Zifeng Zhao - Research

Selected Preprints (google scholar)

F. Jiang, Z. Zhao

On non-stationary dynamic pricing: adaptivity and optimality, in submission. - paper

D. Fabrizi, S. Mehder, J. Sun, O. Yilmaz, Z. Zhao

The effects of within-cart price increases on consumer behavior: evidence from field experiments, in submission. - paper

Y. He, L. Peng, D. Zhang, Z. Zhao

Uniform inference for central and tail distributions with censored data, in submission. - paper

Z. Zhao, O. Yilmaz, C. Ke, R. Wang

Modeling consumer choice and optimizing assortment under the threshold multinomial logit model, R&R at MS. - paper

D. Wang, Z. Zhao

Optimal change-point testing for high-dimensional linear models with temporal dependence. - paper

Z. Zhao, L. Chen, L. Lin

Change-point detection in dynamic networks via graphon estimation. - paper

Publications

[28] Y. Ma, F. Jiang, Z. Zhao, H. Yang, Y. Yu (2026+)

Locally private nonparametric contextual multi-armed bandits with transfer learning. - paper

Journal of the American Statistical Association.

[27] F. Ekbatani, O. Yilmaz, Z. Zhao, R. Wang, A. Vakhutinsky (2026+)

Data-driven pricing for availability-based upgrades under a multiple binary choice model with copula. - paper

Manufacturing and Service Operations Management.

Finalist, 2025 M&SOM Practice-Based Research Competition

[26] C. Ke, R. Wang, Z.Zhao (2026+)

Discrete choice models with piecewise linear utility: modeling, estimation and pricing. - paper

Manufacturing and Service Operations Management.

[25] Z. Zhao, F. Jiang, Y. Yu (2026+)

Contextual dynamic pricing: algorithms, optimality, and local differential privacy constraints. - paper

Journal of the American Statistical Association.

[24] Z. Zhao, F. Jiang, Y. Yu, X. Chen (2026+)

High-dimensional dynamic pricing under non-stationarity: learning and earning with change-point detection. - paper

Management Science.

[23] Y. Cheng, Z. Zhao, H. Xu (2025)

Learning personalized ad impact via contextual reinforcement learning under delayed rewards. - paper

Neural Information Processing Systems (Neurips).

[22] F. Wang, F. Jiang, Z. Zhao, Y. Yu (2025)

Transfer learning for nonparametric contextual dynamic pricing. - paper

International Conference on Machine Learning (ICML).

[21] H. Xu, D. Wang, Z. Zhao, Y. Yu (2024)

Change point inference in high-dimensional regression models under temporal dependence. - paper

Annals of Statistics.

[20] Z. Zhao, T.F. Ma, W.L. Ng, C.Y. Yau (2024)

A composite likelihood-based approach for change-point detection in spatiotemporal processes. - paper

Journal of the American Statistical Association.

[19] S. Sun, Z. Zhao, F. Jiang, X. Shao (2024)

SNSeg: An R package for time series segmentation via self-normalization. - paper

The R Journal.

[18] P. Shi, Z. Zhao (2024)

Enhanced pricing and management of bundled insurance risks with dependence-aware prediction using pair copula construction. - paper

Journal of Econometrics.

[17] R. Wang, C. Ke, Z. Zhao (2024)

Anticipated wait and its effects on consumer choice, pricing, and assortment management. - paper

Manufacturing and Service Operations Management.

[16] F. Jiang, Z. Zhao, X. Shao (2023)

Time series analysis of COVID-19 infection curve: a change-point perspective. - paper

Journal of Econometrics.

[15] C. Padilla, D. Wang, Z. Zhao, Y. Yu (2022)

Change-point detection for sparse and dense functional data in general dimensions. - paper

Neural Information Processing Systems (Neurips).

[14] Z. Zhao, F. Jiang, X. Shao (2022)

Segmenting time series via self-normalization. - paper, package

Journal of the Royal Statistical Society - Series B.

[13] D. Wang, Z. Zhao, Y. Yu, R. Willett (2022)

Functional linear regression with mixed predictors. - paper

Journal of Machine Learning Research.

[12] D. Wang, Z. Zhao, K. Lin, R. Willett (2021)

Statistically and computationally efficient change point localization in regression settings. - paper

Journal of Machine Learning Research.

[11] F. Jiang, Z. Zhao, X. Shao (2021)

Modelling the COVID-19 infection trajectory: A piecewise linear quantile trend model. - paper

Journal of the Royal Statistical Society - Series B, with discussion.

[10] Y. He, L. Peng, D. Zhang, Z. Zhao (2021)

Risk analysis via generalized Pareto distributions. - paper

Journal of Business & Economic Statistics.

[9] Z. Zhao, C.Y. Yau (2021)

Alternating dynamic programming for multiple epidemic change-point estimation. - paper

Journal of Computational and Graphical Statistics.

[8] Z. Zhao, P. Shi, Z. Zhang (2021)

Modeling multivariate time series with copula-linked univariate D-vines. - paper

Journal of Business & Economic Statistics.

[7] Z. Zhao, P. Shi, X. Feng (2020)

Knowledge learning of insurance risks using dependence models. - paper

INFORMS Journal on Computing.

[6] Z. Zhao (2020)

Dynamic bivariate peak over threshold model for joint tail risk dynamics of financial markets. - paper

Journal of Business & Economic Statistics.

[5] P. Shi, Z. Zhao (2020)

Regression for copula-linked compound distributions with applications in modeling aggregate insurance claims. - paper

Annals of Applied Statistics.

[4] Z. Zhao, Z. Zhang (2018)

Semi-parametric dynamic max-copula model for multivariate time series. - paper

Journal of the Royal Statistical Society - Series B.

[3] Z. Zhao, Z. Zhang, R. Chen (2018)

Modeling maxima with autoregressive conditional Frechet model. - paper

Journal of Econometrics.

[2] C.Y. Yau, Z. Zhao (2016)

Inference for multiple change-points in time series via likelihood ratio scan statistics. - paper

Journal of the Royal Statistical Society - Series B.

[1] K. West, Z. Zhao (2016)

Regressor and disturbance have moments of all order, least squares estimator has none. - paper

Statistics & Probability Letters.

Fundings

NSF DMS-2014053 (PI)
Oracle Labs (PI)
The Kemper Foundation (PI)