Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of...

Development of SAS ® linear models procedures over the past several years has led to a number of easily accessible methodological statistical advances for experimental data analysis. The original linear models program, GLM, was a fixed model procedure for analysis of normally distributed data with homogeneous variances. The GENMOD procedure extended the fixed linear model analysis to a number o...

The general linear model (GLM) has been used to analyze simultaneous EEG-fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of indepe...

Over the last 20 to 30 years, there has been a significant amount of tools and statistical methods that have been proposed for analyzing crash data. Yet, the Poisson-gamma (PG) is still the most commonly used and widely acceptable model. This paper documents the application of the Poisson-Weibull (PW) generalized linear model (GLM) for modeling motor vehicle crashes. The objectives of this stud...

Several experiments were conducted in order to investigate the usefulness of mixture of experts approach to an online internet system assisting in real estate appraisal. All experiments were performed using real-world datasets taken from a cadastral system. The analysis of the results was performed using statistical methodology including nonparametric tests followed by post-hoc procedures desig...

Generalized linear models (GLMs) have been used quite effectively in the modeling of a mean response under nonstandard conditions, where discrete as well as continuous data distributions can be accommodated. The choice of design for a GLM is a very important task in the development and building of an adequate model. However, one major problem that handicaps the construction of a GLM design is i...

Synopsis In order to reduce the type I and II errors and obtain more accurate brain activation map, fMR images were statistically analyzed with uncorrected ttest, false discovery rate(FDR) and family-wise error rate(FWE) in the general linear model (GLM) through statistic parameter mapping software(SPM). This study compared and evaluated the power of thresholding correction methods with uncorre...

Let e be an arbitrary even nilpotent element in the general linear Lie superalgebra glM|N and let We associated finite W-superalgebra. Ym|n super Yangian to glm|n. A subalgebra of Ym|n, called shifted denoted by Ym|n(σ), is defined studied. Moreover, explicit isomorphism between a quotient Ym|n(σ) established.

The impact of using motion estimates as covariates of no interest was examined in general linear modeling (GLM) of both block design and rapid event-related functional magnetic resonance imaging (fMRI) data. The purpose of motion correction is to identify and eliminate artifacts caused by task-correlated motion while maximizing sensitivity to true activations. To optimize this process, a combin...