Advanced Statistical Methods in Data Science / edited by Ding-Geng Chen, Jiahua Chen, Xuewen Lu, Grace Y. Yi, Hao Yu.

Format:

Book

Contributor:

Chen, Ding-Geng, Editor.

Chen, Jiahua, Editor.

Lu, Xuewen, Editor.

Yi, Grace Y., Editor.

Yu, Hao, Editor.

Series:

ICSA Book Series in Statistics, 2199-0999

Language:

English

Subjects (All):

Statistics.

Quantitative research.

Statistical Theory and Methods.

Data Analysis and Big Data.

Statistics in Business, Management, Economics, Finance, Insurance.

Local Subjects:

Statistical Theory and Methods.

Data Analysis and Big Data.

Statistics in Business, Management, Economics, Finance, Insurance.

Physical Description:

1 online resource (XVI, 222 p. 41 illus., 20 illus. in color.)

Edition:

1st ed. 2016.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2016.

Summary:

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 invitedthe 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.

Contents:

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.

Notes:

Includes bibliographical references at the end of each chapters and index.