Abstract
We synthesize the knowledge present in various scientific disciplines for the development of the 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.
Original language | English |
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Article number | 8580454 |
Pages (from-to) | 12114-12132 |
Number of pages | 19 |
Journal | IEEE Access |
Volume | 7 |
DOIs | |
State | Published - 2019 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Expectation-maximization
- Fourier-based Sieve estimator
- Monte Carlo simulation
- SCAD
- Vox Populi
- latent reference groups
- local covariate shift
- opinion space
- selectivity bias correction
- semiparametric
- wisdom of crowds
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering