Article
Mathematics
Rong Liu, Shishun Zhao, Tao Hu, Jianguo Sun
Summary: Variable selection is often needed in various fields. This paper focuses on the problem of variable selection in the case of interval-censored failure time data arising from generalized linear models. A penalized least squares method with an unbiased transformation is proposed and the method's properties and asymptotic normality of the estimators are established.
Article
Health Care Sciences & Services
Mingyue Du, Hui Zhao, Jianguo Sun
Summary: A unified penalized variable selection procedure for failure time data is proposed in the paper, which takes into account dependent censoring and overcomes the limitations of existing methods. The approach works well for practical situations and is applied to real data from Alzheimer's Disease Neuroimaging Initiative study.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2021)
Article
Mathematical & Computational Biology
Tian Tian, Jianguo Sun
Summary: This paper discusses variable selection and structure estimation for the nonparametric additive Cox model, proposing a penalized sieve maximum likelihood approach with a group coordinate descent algorithm. Simulations demonstrate the effectiveness of the method, and it is applied to an Alzheimer's disease study for identifying important genetic factors.
BIOMETRICAL JOURNAL
(2023)
Article
Mathematics
Fan Feng, Guanghui Cheng, Jianguo Sun
Summary: In this paper, a penalized variable selection technique based on Cox's proportional hazards model is developed for length-biased data with interval censoring. The proposed method is applied to real data and outperforms traditional variable selection methods based on conditional likelihood.
Article
Mathematical & Computational Biology
Zhong Guan
Summary: The proposed method for estimating baseline density function and regression coefficients in proportional hazard regression models based on interval-censored event time data produces smooth estimates of survival functions with a faster convergence rate than existing methods. Simulation results and examples using real data demonstrate that the proposed method outperforms its main competitors in terms of finite sample performance.
STATISTICS IN MEDICINE
(2021)
Article
Geosciences, Multidisciplinary
Romina Gonella, Mathias Bourel, Liliane Bel
Summary: This work focuses on variable selection for spatial regression models with irregular lattices and Conditional or Simultaneous Auto-Regressive (CAR or SAR) models for errors. The strategy is to whiten the residuals by estimating their spatial covariance matrix and then perform L1-penalized regression LASSO. The study proves the sign consistency for general dependent errors and provides conditions on the weight matrix of the SAR or CAR model to ensure the validity of the method.
SPATIAL STATISTICS
(2022)
Article
Biology
Yuxiang Wu, Hui Zhao, Jianguo Sun
Summary: The paper discusses variable selection on interval-censored failure time data arising from the Cox model. A penalized sieve maximum likelihood variable selection and estimation method is proposed, and its oracle property is established. Simulation results show that the proposed method performs well in practical situations, and it is also applied to a real dataset.
Article
Health Care Sciences & Services
Mengzhu Yu, Yanqin Feng, Ran Duan, Jianguo Sun
Summary: This study proposes an estimating equation-based approach for regression analysis of multivariate interval-censored data from the additive hazards model, allowing for informative censoring. A simulation study confirms the effectiveness of the method in practical situations. The proposed approach is also successfully applied to real data.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2022)
Article
Mathematics, Applied
Shikhar Tyagi, Arvind Pandey, Varun Agiwal, Christophe Chesneau
Summary: The article introduces a method based on frailty models to study the impact of unobserved covariates, using generalized Weibull and generalized log-logistic-II distributions as baseline distributions, and employing Bayesian methods to estimate model parameters and perform model comparisons, with results showing that the new models perform better.
COMPUTATIONAL & APPLIED MATHEMATICS
(2021)
Article
Mathematics
Jiaxuan Liang, Yi Cheng, Yuqi Su, Shuyue Xiao, Yunquan Song
Summary: When the spatial response variables are discrete, the spatial logistic autoregressive model improves classification accuracy by adding an additional network structure to the ordinary logistic regression model. Sparse spatial logistic regression models have attracted significant attention due to the emergence of high-dimensional data in various fields. In this paper, a variable selection method is proposed for the high-dimensional spatial logistic autoregressive model. The penalized likelihood function is efficiently solved using an algorithm to identify important variables and make predictions. Simulations and a real example demonstrate the good performance of the proposed methods in a limited sample size.
Article
Statistics & Probability
Mingyue Du, Jianguo Sun
Summary: Variable selection for interval-censored failure time data has recently gained significant attention in both method developments and practical applications. Interval-censored data, where the failure time is only known to lie within an interval, are common in various fields and more research is needed in this relatively new but important topic.
INTERNATIONAL STATISTICAL REVIEW
(2022)
Article
Mathematics, Applied
Ximeng Zhang, Shishun Zhao, Tao Hu, Jianguo Sun
Summary: In this paper, we propose a copula-based semiparametric partly linear additive hazards model to analyze regression of bivariate interval-censored failure time data in biomedical and epidemiological studies. The model allows for time-dependent covariates and possible non-linear effects. Bernstein polynomials are used to estimate the baseline hazard functions and nonlinear covariate effects. Simulation results show that the proposed method is effective in practice. An illustration is also provided.
Article
Mathematical & Computational Biology
Chunjie Wang, Jingjing Jiang, Xinyuan Song
Summary: This paper presents a fully Bayesian approach for partly interval-censored data, utilizing a four-stage data augmentation procedure to tackle the challenges presented by the complex model and data structure. The proposed method is easy to implement and computationally attractive, with empirical performance evaluated through simulation studies and application to a dental health study.
STATISTICS IN MEDICINE
(2022)
Article
Statistics & Probability
Bo Zhao, Shuying Wang, Chunjie Wang
Summary: This article proposes a new frailty-based generalized estimating equation (GEE) method for proportional hazards (PH) model with informative interval-censored failure time data. The proposed method can provide an unbiased estimator of the cumulative hazards function. The performance of the method is assessed through extensive simulation study and an application to an AIDS clinical trial dataset.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2023)
Article
Statistics & Probability
Rui Ma, Shishun Zhao, Jianguo Sun, Shuying Wang
Summary: The accelerated hazards model is commonly used for regression analysis of failure time data, particularly for hazard functions with monotonicity property. Previous literature has focused on estimation or inference using right-censored data, but limited methods exist for interval-censored data. This paper proposes a sieve borrow-strength method for interval-censored data with informative censoring and establishes its asymptotic properties. Simulation studies validate the effectiveness of the proposed method, which is further applied to an AIDS clinical trial.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Statistics & Probability
Haiming Zhou, Timothy Hanson, Alejandro Jara, Jiajia Zhang
ANNALS OF APPLIED STATISTICS
(2015)
Article
Biology
Haiming Zhou, Timothy Hanson, Roland Knapp
Article
Medicine, General & Internal
Gang Xu, Junxiu Liu, Shiwei Liu, Haiming Zhou, Olubunmi Orekoya, Jie Liu, Yichong Li, Ji Tang, Chunlian Zhou, Jiuling Huang
Article
Public, Environmental & Occupational Health
Junxiu Liu, Shiwei Liu, Haiming Zhou, Timothy Hanson, Ling Yang, Zhengming Chen, Maigeng Zhou
EUROPEAN JOURNAL OF EPIDEMIOLOGY
(2016)
Article
Mathematics, Interdisciplinary Applications
Haiming Zhou, Timothy Hanson, Jiajia Zhang
LIFETIME DATA ANALYSIS
(2017)
Article
Statistics & Probability
Haiming Zhou, Xianzheng Huang
ELECTRONIC JOURNAL OF STATISTICS
(2016)
Article
Statistics & Probability
Xianzheng Huang, Haiming Zhou
JOURNAL OF NONPARAMETRIC STATISTICS
(2017)
Article
Statistics & Probability
Dewei Wang, Haiming Zhou, K. B. Kulasekera
JOURNAL OF NONPARAMETRIC STATISTICS
(2013)
Article
Cardiac & Cardiovascular Systems
Junxiu Liu, Xuemei Sui, Carl J. Lavie, Haiming Zhou, Yong-Moon Mark Park, Bo Cai, Jihong Liu, Steven N. Blair
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
(2014)
Article
Computer Science, Interdisciplinary Applications
Haiming Zhou, Timothy Hanson, Jiajia Zhang
JOURNAL OF STATISTICAL SOFTWARE
(2020)
Article
Cardiac & Cardiovascular Systems
Yong-Moon Mark Park, Xuemei Sui, Junxiu Liu, Haiming Zhou, Peter F. Kokkinos, Carl J. Lavie, James W. Hardin, Steven N. Blair
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY
(2015)