New Advances in Statistics and Data Science
Springer International Publishing (Verlag)
978-3-319-88776-0 (ISBN)
This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the "Challenge of Big Data and Applications of Statistics," in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.
Ding-Geng Chen is a Fellow of the American Statistical Association and is currently the Wallace Kuralt distinguished professor at the University of North Carolina at Chapel Hill. He was a professor at the University of Rochester and the Karl E. Peace endowed eminent scholar chair in biostatistics at Georgia Southern University. He is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in Monte-Carlo simulations, clinical trial biostatistics and public health statistics. Professor Chen has more than 100 referred professional publications, has co-authored and co-edited six books on clinical trial methodology, meta-analysis and public health applications, and has been invited nationally and internationally to give speeches on his research. Professor Chen was honored with the "Award of Recognition" in 2014 by the Deming Conference Committee for highly successful advanced biostatistics workshop tutorials with his books. Zhezhen Jin is a Professor of Biostatistics at Columbia University. His research interests in statistics include survival analysis, resampling methods, longitudinal data analysis, and nonparametric and semiparametric models. Dr. Jin has collaborated on research in the areas of cardiology, neurology, hematology, oncology and epidemiology. He is a founding editor-in-chief of Contemporary Clinical Trials Communications, serves as an associate editor for Lifetime Data Analysis, Contemporary Clinical Trials, and Communications for Statistical Applications and Methods, and is on the editorial board for Kidney International, the Journal of the International Society for Nephrology. Dr. Jin has published over 150 peer-reviewed research papers in statistical and medical journals, and is also a Fellow of the American Statistical Association. Gang Li is a Professor of Biostatistics and Biomathematics at the University of California at Los Angeles (UCLA) and the Director of UCLA's Jonsson Comprehensive Cancer Center Biostatistics Shared Resource. His research interests include survival analysis, longitudinal data analysis, high dimensional and OMICs data analysis, clinical trials, and evaluation and development of biomarkers. He has published over 100 papers in a wide variety of prestigious journals such as the Annals of Statistics, the Journal of the American Statistical Association, the Journal of the Royal Statistical Society: Series B, Biometrika, and Biometrics. He is an Elected Fellow of the Institute of Mathematics, the American Statistical Association, and the Royal Statistical Society, as well as an Elected Member of the International Statistics Institute. He has been serving on the editorial board of several statistical journals including Biometrics. Dr. Li has been active in collaborating with researchers in basic science, translational research, and clinical trials, and has been a statistics Principal Investigator for multiple NIH funded projects. Yi Li is a Professor of Biostatistics and Global Public Health at the University of Michigan School of Public Health (UM-SPH). He is currently the Director of China Initiatives at UM-SPH, and served as the Director of Kidney Epidemiology and Cost Center at the University of Michigan from 2011-2016. Dr. Li is an Elected Fellow of the American Statistical Association, and is serving as Associate Editor for several major statistical journals, including the Journal of the American Statistical Association, Biometrics, the Scandinavian Journal of Statistics, and Lifetime Data Analysis. His current research interests are survival analysis, longitudinal and correlated data analysis, measurement error problems, spatial models and clinical trial designs. He has published more than 140 papers in major statistical and biom
Part 1 Review and Theoretical Framework in Data Science.- Ch 1 Statistical Distances and Their Role in Robustness.- Ch 2 The Out-source Error in Multi-source Cross Validation-type Procedures.- Ch 3.- Meta-Analysis for Rare Events as Binary Outcomes.- Ch 4 New Challenges and Strategies in Robust Optimal Design for Multicategory Logit Modelling.- Ch 5 Testing of Multivariate Spline Growth Model.- Part 2 Complex and Big Data Analysis.- Ch 6 Uncertainty Quantification Using the Neighbor Gaussian Process.- Ch 7 Tuning Parameter Selection in the LASSO with Unspecified Propensity.- Adaptive Filtering Increases Power to Detect Differently Expressed Genes.- Ch 9 Estimating Parameters in Complex Systems with Functional Outputs - A Wavelet-based Approximate Bayesian Computation Approach.- Ch 10 A maximum Likelihood Approach for Non-invasive Cancer Diagnosis Using Methylation Profiling of Cell-free DNA from Blood.- Part 3 Clinical Trials, Statistical Shape Analysis and Application.- Ch 11 A Simpleand Efficient Statistical Approach for Designing an Early Phase II Clinical Trial - Ordinal Linear Contrast Test.- Ch 12 Landmark-constrained Statistical Shape Analysis of Elastic Curves and Surfaces.- Ch 13 Phylogeny-based kernels with Application to Microbiome Association Studies.- Ch 14 Accounting for Differential Error in Time-to-event Analyses using Imperfect Electronic Health Record-derived Endpoints.- Part 4 Statistical Modeling and Data Analysis.- Ch 15 Modeling Inter-trade Durations in the Limit Order market.- Ch 16 Assessment of Drug Interactions with Repeated Measurements.- Ch 17 Statistical Indices for Risk Tracking in Longitudinal Studies.- Ch 18 Statistical Analysis of Labor market Integration: A Mixture Regression Approach.- Ch 19 Bias Correction in Age-Cohort Models Using Eigen Analysis.
| Erscheinungsdatum | 09.02.2019 |
|---|---|
| Reihe/Serie | ICSA Book Series in Statistics |
| Zusatzinfo | XXIII, 348 p. 74 illus., 41 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 562 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | Big Data • Clinical trials design • DNA statistical analysis • functional data analysis • Gene expression analysis • high dimensional statistical method • Longitudinal data analysis • Nonparametric Statistics • phylogeny-based kernels • spline growth model • statistical genetics and bioinformatics • Statistical Methods • statistical shape analysis • survival data analysis • uncertainty quantification |
| ISBN-10 | 3-319-88776-9 / 3319887769 |
| ISBN-13 | 978-3-319-88776-0 / 9783319887760 |
| Zustand | Neuware |
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