# Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision

## TL;DR

This paper introduces a semiparametric algorithm that corrects for endogenous truncation bias by leveraging the Vox Populi concept, significantly improving accuracy in self-selected data scenarios through extensive simulations.

## Contribution

The paper develops a novel semiparametric endogenous truncation-proof algorithm that incorporates Vox Populi principles to enhance bias correction in self-selected data.

## Key findings

- High accuracy demonstrated through extensive Monte Carlo simulations
- Algorithm effectively corrects for endogenous self-selection bias
- Model performs well across diverse distribution functions

## Abstract

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.