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Motivated by analysis of gene expression data measured over different tissues or over time, we consider matrix-valued random variable and matrix-normal distribution, where the precision matrices have a graphical interpretation for genes and tissues, respectively. We present a l(1) penalized likelihood method and an efficient coordinate descent-based(More)
Genetical genomics experiments have now been routinely conducted to measure both the genetic markers and gene expression data on the same subjects. The gene expression levels are often treated as quantitative traits and are subject to standard genetic analysis in order to identify the gene expression quantitative loci (eQTL). However, the genetic(More)
For a prediction problem of a given target feature in a large causal network under external interventions, we propose in this paper two partial orientation and local structural learning (POLSL) approaches, Local-Graph and PCD-by-PCD (where PCD denotes Parents, Children and some Descendants). The POLSL approaches are used to discover the local structure of(More)
There has been considerable attention on estimation of conditional variance function in the literature. We propose here a nonparametric model for conditional covariance matrix. A kernel estimator is developed accordingly, its asymptotic bias and variance are derived, and its asymptotic normality is established. A real data example is used to illustrate the(More)
Motivated by the analysis of genetical genomic data, we consider the problem of estimating high-dimensional sparse precision matrix adjusting for possibly a large number of covariates, where the covariates can affect the mean value of the random vector. We develop a two-stage estimation procedure to first identify the relevant covariates that affect the(More)
When we explore the causal relationship among time series variables, we first remove the potential seasonal term then we deal with the problem in the feature selection framework. For a time series with seasonal term, we use several sequences of sin(t) and cos(t) functions with different frequencies to design a ’pseudo’ design matrix, and the seasonal term(More)
Multi-way tensor data have become prevalent in many scientific areas such as genomics and biomedical imaging. We consider a K-way tensor-normal distribution, where the precision matrix for each way has a graphical interpretation. We develop an l1 penalized maximum likelihood estimation and an efficient coordinate descent-based algorithm for model selection(More)
OBJECTIVE To investigate the operative techniques and clinical results of the superficial peroneal neurofasciocutaneous flap based on the distal perforating branch of peroneal artery in repairing donor site defect of forefoot. METHODS From March 2005 to October 2007, 15 patients (11 males and 4 females, aged 20-45 years with an average of 33.6 years) with(More)