Zhiliang Ying
Columbia University
193 Papers
1.4K Citations
Zhiliang Ying is an academic researcher from Columbia University. The author has contributed to research in topics: Estimator & Regression analysis. The author has an hindex of 52, co-authored 190 publications. Previous affiliations of Zhiliang Ying include University of North Carolina at Chapel Hill & University of Illinois at Urbana–Champaign.
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Papers
Rank-based inference for the accelerated failure time model
TL;DR: In this paper, a broad class of rank-based monotone estimating functions is developed for the semiparametric accelerated failure time model with censored observations, which are shown to be consistent and asymptotically normal.
A Global Information Approach to Computerized Adaptive Testing
Hua Hua Chang,Zhiliang Ying +1 more
TL;DR: It is argued here that selection procedures based on global information should be used, at at early stages of a test when θ estimates are not likely to be close to the true θ, and an item selection procedure based on average global information is proposed.
Semiparametric analysis of transformation models with censored data
TL;DR: In this article, a unified estimation procedure for the analysis of censored data using linear transformation models, which include the proportional hazards model and the proportional odds model as special cases, is proposed, which is easily implemented numerically and its validity does not rely on the assumption of independence between the covariates and the censoring variable.
A simple resampling method by perturbing the minimand
TL;DR: In this article, a simple resampling method by perturbing the objective function repeatedly was proposed to estimate the covariance matrix of the estimator of a vector of parameters of interest, which can then be made based on a large collection of the resulting optimisers.
a-Stratified Multistage Computerized Adaptive Testing
Hua Hua Chang,Zhiliang Ying +1 more
TL;DR: In this article, a multistage adaptive testing approach that factors a into the item selection process is proposed, where the items in the item bank are stratified into a number of levels based on their a values.
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