Results 211 to 220 of about 332,658 (263)
ABSTRACT Objective Building on our prior Behavioral Risk Factor Surveillance System analysis identifying adults aged 18–39 as the primary driver of the national increase in self‐reported cognitive disability, we examined factors associated with this rise using 2013–2024 U.S. BRFSS data. Methods We analyzed U.S.
Adam de Havenon +9 more
wiley +1 more source
ABSTRACT Objective To evaluate the expression of nine blood RNA biomarkers in a clinical trial based on genes previously identified in an experimental monkey model of stroke for diagnosis feasibility and prognostication. Methods IBIS‐CT1 was a prospective longitudinal study enrolling patients with ischemic stroke (IS) or intracerebral hemorrhage (ICH ...
Salomé Retailleau +11 more
wiley +1 more source
ABSTRACT Objective To investigate which baseline clinical and imaging characteristics best predict TSPO‐PET‐measurable reduction in glial activation following treatment of multiple sclerosis (MS), to utilize this information for designing more efficient biomarker‐based clinical trials targeting glial activation.
Marlene T. Morch +5 more
wiley +1 more source
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Model Checks for Generalized Linear Models
Scandinavian Journal of Statistics, 2002In this paper we propose and study non‐parametric tests for the validity of (composite) Generalized Linear Models with a given parametric link structure, which are based on certain empirical processes marked by the residuals. When properly transformed to their innovation part the resulting test statistics are distribution‐free.
Stute, Winfried, Zhu, Li-Xing
exaly +3 more sources
Use generalized linear models or generalized partially linear models?
Statistics and Computing, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xinmin Li +3 more
openaire +2 more sources
2015
This article provides an introduction into the statistical analysis of neuroimaging data using the general linear model. The analysis allows a flexible use of various models offering a wide range of statistical tests for the analysis of typical neuroimaging experiments. A short introduction to the general linear model is provided using simple examples.
Kiebel, S. +1 more
openaire +2 more sources
This article provides an introduction into the statistical analysis of neuroimaging data using the general linear model. The analysis allows a flexible use of various models offering a wide range of statistical tests for the analysis of typical neuroimaging experiments. A short introduction to the general linear model is provided using simple examples.
Kiebel, S. +1 more
openaire +2 more sources
JACKKNIFING THE GENERAL LINEAR MODEL
Australian Journal of Statistics, 1983SummaryThe aim of this paper is to investigate the problems of estimating a smooth function of the parameters in a general linear model and to clarify some of the points raised by Hinkley (1977) in connection with this problem. An example of the type of problem at hand is that of estimating the maximum (or minimum) mean value in a quadratic regression ...
Weber, N. C., Welsh, A. H.
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Linear models for keystream generators
IEEE Transactions on Computers, 1996Consider a keystream generator (KSG) with \(M\) bits of memory. For dimensional reasons, there exists at least one linear function \(L\) of any \(M+1\) consecutive output bits that is not balanced. Under reasonable hypotheses, \(L\) is independent of time, so \(L\) (essentially an \(| M+1| \times 1\) matrix) is a function of the initial state vector ...
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An Introduction to Generalized Linear Models
2008Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the
Dobson, Annette J, Barnett, Adrian G
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