A review of the different approaches using the General Linear Model (GLM) to analyse multi-subject fMRI studies is presented. The first part of the paper attempts to expose the approaches with the least amount of statistic knowledge whilst the second part embeds those approaches in the GLM framework necessitating more statistical mathematical awareness, but enabling more advanced applications. ...

The study focuses on how socio-economic and demographic indicators affect fertilization sustainability (excessive amount of fertilization). Principally we aim to examine the significance magnitude of the effects of three socio-demographic variables such as traditional way of fertilization, rental land farming, and farmers’ younger age on over-fertilization in Bangladesh and other developing cou...

In recent years, the class of generalized linear models has gained popularity as a statistical modeling tool. This popularity is due in part to the flexibility of generalized linear models in addressing a variety of statistical problems and to the availability of software to fit the models. The SAS system provides two new tools that fit generalized linear models. The GENMOD procedure in SAS/ST...

Frequentist approaches to making inferences about the variances of random cluster effects in hierarchical generalized linear models (HGLMs) for non-normal variables have several limitations. These include reliance on asymptotic theory, questionable properties of classical likelihood ratio tests when pseudo-likelihood methods are used for estimation, and a failure to account for uncertainty in t...

The crossover design involves observing the same response variate under different experimental conditions for each subject, and as such, is a special case of a repeated measures or multivariate data structure. The general multivariate linear model provides a natural framework for analysis of such data because generalizations to more complex structures are feasible and follow the same rationale....

There is an especially strong need in modern largescale data analysis to prioritize samples for manual inspection. For example, the inspection could target important mislabeled samples or key vulnerabilities exploitable by an adversarial attack. In order to solve the “needle in the haystack" problem of which samples to inspect, we develop a new scalable version of Cook’s distance, a classical s...

We propose a class of double hierarchical generalized linear models in which random effects can be specified for both the mean and dispersion. Heteroscedasticity between clusters can be modelled by introducing random effects in the dispersion model, as is heterogeneity between clusters in the mean model.This class will, among other things, enable models with heavy-tailed distributions to be exp...

Association models for a pair of random elements X and Y (e.g., vectors) are considered which specify the odds ratio function up to an unknown parameter θ. These models are shown to be semiparametric in the sense that they do not restrict the marginal distributions of X and Y . Inference for the odds ratio parameter θ may be obtained from sampling either Y conditionally on X or vice versa. Gene...

Linear and generalised linear models: A student who has covered Chapters 8 and 10.110.4 of Statistical Models by A. C. Davison (Cambridge University Press, 2003) will be more than adequately prepared for the APTS module. For students without access to this book, the main theory is repeated below. The inference methodology described is largely based on classical statistical theory. Although prio...

This paper aims at studying the impact of the colors of e-commerce websites on consumer memorization and buying intention. Based on a literature review we wish to introduce the theoretical and methodological bases addressing this issue. A conceptual model is proposed, showing the effects of the color of the e-commerce website and of its components Hue, Brightness and Saturation, on the behavior...