Dr. Xiaofeng Wang is currently a faculty member in the Department of Quantitative Health Sciences at the Cleveland Clinic Lerner Research Institute. The majority of his research has been in the context of biostatistical methodology and computing with applications to medical and public health sciences. His areas of interest in this context have extended from latent variable and measurement error models, quantitative image data analysis, analysis of massive observational data and statistical mining in bioinformatics and neuroinfomatics.
Latent Variable and Measurement Error Models
The Effects of Error Magnitude and Bandwidth Selection for Deconvolution with Unknown Error Distribution (with Deping Ye), Journal of Nonparametric Statistics (2012)
The error distribution is generally unknown in deconvolution problems with real applications. A separate independent...
Nonparametric Multi-State Representations of Survival and Longitudinal Data with Measurement Error (with Bo Hu, Liang Li, and Tom Greeene), Statistics in Medicine (2012)
This paper proposes a nonparametric procedure to describe the progression of longitudinal cohorts over time...
Deconvolution Estimation in Measurement Error Models: The R Package decon (with Bin Wang), Journal of Statistical Software (2011)
Data from many scientific areas often come with measurement error. Density or distribution function estimation...
Estimating Smooth Distribution Function in the Presence of Heteroscedastic Measurement Errors (with Zhaozhi Fan and Bin Wang), Computational Statistics and Data Analysis (2010)
Measurement error occurs in many biomedical fields. The challenges arise when errors are heteroscedastic since...
Marginal Hazards Model for Multivariate Failure Time Data with Auxiliary Covariates (with Zhaozhi Fan), Journal of Nonparametric Statistics (2009)
A marginal hazards model of multivariate failure times has been developed based on the ‘working...
Quantitative Image Data Analysis
Strengthened Functional Connectivity in the Brain During Muscle Fatigue (with Zhiguo Jiang, Katarzyna Kisiel-Sajewicz, Jin H. Yan, and Guang H. Yue), NeuroImage (2012)
Fatigue caused by sustaining submaximal-intensity muscle contraction(s) involves increased activation in the brain such as...
A Generalized Regression Model for Region of Interest Analysis of fMRI Data (with Zhiguo Jiang, Janis J. Daly, and Guang H. Yue), NeuroImage (2012)
In this study functional Magnetic Resonance Imaging (fMRI) was used to evaluate cortical motor network...
On Nonparametric Comparison of Images and Regression Surfaces (with Deping Ye), Journal of Statistical Planning and Inference (2010)
Multivariate local regression is an important tool for image processing and analysis. In many practical...
Analysis of Complex Observational Data
Joint Generalized Models for Multi-Dimensional Outcomes: A Case Study of Neuroscience Data from Multi-Modalities, Biometrical Journal (2012)
This paper is motivated from the analysis of neuroscience data in a study of neural...
Modeling Heterogeneity and Dependence for Analysis of Neuronal Data (with Jiayang Sun, Kenneth J. Gustafson, and Guang H. Yue), Statistics in Medicine (2007)
In this paper, we describe two types of neuroscience problems which challenge the typical statistical...
Spatial-Temporal Data Mining Procedure: LASR (with Jiayang Sun and Kath Bogie), IMS Lecture Notes–Monograph Series (2006)
This paper is concerned with the statistical development of our spatial-temporal data mining procedure, LASR...
Bioinformatics and Neuroinformatics
Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression (with Bin Wang, Shu-Guang Zhang, Ming Tan, and Yaguang Xi), PLoS One (2012)
MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most...
Time-Dependent Cortical Activation in Voluntary Muscle Contraction (with Qi Yang, Yin Fang, Vlodek Siemionow, Wanxiang Yao, and Guang H. Yue), The Open Neuroimaging Journal (2011)
This study was to characterize dynamic source strength changes estimated from high-density scalp electroencephalogram (EEG)...
Normalizing Bead-Based MicroRNA Expression Data: A Measurement Error Model-Based Approach (with Bin Wang and Yaguang Xi), Bioinformatics (2011)
Motivation: Compared with complementary DNA (cDNA) or messenger RNA (mRNA) microarray data, microRNA (miRNA) microarray...
A Personalized MicroRNA Microarray Normalization Method Using a Logistic Regression Model (with Bin Wang, Paul Howell, Xuemin Qian, Kun Huang, Adam I. Riker, Jingfang Ju, and Yaguang Xi), Bioinformatics (2010)
Motivation: MicroRNA (miRNA) is a set of newly discovered non-coding small RNA molecules. Its significant...
Assessing Time-Dependent Association between Scalp EEG and Muscle Activation: A Functional Random-Effects Model Approach (with Qi Yang, Zhaozhi Fan, Chang-Kai Sun, and Guang H. Yue), Journal of Neuroscience Methods (2009)
This study investigates time-dependent associations between source strength estimated from high-density scalp electroencephalogram (EEG) and...