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20132019
most citedNonparametric independence screening and structure identification for ultra-high dimensional longitudinal data

57 citations · 72 across the 6 of their papers we have counts for

collaborators

6 papers

stat.ME2019★ 7 cited

A distribution-free smoothed combination method of biomarkers to improve diagnostic accuracy in multi-category classification

Raju Maiti, Jialiang Li, Priyam Das +3

Results from multiple diagnostic tests are usually combined to improve the overall diagnostic accuracy. For binary classification, maximization of the empirical estimate of the are…

stat.ME2018★ 6 cited

Multi-threshold Change Plane Model: Estimation Theory and Applications in Subgroup Identification

Jialiang Li, Yaguang Li, Baisuo Jin

We propose a multi-threshold change plane regression model which naturally partitions the observed subjects into subgroups with different covariate effects. The underlying grouping…

math.ST2018★ 2 cited

Copula-based Partial Correlation Screening: a Joint and Robust Approach

Xiaochao Xia, Jialiang Li

Screening for ultrahigh dimensional features may encounter complicated issues such as outlying observations, heteroscedasticity or heavy-tailed distribution, multi-collinearity and…

stat.ME2016

Factor Models for Asset Returns Based on Transformed Factors

Efang Kong, Jialiang Li, Wenyang Zhang

The Fama-French three factor models are commonly used in the description of asset returns in finance. Statistically speaking, the Fama-French three factor models imply that the ret…

stat.ME2015

Efficient estimation in semivarying coefficient models for longitudinal/clustered data

Ming-Yen Cheng, Toshio Honda, Jialiang Li

In semivarying coefficient models for longitudinal/clustered data, usually of primary interest is usually the parametric component which involves unknown constant coefficients. Fir…

stat.ME2013★ 57 cited

Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data

Ming-Yen Cheng, Toshio Honda, Jialiang Li +1

Ultra-high dimensional longitudinal data are increasingly common and the analysis is challenging both theoretically and methodologically. We offer a new automatic procedure for fin…