In this paper, the author presents the experiences made by applying the linear mixture modelling technique to an urban area located in the north of San Diego, Ca. Landsat TM satellite imagery was chosen to perform the analysis on. The paper presents the results of linear unmixing applied to raw and first-order corrected imagery. To evaluate the accuracy of the unmixing method, the results of th...

BACKGROUND For the analysis of length-of-stay (LOS) data, which is characteristically right-skewed, a number of statistical estimators have been proposed as alternatives to the traditional ordinary least squares (OLS) regression with log dependent variable. METHODS Using a cohort of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 2008-2009,...

Hyperspectral unmixing decomposes the observed mixed spectra into a collection of constituent pure material signatures and associated fractional abundances. Because universal modeling ability neural networks, deep learning (DL) techniques are gaining prominence in solving hyperspectral analysis tasks. The autoencoder (AE) network has been extensively investigated linear blind source unmixing. H...

paragraph One of the major developments in recent years in the search for missing heritability of human phenotypes is the adoption of linear mixed-effects models (LMMs) to estimate heritability due to genetic variants which are not significantly associated with the phenotype 1. A variant of the LMM approach has been adapted to case-control studies and applied to many major diseases 2–5 , succes...

Restricted Maximum Likelihood (REML) is the most recommended approach for fitting a Linear Mixed Model (LMM) nowadays. Yet, as ML, REML suffers drawback that it performs such by assuming normality both random effects and residual errors, dubious assumption many real data sets. Now, there have been several attempts at trying to justify use of likelihood equations outside Gaussian world, with var...

Linear mixed models (LMMs) are frequently used to analyze longitudinal data. Although these models can be used to evaluate mediation, they do not directly model causal pathways. Structural equation models (SEMs) are an alternative technique that allows explicit modeling of mediation. The goal of this paper is to evaluate the performance of LMMs relative to SEMs in the analysis of mediated longi...

R estimators based on the joint ranks (JR) of all the residuals have been developed over the last twenty years for fitting linear models with independently distributed errors. In this paper, we extend these estimators to estimating the fixed effects in a linear model with cluster correlated continuous error distributions for general score functions. We discuss the asymptotic theory of the estim...

Genetic association studies in admixed populations are under-represented in the genomics literature, with a key concern for researchers being the adequate control of spurious associations due to population structure. Linear mixed models (LMMs) are well suited for genome-wide association studies (GWAS) because they account for both population stratification and cryptic relatedness and achieve in...

Linear mixed-effect models (LMMs) are being increasingly widely used in psychology to analyse multi-level research designs. This feature allows LMMs to address some of the problems identified by Speelman and McGann (2013) about the use of mean data, because they do not average across individual responses. However, recent guidelines for using LMM to analyse skewed reaction time (RT) data collect...

We present the hglm package for fitting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.