Three different stages of pig antral follicles have been studied in a granulosa-cell transcriptome analysis on nylon microarrays (1152 clones). The data have been generated from seven RNA follicle pools and several technical replicates were made. The objective of this paper was to state the feasibility of a transcriptomic protocol for the study of folliculogenesis in the pig. A statistical anal...

The scalability of statistical estimators is of increasing importance in modern applications. One approach to implementing scalable algorithms is to compress data into a low dimensional latent space using dimension reduction methods. In this paper we develop an approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational...

Linear mixed models (LMMs) are a powerful and established tool for studying genotype-phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and loss in power. To mitigate this problem, it is common practice to pre-process the phenotypic val...

It has been observed that heavy meromyosin (HMM) propels actin filaments to higher velocities than native myosin in the in vitro motility assay, yet the reason for this difference has remained unexplained. Since the major difference between these two proteins is the presence of the tail in native myosin, we tested the hypothesis that unknown interactions between actin and the tail (LMM) slow mo...

Statistical models that include random effects are commonly used to analyze longitudinal and correlated data, often with strong and parametric assumptions about the random effects distribution. There is marked disagreement in the literature as to whether such parametric assumptions are important or innocuous. In the context of generalized linear mixed models used to analyze clustered or longitu...

A complex trait like crop yield is determined by its component traits. Multivariable conditional analysis in a general mixed linear model is helpful in dissecting the gene expression for the complex trait due to different effects, such as environment, genotype, and genotype× environment interaction. A recursive approach is presented for constructing a new randomvector that can be equivalently u...

Rank-Based methods for iid linear models have been developed over the past 30 years. However, little work has been done in the area of mixed models. In this paper, we discuss a transformation approach to modeling a particular mixed model: one with an arbitrary number of fixed effects and covariates but only one random effect. Discussion of the asymptotic theory is given and the results of a sim...

Small area estimation techniques are employed when sample data are insufficient for acceptably precise direct estimation in domains of interest. These techniques typically rely on regression models that use both covariates and random effects to explain variation between domains. However, such models also depend on strong distributional assumptions, require a formal specification of the random p...

The research investigated development of voice onset time (VOT) contrasts in children who spoke Jordanian Arabic. factors were: (1) the age at which VOT contrast is acquired; and (2) role place articulation emphasis on development. One hundred twenty (60 males, 60 females; range 2;0–7;11) produced word-initial plosives. Linear Mixed Model (LMM), Bonferroni post hoc t test analyses were conducte...

Large-scale biobanks are being collected around the world in efforts to better understand human health and risk factors for disease. They often survey hundreds of thousands of individuals, combining questionnaires with clinical, genetic, demographic, and imaging assessments; some of this data may be collected longitudinally. Genetic associations analysis of such datasets requires methods to pro...