quality of products and services is considered as a key factor for customer satisfaction. quality function deployment (qfd) is known as a critical tool for translating voice of customers to prioritize technical requirements of a production. the level of this satisfaction depends on the number of fulfilled requirements. it should be noted that this level varies according to the possible constrai...

We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the genera...

A statistical parameter map of fMRI group analyses relies on the assumptions of the General Linear Model (GLM). The assumptions at the single subject level are that the noise is stationary and unbiased for every voxel of every subject. In the mixed level model, the group level GLM assumes that all subjects are drawn randomly from a population, and that the estimates brought up from the single s...

Abstract: Consider a random vector (X, Y ) and let m(x) = E(Y |X = x). We are interested in testing H0 : m ∈ MΘ,G = {γ(·, θ, g) : θ ∈ Θ, g ∈ G} for some known function γ, some compact set Θ ⊂ IR and some function set G of real valued functions. Specific examples of this general hypothesis include testing for a parametric regression model, a generalized linear model, a partial linear model, a si...

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Generalized linear models with group effects are commonly used in scientific studies. However, there appear to be no results for selecting optimal designs. In this paper, we identify the structure of locally optimal designs, provide a general strategy to determine the design points and the corresponding weights for optimal designs, and present theoretical results for the special case of D-optim...

The adjustment of the binomial data by small constants is a common practice in statistical modelling, for avoiding sparseness issues and, historically, for improving the asymptotic properties of the estimators. However, there are two main disadvantages with such practice: i) there is not a universal constant adjustment that results estimators with optimal asymptotic properties for all possible ...

Functional Magnetic Resonance Imaging (fMRI) is one of the most popular neuroimaging methods for investigating the activity of the human brain during cognitive tasks. As with many other neuroimaging tools, the group analysis of fMRI data often requires a transformation of the individual datasets to a common stereotaxic space, where the different brains have a similar global shape and size. Howe...

Fully nonparametric methods for regression from functional data have poor accuracy from a statistical viewpoint, reflecting the fact that their convergence rates are slower than nonparametric rates for the estimation of high-dimensional functions. This difficulty has led to an emphasis on the so-called functional linear model, which is much more flexible than common linear models in finite dime...