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LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the <i>L</i><inf>1</inf> penalty, the optimization should rely on the quadratic program (QP) or general non-linear program which is known to be computational intensive. In this paper, we propose a gradient(More)
MOTIVATION There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Cox's(More)
We consider a Bayesian analysis method of paired survival data using a bivariate exponential model proposed by Moran (1967, Biometrika 54:385-394). Important features of Moran's model include that the marginal distributions are exponential and the range of the correlation coefficient is between 0 and 1. These contrast with the popular exponential model with(More)
With the exponentially growing amount of information available on the Internet, retrieving web pages of interest has become increasingly difficult. While several web page recommender systems have been developed, it is still difficult to search related information which reflects users&#x02019; preference. In this paper, we propose a new type of web page(More)
We propose a new ensemble algorithm called Convex Hull Ensemble Machine (CHEM). CHEM in Hilbert space is first developed and modified for regression and classification problems. We prove that the ensemble model converges to the optimal model in Hilbert space under regularity conditions. Empirical studies reveal that, for classification problems, CHEM has a(More)
A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. In this paper, we present a prediction method for time-to-event traits using genome-wide single-nucleotide polymorphisms (SNPs). We also propose(More)
One of main objectives of a genome-wide association study (GWAS) is to develop a prediction model for a binary clinical outcome using single-nucleotide polymorphisms (SNPs) which can be used for diagnostic and prognostic purposes and for better understanding of the relationship between the disease and SNPs. Penalized support vector machine (SVM) methods(More)