We examined individual differences in masked repetition priming by re-analysing item-level response-time (RT) data from three experiments. Using a linear mixed model (LMM) with subjects and items specified as crossed random factors, the originally reported priming and word-frequency effects were recovered. In the same LMM, we estimated parameters describing the distributions of these effects ac...

Mixed pixels, which are inevitable in remote sensing images, often result in a lot of limitations in their applications. A novel approach for mixed pixel’s fully constrained unmixing, Fully Constrained Oblique Subspace Projection (FCOBSP) Linear Unmixing algorithm, is proposed to handle this problem. The Oblique Subspace Projection, in which the signal space is oblique to the background space, ...

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...

We consider a particular maximum likelihood estimator (MLE) and a computationally-intensive Bayesian method for differentially private estimation of the linear mixed-effects model (LMM) with normal random errors. The LMM is important because it is used in small area estimation and detailed industry tabulations that present significant challenges for confidentiality protection of the underlying ...

The aim of this study is to describe factors that influence the measured intraocular pressure (IOP) change and to develop a predictive model after myopic laser in situ keratomileusis (LASIK) with a femtosecond (FS) laser or a microkeratome (MK). We retrospectively reviewed preoperative, intraoperative, and 12-month postoperative medical records in 2485 eyes of 1309 patients who underwent LASIK ...

This study develops a linear mixed model (LMM) that includes spatial effects between regions with autoregressive (SAR model). Between observations (regions) on LMM are usually assumed to be independent. However, these assumptions not always fulfilled due dependency regions. There two important parts in modeling: dependence and heterogeneity. In this study, we concerned the lag or SAR models bec...

The linear mixed model (LMM) is now routinely used to estimate heritability. Unfortunately, as we demonstrate, LMM estimates of heritability can be inflated when using a standard model. To help reduce this inflation, we used a more general LMM with two random effects-one based on genomic variants and one based on easily measured spatial location as a proxy for environmental effects. We investig...

In this paper, we consider the nonlinear system modelling problem for on-chip testing and diagnosis of embedded mixed-signal systems. A SituationDependent AutoRegressive model with eXogenous variable (SDARX) is introduced to approximate the conventional Nonlinear-ARX (NARX). The parameter search space is divided into a linear weight subspace and the nonlinear parameter subspace. A nonlinear par...

Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches....

With the renewed interest in the field of second language learning for the knowledge of collocating words, research findings in favour of holistic processing of formulaic language could support the idea that these language units facilitate efficient language processing. This study investigated the difference between processing of a first language (L1) and a second language (L2) of congruent col...