Yong Li, Sushanta K. Mallick, Tao Zeng, Junxing Zhang
arXiv 1 Jul 2025 · Econometrics
arXiv:2507.00763 · PDF · DOI · OpenAlex · Extracted main text
Optimal data detection in massive multiple-input multiple-output (MIMO) systems often requires prohibitively high computational complexity. A variety of detection algorithms have been proposed in the literature, offering different trade-offs between complexity and detection performance. In recent years, Variational Bayes (VB) has emerged as a widely used method for addressing statistical inference in the context of massive data. This study focuses on misspecified models and examines the risk functions associated with predictive distributions derived from variational posterior distributions. These risk functions, defined as the expectation of the Kullback-Leibler (KL) divergence between the true data-generating density and the variational predictive distributions, provide a framework for assessing predictive performance. We propose two novel information criteria for predictive model comparison based on these risk functions. Under certain regularity conditions, we demonstrate that the proposed information criteria are asymptotically unbiased estimators of their respective risk functions. Through comprehensive numerical simulations and empirical applications in economics and finance, we demonstrate the effectiveness of these information criteria in comparing misspecified models in the context of massive data.
appendix boundary found by appendix_command · 46% 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 | Li, Y., Yu, J., and Zeng, T (2020) Deviance information criterion for latent variable models and misspecified models self | 1.000 | 12 | 3 | 100% |
| 2 | Zhang, Y. and Yang, Y (2024) Bayesian model selection via mean-field variational approximation | 0.935 | 11 | 6 | 82% |
| 3 | Li, Y., Mallick, S. K., Wang, N., Yu, J., and Zeng, T (2024) Deviance Information Criterion for Model Selection:Theoretical Justification and Applications self | 0.888 | 10 | 4 | 70% |
| 4 | Takeuchi, K (1976) Distribution of information statistics and validity criteria of models | 0.874 | 10 | 2 | 100% |
| 5 | Akaike, H (1973) Information Theory and an Extension of the Maximum Likelihood Principle, pages 267–281 | 0.874 | 6 | 2 | 100% |
| 6 | Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and Van Der Linde, A (2002) Bayesian measures of model complexity and fit | 0.874 | 6 | 2 | 100% |
| 7 | Han, W. and Yang, Y (2019) Statistical inference in mean-field variational bayes | 0.843 | 4 | 3 | 75% |
| 8 | Anderson, D. and Burnham, K (2004) Model selection and multi-model inference | 0.737 | 3 | 2 | 100% |
| 9 | Schwarz, G (1978) Estimating the dimension of a model | 0.644 | 2 | 2 | 100% |
| 10 | Wang, Y. and Blei, D (2019) Variational bayes under model misspecification | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 40 scored citations.