基于Vox Populi参与决策的内生截断偏差的半参数修正
Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision
摘要：
我们综合了各种科学学科中存在的知识，用于开发半参数内源性截断证明算法，校正由于内源性自我选择引起的截断偏差。这种综合丰富了算法的准确性、效率和适用性。改进协变量转移假设，数据在本质上受到影响，并在很大程度上由其自身的行为（认知）产生。改进Vox Populi（群体智慧）的概念允许数据点根据其估计的潜在参考群体意见空间进行分类。蒙特卡罗模拟，基于200万个不同的分布函数，实际上产生1亿个实现，证明了我们的模型的非常高的准确性。
英文摘要：
We synthesize the knowledge present in various scientific disciplines for the development of semiparametric endogenous truncation-proof algorithm, correcting for truncation bias due to endogenous self-selection. This synthesis enriches the algorithm's accuracy, efficiency and applicability. Improving upon the covariate shift assumption, data are intrinsically affected and largely generated by their own behavior (cognition). Refining the concept of Vox Populi (Wisdom of Crowd) allows data points to sort themselves out depending on their estimated latent reference group opinion space. Monte Carlo simulations, based on 2,000,000 different distribution functions, practically generating 100 million realizations, attest to a very high accuracy of our model.
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