Advanced Statistical Methods in Data Science, 1st ed. 2016
ICSA Book Series in Statistics Series

Language: English

105.49 €

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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.

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.
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 biostatis

Written by experts who are engaged in advanced statistical modeling in big-data sciences

Includes timely discussions and presentations on methodological development and real applications

Introduces publicly available data and computer programs to replicate the model development

Offers new methods that are readily adoptable and extendable