[PDF] Top 20 Statistical Methods of SNP Data Analysis and Applications

... the analysis only when AH and HC were ...both SNP information and non-genetic risk factors were taken into account and SGB was ...from data increased the error by ...influential data were ... See full document

... Pseudo-likelihood methods (Gong and Samaniego, 1981; Parke, 1986) and composite marginal likelihood methods (Cox and Reid, 2004; Varin et ...pseudo-likelihood methods can be viewed as an extension of ... See full document

... for this, Malfait and Ramsay (2003) propose a model for a functional response that uses only the history of one functional predictor. In their model, they represent the coefficient function using a triangular basis ... See full document

... Development of tests for the equality of two population means in high-dimensional settings has received much attention recently; see, e.g., Bai and Saranadasa, (1996), Srivastava, (2009), S- rivastava, (2009), Chen and ... See full document

... Here we assess the concurrent effects of inflammation, diet, and antibiotic use on the the gut micro- biome in pediatric Crohn’s disease. The environmental stresses experienced by Crohn’s disease patients were associated ... See full document

... the data that is generated by a computer simulator and for designing computer ...First, analysis can indicate which input factors have large effects (and which small effects) on the ...cases, ... See full document

... the methods that have been developed in recent ...developed methods were identi fi ed in our review (N=11), we noticed that none of the recently developed methods have been applied by other ... See full document

... on data from climate models or impact ...large data sets or an ensemble of these data sets, the use of statistical methods is ...the methods, which have been used or developed by ... See full document

... variate P-splines (Eilers and Marx, 2003) and thin plate regression splines (Wood, 2003) are among the popular methods for smoothing the sample covariance function. For example, the fpca.sc func- tion in the R ... See full document

... tests data, most of them suffer ...in statistical inference process and statistical methods supporting, therefore un- scientific judgments have been made on cigarette product quality judgment ... See full document

... lated data being the second most powerful of the six non parametric statistical methods presented here for the analysis of paired or matched sample ...the data being analyzed including ... See full document

... Directly modeling a tensor image predictor takes into account spatial correlations among the voxels, and is intuitively superior than the one-voxel-at-a-time modeling solution that ignores such correlations. This ... See full document

... spatial analysis in annual crop experiments are well ...spatial analysis to ...of methods used were: (1) univariate spatial models for individual annual measures on each trial; (2) longitudinal ... See full document

... MRI data analysis. After com- pleting the data collection using the fMRI, first step of analysis is to apply the preprocessing stages to the data to prepare them for different ... See full document

... are methods for obtaining approximate standard errors from EM algorithms (Louis, 1982; McLachlan and Krishnan, 2008; Meng and Rubin, 1991), they may require extensive derivation or numerical di ff erentiation ... See full document

... of data. Over the last two decades, high-throughput data analysis has been the focal research area for bioinformatics and ...with data from different high-throughput ...diverse data ... See full document

... To study the biases introduced by the different numerical approximations, we computed the AFS in seven different demographic scenarios with varying numbers of populations and intensities of gene flow (models 1–7). We used ... See full document

... In this chapter, we consider the situation where multiple functional variables are ob- served, but only several of them are relevant in predicting the scalar response. Moreover, those relevant functional predictors enter ... See full document

... Accurate regression coefficient estimation in survival analysis is crucial for studying factors that affect disease progression. However in some survival studies the outcome of interest may be subject to either ... See full document

... The flexibility of Bayesian designs can result in additional complexity. Whilst a Bayesian simulation approach to sample size calculation allows for variability to be built into multiple aspects of a trial design, it ... See full document