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000857467 019__ $$a1080599921
000857467 020__ $$a9783319993898$$q(electronic book)
000857467 020__ $$a3319993895$$q(electronic book)
000857467 020__ $$z9783319993881
000857467 0247_ $$a10.1007/978-3-319-99389-8$$2doi
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000857467 035__ $$aSP(OCoLC)1078921940$$z(OCoLC)1080599921
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000857467 049__ $$aISEA
000857467 050_4 $$aQH323.5
000857467 08204 $$a570.1/5195$$223
000857467 1112_ $$aWorkshop on Biostatistics and Bioinformatics$$n(5th :$$d2017 :$$cAtlanta, Ga.)
000857467 24510 $$aNew frontiers of biostatistics and bioinformatics /$$cYichuan Zhao, Ding-Geng Chen, editors.
000857467 264_1 $$aCham, Switzerland :$$bSpringer,$$c2018.
000857467 300__ $$a1 online resource (xxiv, 463 pages) :$$billustrations.
000857467 336__ $$atext$$btxt$$2rdacontent
000857467 337__ $$acomputer$$bc$$2rdamedia
000857467 338__ $$aonline resource$$bcr$$2rdacarrier
000857467 347__ $$atext file$$bPDF$$2rda
000857467 4901_ $$aICSA book series in statistics,$$x2199-0980
000857467 500__ $$aIncludes index.
000857467 5050_ $$aIntro; Preface; Part I: Review and Theoretical Framework in Biostatistics (Chaps. 1 -4); Part II: Wavelet-Based Approach for Complex Data (Chaps. 5 -8); Part III: Clinical Trials and Statistical Modeling (Chaps. 9 -14); Part IV: High-Dimensional Gene Expression Data Analysis (Chaps. 15 -18); Part V: Survival Analysis (Chaps. 19 -22); Contents; List of Contributors; List of Chapter Reviewers; About the Editors; Part I Review of Theoretical Framework in Biostatistics; 1 Optimal Weighted Wilcoxon-Mann-Whitney Testfor Prioritized Outcomes; 1.1 Introduction
000857467 5058_ $$a1.2 Wilcoxon-Mann-Whitney Test for Prioritized Endpoints1.2.1 Notation; 1.2.2 Wilcoxon-Mann-Whitney Test; 1.2.3 Weighted Wilcoxon-Mann-Whitney Test; 1.2.3.1 Prespecified Weights; 1.2.3.2 Optimal Weights; 1.3 Simulation Studies; 1.4 Application to a Stroke Clinical Trial; 1.5 Discussion; Appendix; Appendix 1: Proof of Theorem 1.1; Appendix 2: Mean and Variance of the Weighted U-Statistic; Appendix 3: Optimal Weights; Appendix 4: Conditional Probabilities; Exponential Distribution; Normal Distribution; References; 2 A Selective Overview of Semiparametric Mixtureof Regression Models
000857467 5058_ $$a2.1 Introduction2.2 Mixture of Regression Models with Varying Proportions; 2.2.1 Continuous Response, p=1; 2.2.2 Continuous Response, p>1; 2.2.3 Discrete Response; 2.3 Nonparametric Errors; 2.3.1 Semiparametric EM Algorithm with Kernel Density Error; 2.3.2 Log-Concave Density Error; 2.3.3 Mixtures of Quantile Regressions; 2.4 Semiparametric Mixture of Nonparametric Regressions; 2.4.1 Nonparametric Mixture of Regressions; 2.4.2 Nonparametric Component Regression Functions; 2.4.3 Mixture of Regressions with Single-Index; 2.5 Semiparametric Regression Models for Longitudinal/Functional Data
000857467 5058_ $$a2.5.1 Mixture of Time-Varying Effects for Intensive Longitudinal Data2.5.2 Mixtures of Gaussian Processes; 2.5.3 Mixture of Functional Linear Models; 2.6 Some Additional Topics; 2.7 Discussion; References; 3 Rank-Based Empirical Likelihood for Regression Modelswith Responses Missing at Random; 3.1 Introduction; 3.2 Imputation; 3.2.1 Imputation Under MAR; 3.2.2 Empirical Likelihood Method; 3.3 Simulation Study; 3.3.1 Simulation Settings; 3.3.2 Real Data; 3.4 Conclusion; Appendix; Assumptions; References; 4 Bayesian Nonparametric Spatially Smoothed Density Estimation; 4.1 Introduction
000857467 5058_ $$a4.2 The Predictive Model4.2.1 Markov Chain Monte Carlo; 4.2.2 Censored Data; 4.2.3 Direct Estimation and a Permutation Test p-Value; 4.3 Examples; 4.3.1 IgG Distribution Evolving with Age; 4.3.2 Time to Infection in Amphibian Populations; 4.3.3 Simulated Data; 4.4 Conclusion; References; Part II Wavelet-Based Approach for Complex Data; 5 Mammogram Diagnostics Using Robust Wavelet-Based Estimator of Hurst Exponent; 5.1 Introduction; 5.2 Background; 5.2.1 Non-decimated Wavelet Transforms; 5.2.2 The fBm: Wavelet Coefficients and Spectra; 5.3 General Trimean Estimators
000857467 506__ $$aAccess limited to authorized users.
000857467 520__ $$aThis book is comprised of presentations delivered at the 5th Workshop on Biostatistics and Bioinformatics held in Atlanta on May 5-7, 2017. Featuring twenty-two selected papers from the workshop, this book showcases the most current advances in the field, presenting new methods, theories, and case applications at the frontiers of biostatistics, bioinformatics, and interdisciplinary areas. Biostatistics and bioinformatics have been playing a key role in statistics and other scientific research fields in recent years. The goal of the 5th Workshop on Biostatistics and Bioinformatics was to stimulate research, foster interaction among researchers in field, and offer opportunities for learning and facilitating research collaborations in the era of big data. The resulting volume offers timely insights for researchers, students, and industry practitioners. 
000857467 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 14, 2018).
000857467 650_0 $$aBiometry$$vCongresses.
000857467 650_0 $$aBioinformatics$$vCongresses.
000857467 7001_ $$aZhao, Yichuan,$$eeditor.
000857467 7001_ $$aChen, Ding-Geng,$$eeditor.
000857467 77608 $$iPrint version: $$z9783319993881
000857467 830_0 $$aICSA book series in statistics.
000857467 852__ $$bebk
000857467 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-99389-8$$zOnline Access$$91397441.1
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