共查询到20条相似文献,搜索用时 0 毫秒
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Alternative confidence regions for canonical variate analysis 总被引:2,自引:0,他引:2
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All subsets regression in a proportional hazards model 总被引:2,自引:0,他引:2
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Pyrolysis mass spectrometry (PyMS) is a rapid, simple, high-resolution analytical method based on thermal degradation of complex
material in a vacuum, and has been widely applied to the discrimination of closely related microbial strains. Minimally prepared
samples of embryogenic and non-embryogenic calluses derived from various higher plants (sweet potato, morning glory, Korean
ginseng, Siberian ginseng, and balloon flower) were subjected to PyMS for spectral fingerprinting. A dendrogram based on the
unweighted pair group method, with arithmetic mean of pyrolysis mass spectra, divided the calluses into Siberian ginseng embryogenic
callus and the others, which were subsequently divided into embryogenic and non-embryogenic callus groups, regardless of plant
species from which the calluses were derived. In the non-embryogenic callus group, the dendrogram was in agreement with the
known taxonomy of the plants. These results indicate that PyMS analysis could be applied for discriminating plant calluses
based on embryogenic capacity and taxonomic classification. 相似文献
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Modified AIC and Cp in multivariate linear regression 总被引:2,自引:0,他引:2
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Summary . Analysis of multiple traits can provide additional information beyond analysis of a single trait, allowing better understanding of the underlying genetic mechanism of a common disease. To accommodate multiple traits in familial correlation analysis adjusting for confounders, we develop a regression model for canonical correlation parameters and propose joint modeling along with mean and scale parameters. The proposed method is more powerful than the regression method modeling pairwise correlations because it captures familial aggregation manifested in multiple traits through maximum canonical correlation. 相似文献
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Canonical analysis of several sets of variables 总被引:4,自引:0,他引:4
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Canonical correlation analysis (CCA) describes the associations between two sets of variables by maximizing the correlation between linear combinations of the variables in each dataset. However, in high‐dimensional settings where the number of variables exceeds the sample size or when the variables are highly correlated, traditional CCA is no longer appropriate. This paper proposes a method for sparse CCA. Sparse estimation produces linear combinations of only a subset of variables from each dataset, thereby increasing the interpretability of the canonical variates. We consider the CCA problem from a predictive point of view and recast it into a regression framework. By combining an alternating regression approach together with a lasso penalty, we induce sparsity in the canonical vectors. We compare the performance with other sparse CCA techniques in different simulation settings and illustrate its usefulness on a genomic dataset. 相似文献
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Using a quantitative genetic model, this paper compares four different methods for estimating genetic variance components. Given various genetic parameters, data were generated and estimates computed. The number of negative estimates, the sample mean, the sample variance, and the sample mean squared error were computed for each method. It is shown that, if the genetic values are not very small, the traditional MATHER -JINKS method is at least as good as any other method. The ML method might be preferable only if the genetic values are very small and the number of loci large. 相似文献
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