Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision
This addresses bias in data affected by endogenous behavior for researchers in statistics and econometrics, though it appears incremental as it refines existing concepts.
The paper tackles truncation bias from endogenous self-selection by developing a semiparametric correction algorithm that uses Vox Populi to let data points sort themselves based on latent reference group opinions. Monte Carlo simulations with 100 million realizations show very high accuracy.
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.