安道知寛
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安道 知寛（あんどう ともひろ）は、日本の経営学者。メルボルン大学ビジネススクール教授（日本では教授より上位の職階にあたる Distinguished Professor に相当[1]）。
- 最優秀論文賞：学術論文誌 Econometric Reviews より、論文 "Selecting the regularization parameters in high-dimensional panel data models" に授与される.
- Bayesian statistical modeling and model selection, Chapman & Hall/CRC.
- ベイズ統計モデリング. 朝倉書店.
- 高次元データ分析の方法. 朝倉書店.
- Large-scale generalized linear models for longitudinal data with grouped patterns of unobserved heterogeneity. (共著) Journal of Business and Economic Statistics, 2022.
- A spatial panel quantile model with unobserved heterogeneity. (共著) Journal of Econometrics, 2022.
- Modelling tail behaviour in the topology of financial networks. (共著) Management Science, 2022.
- Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity. (共著) Journal of Econometrics, 2022.
- Quantile co-movement in financial markets. (共著) Journal of the American Statistical Association, 2020.
- A weight-relaxed model averaging approach for high-dimensional generalized linear models. (共著) Annals of Statistics, 2017.
- Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures. (共著) Journal of the American Statistical Association, 2016.
- Panel data models with grouped factor structure under unknown group membership. (共著) Journal of Applied Econometrics, 2016.
- A model averaging approach for high-dimensional regression models. (共著) Journal of the American Statistical Association, 2014.
- A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model.(共著) Journal of Econometrics, 2010.
- Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models. Biometrika, 2007.
- Bayesian information criteria and smoothing parameter selection in radial basis function networks.(共著) Biometrika, 2004.
