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Posts by Collection
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publications
Modified Likelihood Root in High Dimensions
Published in JRSS-B, 2020
This paper is the behavour of the modified likelihood root in high dimensions. A related piece of work on the saddlepoint approximation and laplace approximation in high-dimension is currently being written.
Recommended citation: Tang and Reid. (2020). " Modified Likelihood Root in High Dimensions." Journal of the Royal Statistical Society Series B: Statistical Methodology 82(5), 1349-1369 https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/rssb.12389
General Behaviour of P-values under the Null and Alternative
Published in ArXiv, 2020
This paper studes the behaviour of the p-values distribtuion under general assumptions.
Recommended citation: Tang, Craiu and Sun. "General Behaviour of P-values under the Null and Alternative." ArXiv. https://arxiv.org/abs/2012.01697
Laplace and Saddlepoint Approximations in High Dimensions
Published in ArXiv, 2021
This paper examines the behaviour of the Laplace and saddlepoint approximations in the high-dimensional setting, where the dimension of the model is allowed to increase with the number of observations.
Recommended citation: Tang and Reid (2021). "Laplace and Saddlepoint Approximations in High Dimensions.". ArXiv. [https://arxiv.org/abs/2107.10885]
Types of Parental Psychological Control and Rural and Urban Chinese Adolescents’ Psychological Well-Being and Academic Functioning
Published in Child Development, 2022
This paper studies the effect of parental psychological control on adolescent among 642 urban and rural Chinese adolescents.
Recommended citation: Fang, Q., Liu, C., Tang, Y., Shi, Z., Wang, Q., and Helwig, C. C. (2022). Types of parental psychological control and rural and urban Chinese adolescents’ psychological well‐being and academic functioning. Child Development, 93(2), 484-501. https://srcd.onlinelibrary.wiley.com/doi/full/10.1111/cdev.13699
Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference
Published in JASA, 2022
This paper looks at the application of adaptive quadrature to Bayesian inference, showing the rate of convergence as well as demonstrating the ultility of the method in a few challenging examples.
Recommended citation: Bilodeau, Stringer and Tang. "Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference." Journal of the American Statistical Association, 1-11. [https://www.tandfonline.com/doi/full/10.1080/01621459.2022.2141635]
Variability of extragalactic X-ray jets on kiloparsec scales
Published in Nature Astronomy, 2023
This paper examines X-ray emissions around supermassive black holes and provides evidence that emissions varies in intensity across time.
Recommended citation: Meyer, Shaik, Tang, Reid, Reddy, Breiding, ... and Trevor (2023). Variability of extragalactic X-ray jets on kiloparsec scales. Nature Astronomy, 1-9. https://www.nature.com/articles/s41550-023-01983-1
On the tightness of the Laplace approximation for statistical inference
Published in Statistics and Probability Letters, 2023
This paper demonstrates that the error rate of the Laplace approximation is tight by providing a sharp lower bound for it.
Recommended citation: Bilodeau, Tang, and Stringer (2023). On the tightness of the Laplace approximation for statistical inference. Statistics and Probability Letters, 198, 109839. https://www.sciencedirect.com/science/article/pii/S0167715223000639
Asymptotic behaviour of the modified likelihood root
Published in Statistica Sinica, 2023
This paper examines the modified likelihood root and demonstrates that it can be expanded as a formal series in the Wald and score statistics.
Recommended citation: Tang and Reid. "Asymptotic behaviour of the modified likelihood root" Statistica Sinica, to appear. https://www3.stat.sinica.edu.tw/ss_newpaper/SS-2022-0016_na.pdf
A Note on Monte Carlo Integration in High Dimensions
Published in The American Statistician, 2023
This paper examines Monte Carlo integration for high-dimensional functions and obtains finite sample guarantees for its performance through concentration inequalities.
Recommended citation: Tang, Y. (2023). A Note on Monte Carlo Integration in High Dimensions. The American Statistician (accepted). https://arxiv.org/abs/2206.09036
talks
Talk on: Modified Likelihood Root in High Dimensions
Published:
A talk about the paper “Modified Likelihood Root in High Dimensions”. Recorded at for the JSM 2020 (virtual) conference.
Talk on: Approximate nature of p-values and reproducibility
Published:
A talk based on the paper “General Behaviour of P-values under the Null and Alternative”. Presented live for the Toronto Workshop on Reproducibility.
Talk on: Laplace and Saddlepoint Approximations in High Dimensions
Published:
A talk based on the paper “Laplace and Saddlepoint Approximations in High Dimensions”. Presented live for SSC 2021.
Talk on: Laplace and Saddlepoint Approximations in High Dimensions
Published:
A talk based on the paper “Laplace and Saddlepoint Approximations in High Dimensions”. Presented live for ISBA 2021. Recepient of the best student presentation award.
Talk on: Laplace and Saddlepoint Approximations in High Dimensions
Published:
A talk based on the paper “Laplace and Saddlepoint Approximations in High Dimensions” (the last one I promise). Presented live for JSM 2021.
Talk on: Monte Carlo in High Dimensions
Published:
Talk on: Integration Rules in Statistics
Published:
Talk on: Integration Rules in Statistics
Published:
teaching
STA248 - University of Toronto
Undergraduate course, University of Toronto, Department of Statistical Sciences, 2019
Introductory class to statistics primarily geared towards computer science majors.
Probability for Statistics - Imperial College London
Graduate course, Imperial College London, Department of Mathematics, 2022
Introductory class to probability theory for MSc students in statistics.