Haipeng SHEN
Prof. Haipeng SHEN
Innovation and Information Management
Associate Dean (Executive Education)
Patrick S C Poon Professor in Analytics and Innovation
Chair of Business Analytics and Innovation

3917 1624

KK 815

Testing and Support Recovery of Correlation Structures for Matrix-valued Observations With an Application to Stock Market Data

Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between emerging markets and developed countries. It is therefore imperative to make rigorous statistical inference on correlation matrix equality between the two groups of countries. However, if the traditional vector-valued approach is undertaken, such inference is either infeasible due to limited number of countries comparing to the relatively abundant assets, or invalid due to the violations of temporal independence assumption. This highlights the necessity of treating the observations as matrix-valued rather than vector-valued. With matrix-valued observations, our problem of interest can be formulated as statistical inference on covariance structures under sub-Gaussian distributions, i.e., testing non-correlation and correlation equality, as well as the corresponding support estimations. We develop procedures that are asymptotically optimal under some regularity conditions. Simulation results demonstrate the computational and statistical advantages of our procedures over certain existing state-of-the-art methods for both normal and non-normal distributions. Application of our procedures to stock market data reveals interesting patterns and validates several economic propositions via rigorous statistical testing.

Hazard Rate Estimation for Call Center Customer Patience Time

Estimating the hazard function of customer patience time has become a necessary component of effective operational planning such as workforce staffing and scheduling in call centers. When customers get served, their patience times are right-censored. In addition, the exact event times in call centers are sometimes unobserved and naturally binned into time intervals, due to the design of data collection systems. We develop a TunT (Transform-unTransform) estimator that turns the difficult problem of nonparametric hazard function estimation into a regression problem on binned and right-censored data. Our approach starts with binning event times and transforming event count data with a mean-matching transformation, which enables a simpler characterization of the heteroscedastic variance function. A nonparametric regression technique is then applied to the transformed data. Finally, the estimated regression function is back-transformed to yield an estimator for the original hazard function. The proposed estimation procedure is illustrated using call center data to reveal interesting customer patience behavior, and health insurance plan trial data to compare the effect between treatment and control groups. The numerical study shows that our approach yields more accurate estimates and better staffing decisions than existing methods.

HKU Business School at the heart of medical revolution

Innovation in healthcare is forever changing how we see and experience the medical industry. The environment is offering HKU’s Faculty of Business and Economics (the Faculty) a unique opportunity to be at the forefront of utilising rich data, creating better health outcomes for everyone.

Big data is rewriting the medical future of millions of people

Patients in China suffering from acute ischemic stroke, when arteries leading to the brain are blocked, have traditionally not experienced excellent clinical outcomes. Battling this disease has been a long-term battle for physicians working in the country’s overcrowded under-resourced public hospitals. Professor Haipeng Shen, Patrick S C Poon Professor in Analytics and Innovation at HKU Business School, has been working to change this situation by collaborating with top physicians and embracing the power of big data.

Functional Censored Quantile Regression

We propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we conduct extensive simulation studies to evaluate the finite sample performance of our method. Finally, we analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients. The results reinforce the importance of the morning blood pressure surge phenomenon, whose effect has caught attention but remains controversial in the medical literature. Supplementary materials for this article are available online.


Professor Haipeng SHEN, Patrick S C Poon Professor in Analytics and Innovation, was interviewed by four media outlets on his recent research in data-driven decision making in healthcare management.