New Frontiers of Biostatistics and Bioinformatics [electronic resource] /
Zhao, Yichuan.editor.
Chen, Ding-Geng.editor.
This book is comprised of presentations delivered at the 5th Workshop on Biostatistics and Bioinformatics held in Atlanta on May 5-7, 2017. Featuring twenty-two selected papers from the workshop, this book showcases the most current advances in the field, presenting new methods, theories, and case applications at the frontiers of biostatistics, bioinformatics, and interdisciplinary areas. Biostatistics and bioinformatics have been playing a key role in statistics and other scientific research fields in recent years. The goal of the 5th Workshop on Biostatistics and Bioinformatics was to stimulate research, foster interaction among researchers in field, and offer opportunities for learning and facilitating research collaborations in the era of big data. The resulting volume offers timely insights for researchers, students, and industry practitioners. .
Includes bibliographical references and index.
Chapter1. Importance of Adjusting for Multi-Stage Design when Analyzing Data from Complex Surveys -- Chapter2. A selective overview of semiparametric mixture of regression models -- Chapter3. Estimating the Confidence Interval of Evolutionary Stochastic Process Mean from Wavelet based Bootstrapping -- Chapter4. A New Wavelet-Based Approach for Mass Spectrometry Data Classification -- Chapter5. Identification of Pathway-Modulating Genes using the Biomedical Literature Mining -- Chapter6. Equivalence tests in subgroup analyses -- Chapter7. Empirical Study on High-Dimensional Variable Selection and Prediction under Competing Risks -- Chapter8. Learning Gene Regulatory Networks with High-Dimensional Heterogeneous Data -- Chapter9. Discriminant Analysis and Normalization Methods for Next-generation Sequencing Data -- Chapter10. Rank-based Empirical Likelihood for Regression Models with Responses Missing at Random -- Chapter11. Nonparametric Estimation of a Hazard Rate Function with Right Truncated Data -- Chapter12. On the landmark survival model for dynamic prediction of event occurrence using longitudinal data -- Chapter13. Analysis of the High School Longitudinal Study to Evaluate Associations Among Mathematics Achievement, Mentorship and Student Participation in STEM Programs -- Chapter14. OptimalWeightedWilcoxon–Mann–Whitney Test for Prioritized Outcomes -- Chapter15. Wavelet-based profile monitoring using order-thresholding recursive CUSUM schemes -- Chapter16. Bayesian Nonparametric Spatially Smoothed Density Estimation -- Chapter17. Nonparametric Estimation of a Cumulative Hazard Function with Right Truncated Data -- Chapter18. Mammogram Diagnostics Using Robust Wavelet-based Estimator of Hurst Exponent -- Chapter19. Statistical Power and Bayesian Assurance in Clinical Trial Design -- Chapter20. Predicting Confidence Interval for the Proportion at the Time of Study Planning in Small Clinical Trials -- Chapter21. Performance evaluation of normalization approaches for metagenomic compositional data on differential abundance analysis -- Chapter22. Statistical Modeling for the Heart Disease Diag-nosis via Multiple Imputation.
This book is comprised of presentations delivered at the 5th Workshop on Biostatistics and Bioinformatics held in Atlanta on May 5-7, 2017. Featuring twenty-two selected papers from the workshop, this book showcases the most current advances in the field, presenting new methods, theories, and case applications at the frontiers of biostatistics, bioinformatics, and interdisciplinary areas. Biostatistics and bioinformatics have been playing a key role in statistics and other scientific research fields in recent years. The goal of the 5th Workshop on Biostatistics and Bioinformatics was to stimulate research, foster interaction among researchers in field, and offer opportunities for learning and facilitating research collaborations in the era of big data. The resulting volume offers timely insights for researchers, students, and industry practitioners. .
Statistics .
Big data.
Biostatistics.
Statistical Theory and Methods.
Big Data/Analytics.
Statistics for Life Sciences, Medicine, Health Sciences.
Biostatistics.