【數(shù)據(jù)科學(xué)的高階統(tǒng)計(jì)學(xué)方法】 Advanced Statistical Methods in Data Science (2016)

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Advanced Statistical Methods in Data Science

Editors: Ding-Geng Chen, Jiahua Chen, Xuewen Lu, Grace Y. Yi, Hao Yu



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

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.

Table of contents

Front Matter

Data Analysis Based on Latent or Dependent Variable Models
• Front Matter
• The Mixture Gatekeeping Procedure Based on Weighted Multiple Testing Correction for Correlated Tests
• Regularization in Regime-Switching Gaussian Autoregressive Models
• Modeling Zero Inflation and Overdispersion in the Length of Hospital Stay for Patients with Ischaemic Heart Disease
• Robust Optimal Interval Design for High-Dimensional Dose Finding in Multi-agent Combination Trials

Life Time Data Analysis
• Front Matter
• Group Selection in Semiparametric Accelerated Failure Time Model
• A Proportional Odds Model for Regression Analysis of Case I Interval-Censored Data
• Empirical Likelihood Inference Under Density Ratio Models Based on Type I Censored Samples: Hypothesis Testing and Quantile Estimation
• Recent Development in the Joint Modeling of Longitudinal Quality of Life Measurements and Survival Data from Cancer Clinical Trials

Applied Data Analysis
• Front Matter
• Confidence Weighting Procedures for Multiple-Choice Tests
• Improving the Robustness of Parametric Imputation
• Maximum Smoothed Likelihood Estimation of the Centre of a Symmetric Distribution
• Modelling the Common Risk Among Equities: A Multivariate Time Series Model with an Additive GARCH Structure

Back Matter

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cmwei333 發(fā)表于 2016-12-6 04:15
Advanced Statistical Methods in Data Science

Editors: Ding-Geng Chen, Jiahua Chen, Xuewen Lu, Gra ...

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