Advanced Statistical Methods in Data Science

ISBN-13: 9789811096624 / Angielski / Miękka / 2018 / 222 str.

Ding-Geng Chen; Jiahua Chen; Xuewen Lu

Recenzje

"This handbook has a good collection of material on useful and interesting topics on data science. The book will be useful to graduate students and researchers interested in gaining perspectives and knowledge on this useful topic. The book comprises a wealth of information, a one-stop shopping, and can be served as a research reference book." (Technometrics, Vol. 59 (2), April, 2017)

Spis treści

Part I: Data Analysis Based on Latent or Dependent Variable Models.- Chapter 1: A New Method for Robust Mixture Regression and Outlier Detection.- Chapter 2: The Mixture Gatekeeping Procedure Based on Weighted Multiple Testing Correction for Correlated Tests.- Chapter 3: Regularization in Regime-switching Gaussian Autoregressive Models.- Chapter 4: Modeling Zero Inflation and Over-dispersion in the Length of Hospital Stay for Patients with Ischaemic Heart Disease.- Chapter 5: Robust Optimal Interval Design for High-Dimensional Dose Finding in Multi-Agent Combination Trials.- Part II: Life Time Data Analysis.- Chapter 6: Group Selection in Semi-parametric Accelerated Failure Time Model.- Chapter 7: A Proportional Odds Model for Regression Analysis of Case I Interval-Censored Data.- Chapter 8: Empirical Likelihood Inference under Density Ratio Models Based on Type I Censored Samples: Hypothesis Testing and Quantile Estimation.- Chapter 9: Recent Development in the Joint Modeling of Longitudinal Quality of Life Measurements and Survival Data from Cancer Clinical Trials.- Part III: Applied Data Analysis.- Chapter 10: Confidence Weighting Procedures for Multiple Choice Tests.- Chapter 11: Improving the Robustness of Parametric Imputation.- Chapter 12: Maximum Smoothed Likelihood Estimation of the Centre of a Symmetric Distribution.- Chapter 13: Dividend Pay-out Problems with the Logarithmic Utility.- Chapter 14: Modeling the Common Risk among Equities: A Multivariate Time Series Model with an Additive GARCH Structure.

O autorze

Professor Ding-Geng Chen is a fellow of the American Statistical Association and 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 clinical trial biostatistics and public health statistics. Professor Chen has written more than 100 referred professional publications and co-authored and co-edited seven books on clinical trial methodology, meta-analysis and public health applications and he has been invited nationally and internationally to give presentations on his research. In 2014 Professor Chen received the "Award of Recognition" from the Deming Conference Committee for his contribution to highly successful advanced biostatistics workshop tutorials with his books. In 2013, he was invited to give a short course at the twentieth Annual Biopharmaceutical Applied Statistics Symposium (BASS XX, 2013) for his contribution in meta-analysis and received a "Plaque of Honor" for his short course. 

Professor Jiahua Chen is a Canada Research Chair, Tier I at the Department of Statistics, University of British Columbia. He has made important and fundamental research contributions to the theory and application of mixture models, empirical likelihood, variable select, the theory of sampling and the design of experiments. He has published over 100 research papers. He is the elected fellow of the Institute of Mathematical Statistics and the American Statistical Association. He was the recipient of the Gold Medal of the Statistical Society of Canada in 2014.

Xuewen Lu is Professor of Statistics at the University of Calgary. His broad research interest lies in the areas of biostatistics, predictive microbiology models, survival analysis, theory of semiparametric models, high-dimensional data analysis, statistical computing, and applications of statistical methods in biological and medical sciences. He has published more than 80 research papers in both theoretical statistical and applied scientific journals, and co-edited a book on modeling microbial responses in food.  He has served on the editorial boards for several statistical journals.

Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. Her broad research interests include measurement error models, missing data problems, high dimensional data analysis, survival data and longitudinal data analysis, estimating function and likelihood methods, and medical applications. Grace Y. Yi is a Fellow of the American Statistical Association, and an Elected Member of the International Statistical Institute. She is the editor of the Canadian Journal of Statistics (2016-2018). She is President of Biostatisitcs Section of Statistical Society of Canada in 2016, and the Founder and President of the first chapter (Canada Chapter) of International Chinese Statistical Association.

Hao Yu is a Professor of Statistical and Actuarial Sciences at the University of Western Ontario.  His primary specializations are in the fields of Stochastic Process Modeling, Nonlinear Time Series, High Performance Statistical Computing and Applications of Parallel Computation. Yu’s research in high performance computing includes the development of Rmpi package for R, which allows parallel computing running on the high level statistical software R. He was President of Probability Section of Statistical Society of Canada from 2011 to 2012.

Blurb

This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world.  It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.