A5051903409- Statistical Methods and Inference
- Advanced Statistical Methods and Models
- Bayesian Methods and Mixture Models
- Statistical Methods and Bayesian Inference
- Statistical Distribution Estimation and Applications
- Spatial and Panel Data Analysis
- Financial Risk and Volatility Modeling
- Advanced Clustering Algorithms Research
- Probabilistic and Robust Engineering Design
- Structural Health Monitoring Techniques
- Genetic and phenotypic traits in livestock
- Advanced Causal Inference Techniques
- Data Management and Algorithms
- Grey System Theory Applications
- Knee injuries and reconstruction techniques
- Image and Signal Denoising Methods
- Direction-of-Arrival Estimation Techniques
- Statistical Methods in Clinical Trials
- Gene expression and cancer classification
- Monetary Policy and Economic Impact
- Livestock Management and Performance Improvement
- Menstrual Health and Disorders
- Firm Innovation and Growth
- Osteoarthritis Treatment and Mechanisms
- Healthcare and Venom Research
Jianghan University
2024
Shanghai Lixin University of Accounting and Finance
2019-2022
Southwest Jiaotong University
2022
East China Normal University
2012-2021
Shanghai Normal University
2015-2020
Hubei University of Chinese Medicine
2016
The semiparametric partially linear varying coefficient models (SPLVCM) are frequently used in statistical modelling, but most existing methods were built on either the least-square or likelihood-based methods, which very sensitive to outliers and their efficiency may be significantly reduced for heavy tail error distribution. This paper proposes a new efficient robust estimation procedure SPLVCM based modal regression. We establish asymptotic normality of proposed estimators both parametric...
Exercise is recommended as the first-line management for knee osteoarthritis (KOA); however, it difficult to determine which specific exercises are more effective. This study aimed explore potential mechanism and effectiveness of a leg-swinging exercise practiced in China, called 'KOA pendulum therapy' (KOAPT). Intraarticular hydrostatic dynamic pressure (IHDP) suggested partially explain signs symptoms KOA. As such this paper set out vivo minipigs human volunteers alongside feasibility...
The beta regression models are commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). In this paper, we consider issue of variable selection for with varying dispersion (VBRM), which both mean and depend upon predictor variables. Based on a penalized likelihood method, consistency oracle property estimators established. Following coordinate descent algorithm idea generalized linear models, develop new procedure VBRM, can efficiently...
In this paper, we propose a new full iteration estimation method for quantile regression (QR) of the single-index model (SIM). The asymptotic properties proposed estimator are derived. Furthermore, variable selection procedure QR SIM by combining with adaptive LASSO penalized to get sparse index parameter. oracle established. Simulations various non-normal errors conducted demonstrate finite sample performance and procedure. illustrate analyzing real data set.
The group Lasso is a penalized regression method, used in problems where the covariates are partitioned into groups to promote sparsity at level [27 M. Yuan and Y. Lin, Model selection estimation with grouped variables, J. R. Stat. Soc. Ser. B 68 (2006), pp. 49–67. doi: 10.1111/j.1467-9868.2005.00532.x[Crossref] , [Google Scholar]]. Quantile Lasso, natural extension of quantile [25 Wu Liu, Variable regression, Statist. Sinica 19 (2009), 801–817.[Web Science ®] Scholar]], good alternative...
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The aim of this paper is to explore variable selection approaches in the partially linear proportional hazards model for multivariate failure time data. A new penalised pseudo-partial likelihood method proposed select important covariates. Under certain regularity conditions, we establish rate convergence and asymptotic normality resulting estimates. We further show that procedure can correctly true submodel, as if it was known advance. Both simulated real data examples are presented...
Single index models are natural extensions of linear and overcome the so-called curse dimensionality. They very useful for longitudinal data analysis. In this paper, we develop a new efficient estimation procedure single with data, based on Cholesky decomposition local smoothing method. Asymptotic normality proposed estimators both parametric nonparametric parts will be established. Monte Carlo simulation studies show excellent finite sample performance. Furthermore, illustrate our methods...
In this paper, the semi varying coefficient zero-inflated generalized Poisson model is discussed based on penalized log-likelihood. All functions are fitted by spline (P-spline), and Expectation-maximization algorithm used to drive these estimators. The estimation approach rapid computationally stable. Under some mild conditions, consistency asymptotic normality of resulting estimators given. score test statistics about dispersion parameter P-spline estimation. Both simulated real data...
The varying coefficient model (VCM) is an important generalization of the linear regression and many existing estimation procedures for VCM were built on L 2 loss, which popular its mathematical beauty but not robust to non-normal errors outliers. In this paper, we address problem both robustness efficiency variable selection procedure based convex combined loss 1 instead only quadratic VCM. By using local modeling method, asymptotic normality driven a useful method proposed weight composite...
Ultra-high-dimensional data are frequently seen in many contemporary statistical studies, which pose challenges both theoretically and methodologically. To address this issue under longitudinal setting, we propose a marginal nonparametric screening method to hunt for the relevant covariates additive models. A new data-driven thresholding an iterative procedure developed. Especially, sample splitting is proposed further reduce false selection rates. Although repeated measurements within each...