

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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