Novel significance tests are proposed for the quite general additive concurrent model formulation without the need of model, error structure preliminary estimation or the use of tuning parameters. Making use of the martingale difference divergence coefficient, we propose new tests to measure the conditional mean independence in the concurrent model framework taking under consideration all observed time instants. In particular, global dependence tests to quantify the effect of a group of covariates in the response as well as partial ones to apply covariates selection are introduced. Their asymptotic distribution is obtained on each case and a bootstrap algorithm is proposed to compute its p-values in practice. These new procedures are tested by means of simulation studies and some real datasets analysis.

翻译：提议对相当一般性的叠加并存模型的配方进行新意义测试,而不需要模型、误差结构初步估计或使用调试参数。我们利用马丁加尔差异系数,提议进行新测试,以衡量同时同时存在的模型框架中的有条件平均独立性,同时考虑所有所观察到的时间,特别是采用全球依赖性测试,以量化反应中一组共变体的影响以及应用共变选择的局部性测试。每个案例都得到无药可治的分布,并提议采用靴套算法在实际中计算其p价值。这些新程序通过模拟研究和一些真实的数据集分析进行测试。