Liming Xiang

Associate Professor

Nanyang Technological University (NTU)

Singapore

Research Interests

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Liming Xiang is an Associate Professor at Nanyang Technological University, Singapore. His research primarily focuses on statistical methodologies, including generalized linear mixed models, survival analysis, and feature screening techniques. He has contributed to the field through recent publications on topics such as disease progression and robotic-assisted rehabilitation following stroke.

Articles (12)

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Collaborators (3)

Simone Kager

Singapore Institute of Technology

SINGAPORE

View details

Tonghui Yu

Nanyang Technological University

SINGAPORE

View details

Domenico Campolo

Associate Professor

Nanyang Technological University (NTU)

SINGAPORE

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Liming Xiang

Research Interests

Contact this professor

About

Articles (12)

An iterative two-step regularization approach for selection of fixed and random effects in generalized linear mixed models

Conditional quasi-likelihood inference for mean residual life regression with clustered failure time data

Disease progression based feature screening for ultrahigh-dimensional survival-associated biomarkers

Upper Limb Position Matching After Stroke: Evidence for Bilateral Asymmetry in Precision but Not in Accuracy

Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data

Generalized accelerated hazards mixture cure models with interval-censored data

Robotic Assisted Upper Limb Training Post Stroke: A Randomized Control Trial Using Combinatory Approach Toward Reducing Workforce Demands

Quantile regression for survival data with covariates subject to detection limits

Generalized accelerated hazards mixture cure models with interval-censored data

Robotic Assisted Upper Limb Training Post Stroke: A Randomized Control Trial Using Combinatory Approach Toward Reducing Workforce Demands

Responses of in-service shield tunnel to overcrossing tunnelling in soft ground

Quantile regression for survival data with covariates subject to detection limits

Collaborators (3)

Simone Kager

Tonghui Yu

Domenico Campolo

Make your next move.