Estimation and testing for multiple regulation of multivariate mixed outcomes.
Estimation and testing for multiple regulation of multivariate mixed outcomes.
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作者：
Agniel D;Liao KP;Cai T
最近，人们对在流行病学和基因组研究中同时研究多种表型产生了极大的兴趣，以捕获复杂疾病的多维性或了解相关疾病的共同病因学。当这些结果可能以非常不同的尺度来衡量或由连续、二元和未完全观察的元素的混合组成时，我们寻求识别与多种结果相关的多个调节因素或预测因素。我们首先提出一种估计技术，将所有影响放在相似的尺度上，并且我们对估计的影响引入稀疏性。我们为该估计器提供了标准渐近结果，并表明重采样可用于量化有限样本中的不确定性。我们最终提供了一个多重测试程序，可以专门针对感兴趣的多个监管机构的类型，并且我们确定，在标准规律性条件下，随着样本量的不同，族错误率将接近 0。仿真结果表明，我们的方法可以在减少估计偏差和提高测试能力方面比非正则方法有所改进。
Considerable interest has recently been focused on studying multiple phenotypes simultaneously in both epidemiological and genomic studies, either to capture the multidimensionality of complex disorders or to understand shared etiology of related disorders. We seek to identify multiple regulators or predictors that are associated with multiple outcomes when these outcomes may be measured on very different scales or composed of a mixture of continuous, binary, and not-fully-observed elements. We first propose an estimation technique to put all effects on similar scales, and we induce sparsity on the estimated effects. We provide standard asymptotic results for this estimator and show that resampling can be used to quantify uncertainty in finite samples. We finally provide a multiple testing procedure which can be geared specifically to the types of multiple regulators of interest, and we establish that, under standard regularity conditions, the familywise error rate will approach 0 as sample size diverges. Simulation results indicate that our approach can improve over unregularized methods both in reducing bias in estimation and improving power for testing.