Experimental neuroscience increasingly requires tractable models for analyzing and predicting the behavior of neurons and networks. The generalized linear model (GLM) is an increasingly popular statistical framework for analyzing neural data that is flexible, exhibits rich dynamic behavior and is computationally tractable (Paninski, 2004; Pillow et al., 2008; Truccolo et al., 2005). What follow...

Estimators of the reliability function in a GLM (generalized life model) are considered. The class of the GLM includes (among others) the Weibull, Pareto, Beta, Gompertz, and Rayleigh distribution. A proper general prior density and the predictive function for general class of distribution proposed by Al-Hussaini [1] are used to obtain the exact estimate. Also, the Bayes estimates relative to s...

—The glass eel is fished in the Gironde Basin, France, with large push nets, scoop nets, and the recently introduced small push net. This study uses fishery data to generate fisheries and abundance indicators for glass eels. Total catch, total effort, and catch per unit effort (CPUE) were calculated for the period 1978–1999 by classical statistical methods and by general linear models (GLM). Us...

We describe a Bayesian learning algorithm for Robust General Linear Models (RGLMs). The noise is modeled as a Mixture of Gaussians rather than the usual single Gaussian. This allows different data points to be associated with different noise levels and effectively provides a robust estimation of regression coefficients. A variational inference framework is used to prevent overfitting and provid...

The primary goal was to determine whether the achievement of finalist positions in the Junior Championship was associated with the achievement of success in the International Swimming Federation (FINA) World Championship (WC). Secondary goals included analyzing the effect of various factors (gender, age, country, etc) on swimmers' performances. Data were obtained from FINA information about the...

OBJECTIVE The mapping of haemodynamic changes related to interictal epileptic discharges (IED) in simultaneous electroencephalography (EEG) and functional MRI (fMRI) studies is usually carried out by means of EEG-correlated fMRI analyses where the EEG information specifies the model to test on the fMRI signal. The sensitivity and specificity critically depend on the accuracy of EEG detection an...

PMC13 ESTIMATING COST-OF-ILLNESS USING GENERALIZED LINEAR MODELS: AN ALTERNATIVETOTHE SMEARING APPROACH Exuzides A, Colby C, Spalding JR ICON Clinical Research, San Francisco, CA, USA, Astellas Pharma US, Deerfield, IL, USA OBJECTIVES: Estimation of cost-of-illness typically involves the analysis of skewed medical costs that include large outliers. Log transformations are frequently used to ove...

The aim of this paper was to estimate the genetic relationships among calving ease (CE), calf survival (CS), and gestation length (GL) to assess the possibility of including this information in beef cattle breeding programs. A total of 35,395 field records were available for CE, 30,684 for GL, and 36,132 for CS from the Asturiana de los Valles beef cattle breed. The 3 traits were analyzed as tr...

In this paper we propose a novel phrase break prediction model for Mandarin speech synthesis. It is generalized linear models (GLM) with stepwise regression solution. We assume phrase break obeys Bernoulli distribution and then model phrase break probability by Logistic GLM. The attribute set is automatically selected by stepwise regression, which is a totally data-driven method. We also introd...

Phenomenal growth in computational power from 1970 through 2010 enabled a parallel expansion in linear model methodology. From humble beginnings in agriculture, linear model applications are now essential in sciences of genetics, education, and biostatistics, to name a few. Indeed, the meaning of "linear models" has evolved accordingly. Developers at SAS Institute have been in the forefront of ...