Ruben Loaiza-Maya, Didier Nibbering, Dan Zhu
arXiv 26 Jun 2023 · Econometrics · publishedJournal of Econometrics (2024) · 1 citations (OpenAlex)
arXiv:2306.14445 · PDF · DOI · OpenAlex · Extracted main text
The exact estimation of latent variable models with big data is known to be challenging. The latents have to be integrated out numerically, and the dimension of the latent variables increases with the sample size. This paper develops a novel approximate Bayesian method based on the Langevin diffusion process. The method employs the Fisher identity to integrate out the latent variables, which makes it accurate and computationally feasible when applied to big data. In contrast to other approximate estimation methods, it does not require the choice of a parametric distribution for the unknowns, which often leads to inaccuracies. In an empirical discrete choice example with a million observations, the proposed method accurately estimates the posterior choice probabilities using only 2% of the computation time of exact MCMC.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hodgkinson, L., Salomone, R., and Roosta, F (2021) Implicit Langevin algorithms for sampling from log-concave densities | 0.928 | 4 | 3 | 100% |
| 2 | Durmus, A. and Moulines, E (2017) Nonasymptotic convergence analysis for the unadjusted Langevin algorithm | 0.811 | 4 | 2 | 100% |
| 3 | Loaiza-Maya, R. and Nibbering, D (2023) Fast variational Bayes methods for multinomial probit models self | 0.811 | 4 | 2 | 100% |
| 4 | Roberts, G. O. and Tweedie, R. L (1996) Exponential convergence of Langevin distributions and their discrete approximations | 0.737 | 3 | 2 | 100% |
| 5 | Dalalyan, A. S (2017) Theoretical guarantees for approximate sampling from smooth and log-concave densities | 0.644 | 2 | 2 | 100% |
| 6 | De Bortoli, V., Durmus, A., Pereyra, M., and Vidal, A. F (2021) Efficient stochastic optimisation by unadjusted Langevin Monte Carlo: Application to maximum marginal likelihood and empirical B… | 0.644 | 2 | 2 | 100% |
| 7 | Loaiza-Maya, R., Smith, M. S., Nott, D. J., and Danaher, P. J (2022) Fast and accurate variational inference for models with many latent variables self | 0.644 | 2 | 2 | 100% |
| 8 | Poyiadjis, G., Doucet, A., and Singh, S. S (2011) Particle approximations of the score and observed information matrix in state space models with application to parameter estimat… | 0.644 | 2 | 2 | 100% |
| 9 | Vollmer, S. J., Zygalakis, K. C., and Teh, Y. W (2016) Exploration of the (non-) asymptotic bias and variance of stochastic gradient Langevin dynamics | 0.644 | 2 | 2 | 100% |
| 10 | Loaiza-Maya, R. and Nibbering, D (2022) Scalable Bayesian estimation in the multinomial probit model self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 19 scored citations.