Advanced Statistical Methods in Data Science. 1st ed. 2016

種類:

電子ブック

責任表示:

edited by Ding-Geng Chen, Jiahua Chen, Xuewen Lu, Grace Y. Yi, Hao Yu

出版情報:

Singapore : Springer Nature Singapore : Imprint: Springer, 2016

著者名:

Chen, Ding-Geng.

Chen, Jiahua.

Lu, Xuewen.

Yi, Grace Y.

Yu, Hao.

SpringerLink (Online service)

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シリーズ名:

ICSA Book Series in Statistics ;

ISBN:

9789811025945 [9811025940]    

注記:

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 Qualit
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 f

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