In this paper, a linear mixed model which has two random effects is broken up into two models. This thesis gets the parameter estimation of the original model and an estimation’s statistical qualities based on these two models. Then many important properties are given by comparing this estimation with other general estimations. At the same time, this paper proves the analysis of variance estima...

Linear mixed models are extremely sensitive to outlying responses and extreme points in the fixed and random effect design spaces. Few diagnostics are available in standard computing packages. We provide routine diagnostic tools, which are computationally inexpensive. The diagnostics are functions of basic building blocks: studentized residuals, error contrast matrix, and the inverse of the res...

A relatively recent development in nonparametric regression is the representation of spline-based smoothers as mixed model fits. In particular, generalized nonparametric regression (e.g. smoothingwith a binary response) corresponds to fitting a generalized linear mixedmodel. Automation, or data-driven smoothing parameter selection, can be achieved via (restricted) maximum likelihood estimation ...

This article deals with the so called GVF (Gradient Vector Flow) introduced by C. Xu, J.L. Prince [14, 15]. We give existence and uniqueness results for the front propagation flow for boundary extraction that was initiated by Paragios, Mellina-Gottardo et Ralmesh [11, 12]. The model combines the geodesic active contour flow and the GVF to determine the geometric flow. The motion equation is con...

The paper explores the interaction between sentence boundaries marked by annotators in transcriptions of Russian spontaneous speech and actual prosodic boundaries in the signal. The aim of the research is to investigate whether annotators’ prosodic competence allows them to correctly detect sentence boundaries in speech based on textual information only. We found that inter-annotator agreement ...

The lme4 package uses sparse matrix technology and clever decompositions of the likelihood to fit linear, generalized, and nonlinear mixed-effects models. The amer package extends lme4’s scope to include generalized additive mixed models (GAMM). This vignette summarizes the main ideas behind additive models and their representation in the form of a mixed model, describes the modifications to lm...

The generalized linear mixed models (GLMMs) for clustered data are studied when covariates aremeasured with error. Themost conventional measurement error models are based on either linear mixed models (LMMs) or GLMMs. Even without the measurement error, the frequentist analysis of LMM, and particularly of GLMM, is computationally difficult. On the other hand, Bayesian analysis of LMM and GLMM i...

The sommer package has been developed to provide R users with a powerful mixed model solver for different genetic and non-genetic analysis in diploid and polyploid organisms. This package allows the user to estimate variance components for a mixed model with the advantage of specifying the variance-covariance structure of the random effects and obtain other parameters such as BLUPs, BLUEs, resi...

A study is presented showing how three state-of-the-art algorithms from the Face Recognition Vendor Test 2006 (FRVT 2006) are effected by factors related to face images and people. The recognition scenario compares highly controlled images to images taken of people as they stand before a camera in settings such as hallways and outdoors in front of buildings. A Generalized Linear Mixed Model (GL...