Empirical likelihood methods in biomedicine and health

Empirical likelihood methods in biomedicine and health /

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Bibliographic Details
Main Authors: Vexler, Albert (Author), Yu, Jihnhee (Author)
Corporate Author: ProQuest (Firm)
Format: Electronic eBook
Language:English
Published: Boca Raton, FL : CRC Press, Taylor & Francis Group, [2019]
Subjects:
Medical informatics > Research > Methodology.
Bioethics > Research > Methodology.
Medical Informatics.
Research Design.
Bioethics.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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020 |a 1351001507 (electronic bk.) 
020 |z 9781466555037 
020 |z 1466555033 
035 |a (NhCcYBP)ebc5495892 
040 |a NhCcYBP  |c NhCcYBP 
050 4 |a R858  |b .V49 2019 
060 4 |a W 26.5 
082 0 4 |a 610.285072  |2 23 
100 1 |a Vexler, Albert,  |e author. 
245 1 0 |a Empirical likelihood methods in biomedicine and health /  |c Albert Vexler, Jihnhee Yu. 
264 1 |a Boca Raton, FL :  |b CRC Press, Taylor & Francis Group,  |c [2019] 
300 |a 1 online resource (xviii, 299 pages.) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
500 |a "A Chapman & Hall book" -- title page. 
504 |a Includes bibliographical references and index. 
505 0 0 |a Machine generated contents note:   |g 1.  |t Preliminaries --   |g 1.1.  |t Overview: From Statistical Hypotheses to Types of Information for Constructing Statistical Tests --   |g 1.2.  |t Parametric Approach --   |g 1.3.  |t Warning---Parametric Approach and Detour: Nonparametric Approach --   |g 1.4.  |t Brief Ode to Likelihood --   |g 1.4.1.  |t Likelihood Ratios and Optimality --   |g 1.4.2.  |t Likelihood Ratio Based on the Likelihood Ratio Test Statistic Is the Likelihood Ratio Test Statistic --   |g 1.5.  |t Maximum Likelihood: Is It the Likelihood? --   |g 1.5.  |t Empirical Likelihood --   |g 1.7.  |t Why Empirical Likelihood? --   |g 1.7.1.  |t Necessity and Danger of Testing Statistical Hypothesis --   |g 1.7.2.  |t Three Sources That Support the Empirical Likelihood Methodology for Applying in Practice --   |t Appendix --   |g 2.  |t Basic Ingredients of the Empirical Likelihood --   |g 2.1.  |t Introduction --   |g 2.2.  |t Classical Empirical Likelihood Methods --   |g 2.3.  |t Techniques for Analyzing Empirical Likelihoods --   |g 2.3.1.  |t Illustrative Comparison of Empirical Likelihood and Parametric Likelihood --   |g 2.4.  |t In Case of the Presence of Extra Estimating Equation Information --   |g 2.4.1.  |t Sketch of the Proof of Equation (2.9) --   |g 2.5.  |t Some Helpful Properties --   |g 2.6.  |t Density-Based Empirical Likelihood Methods --   |g 2.7.  |t Flexible Likelihood Approach Using Empirical Likelihood --   |g 2.8.  |t Bayesians and Empirical Likelihood: Are They Mutually Exclusive? --   |g 2.8.1.  |t Nonparametric Posterior Expectations of Simple Functionals --   |g 2.9.  |t Bartlett Correction --   |g 2.10.  |t Empirical Likelihood in a Class of Empirical Goodness of Fit Tests --   |g 2.11.  |t Empirical Likelihood as a Competitor of the Bootstrap --   |g 2.13.  |t Convex Hull --   |g 2.14.  |t Empirical Likelihood with Plug-In Estimators --   |g 2.15.  |t Implementation of Empirical Likelihood Using R --   |t Appendix --   |g 3.  |t Empirical Likelihood in Light of Nonparametric Bayesian Inference --   |g 3.1.  |t Introduction --   |g 3.2.  |t Posterior Expectation Incorporating Empirical Likelihood --   |g 3.2.1.  |t Nonparametric Posterior Expectations of Simple Functionals --   |g 3.2.2.  |t Nonparametric Posterior Expectations of General Functionals --   |g 3.2.3.  |t Nonparametric Analog of James-Stein Estimation --   |g 3.2.4.  |t Performance of the Empirical Likelihood Bayesian Estimators --   |g 3.3.  |t Confidence Interval Estimation with Adjustment for Skewed Data --   |g 3.3.1.  |t Data-Driven Equal-Tailed CI Estimation --   |g 3.3.2.  |t Data-Driven Highest Posterior Density CI Estimation --   |g 3.3.3.  |t General Cases for CI Estimation --   |g 3.3.4.  |t Performance of the Empirical Likelihood Bayesian CIs --   |g 3.3.5.  |t Strategy to Analyze Real Data --   |g 3.4.  |t Some Warnings --   |g 3.5.  |t Example of the Use of Empirical Likelihood-Based Bayes Factors in the Bayesian Manner --   |g 3.6.  |t Concluding Remarks --   |t Appendix --   |g 4.  |t Empirical Likelihood for Probability Weighted Moments --   |g 4.1.  |t Introduction --   |g 4.2.  |t Incorporating the Empirical Likelihood for βr --   |g 4.2.1.  |t Estimators of the Probability Weighted Moments --   |g 4.2.2.  |t Empirical Likelihood Inference for βr --   |g 4.2.3.  |t Scheme to Implement the Empirical Likelihood Ratio Technique --   |g 4.2.4.  |t Application to the Gini Index --   |g 4.3.  |t Performance Comparisons --   |g 4.4.  |t Data Example --   |g 4.5.  |t Concluding Remarks --   |t Appendix --   |g 5.  |t Two-Group Comparison and Combining Likelihoods Based on Incomplete Data --   |g 5.1.  |t Introduction --   |g 5.2.  |t Product of Likelihood Functions Based on the Empirical Likelihood --   |g 5.3.  |t Classical Empirical Likelihood Tests to Compare Means --   |g 5.3.1.  |t Implementation in R --   |g 5.3.2.  |t Implementation Using Available R Packages --   |g 5.4.  |t Classical Empirical Likelihood Ratio Tests to Compare Multivariate Means --   |g 5.4.1.  |t Profile Analysis --   |g 5.5.  |t Product of Likelihood Functions Based on the Empirical Likelihood --   |g 5.5.1.  |t Product of Empirical Likelihood and Parametric Likelihood --   |g 5.5.1.1.  |t Implementation in R --   |g 5.5.2.  |t Product of the Empirical Likelihoods --   |g 5.5.2.1.  |t Implementation in R (Continued from Section 5.5.1.1) --   |g 5.6.  |t Concluding Remarks --   |t Appendix --   |g 6.  |t Quantile Comparisons --   |g 6.1.  |t Introduction --   |g 6.2.  |t Existing Nonparametric Tests to Compare Location Shifts --   |g 6.3.  |t Empirical Likelihood Tests to Compare Location Shifts --   |g 6.3.1.  |t Plug-in Approach --   |g 6.4.  |t Computation in R --   |g 6.5.  |t Constructing Confidence Intervals on Quantile Differences --   |g 6.6.  |t Concluding Remarks --   |t Appendix --   |g 7.  |t Empirical Likelihood for a U-Statistic Constraint --   |g 7.1.  |t Introduction --   |g 7.2.  |t Empirical Likelihood Statistic for a U-Statistics Constraint --   |g 7.3.  |t Two-Sample Setting --   |g 7.4.  |t Various Applications --   |g 7.4.1.  |t Receiver Operating Characteristic Curve Analysis --   |g 7.4.1.1.  |t R Code --   |g 7.4.2.  |t Generalization for Comparing Two Correlated AUC Statistics --   |g 7.4.2.1.  |t Implementation in R --   |g 7.4.3.  |t Comparison of Two Survival Curves --   |g 7.4.3.1.  |t R Code --   |g 7.4.4.  |t Multivariate Rank-Based Tests --   |g 7.4.4.1.  |t AAA Implementation in R --   |g 7.4.4.2.  |t Comments on the Performance of the Empirical Likelihood Ratio Statistics --   |g 7.5.  |t Application to Crossover Designs --   |g 7.5.  |t Concluding Remarks --   |t Appendix --   |g 8.  |t Empirical Likelihood Application to Receiver Operating Characteristic Curve Analysis --   |g 8.1.  |t Introduction --   |g 8.2.  |t Receiver Operating Characteristic Curve --   |g 8.3.  |t Area under the Receiver Operating Characteristic Curve --   |g 8.4.  |t Nonparametric Comparison of Two Receiver Operating Characteristic Curves --   |g 8.5.  |t Best Combinations Based on Values of Multiple Biomarkers --   |g 8.6.  |t Partial Area under the Receiver Operating Characteristic Curve --   |g 8.6.1.  |t Alternative Expression of the pAUC Estimator for the Variance Estimation --   |g 8.6.2.  |t Comparison of Two Correlated pAUC Estimates --   |g 8.6.3.  |t Empirical Likelihood Approach Based on the Proposed Variance Estimator --   |g 8.7.  |t Concluding Remarks --   |t Appendix --   |g 9.  |t Various Topics --   |g 9.1.  |t Introduction --   |g 9.2.  |t Various Regression Approaches --   |g 9.2.1.  |t General Framework --   |g 9.2.2.  |t Analyzing Longitudinal Data --   |g 9.2.3.  |t Application to the Longitudinal Partial Linear Regression Model --   |g 9.2.4.  |t Empirical Likelihood Approach for Marginal Likelihood Functions --   |g 9.2.5.  |t Empirical Likelihood in the Linear Regression Framework with Surrogate Covariates --   |g 9.3.  |t Empirical Likelihood Based on Censored Data --   |g 9.3.1.  |t Testing the Hazard Function --   |g 9.3.2.  |t Estimating the Quantile Function --   |g 9.3.3.  |t Testing the Mean Survival Time --   |g 9.3.4.  |t Mean Quality-Adjusted Lifetime with Censored Data --   |g 9.3.5.  |t Regression Approach for the Censored Data --   |g 9.4.  |t Empirical Likelihood with Missing Data --   |g 9.4.1.  |t Fully Observed Data Case --   |g 9.4.2.  |t Using Imputation --   |g 9.4.3.  |t Incorporating Missing Probabilities --   |g 9.4.4.  |t Missing Covariates --   |g 9.5.  |t Empirical Likelihood in Survey Sampling --   |g 9.5.1.  |t Pseudo-Empirical Log-Likelihood Approach --   |g 9.5.2.  |t Many Zero Values Problem in Survey Sampling. 
533 |a Electronic reproduction.  |b Ann Arbor, MI  |n Available via World Wide Web. 
588 |a Description based on print version record. 
650 0 |a Medical informatics  |x Research  |x Methodology. 
650 0 |a Bioethics  |x Research  |x Methodology. 
650 2 |a Medical Informatics. 
650 2 |a Research Design. 
650 2 |a Bioethics. 
700 1 |a Yu, Jihnhee,  |e author. 
710 2 |a ProQuest (Firm) 
776 0 8 |c Original  |z 9781466555037  |z 1466555033  |w (DLC) 2017060382 
856 4 0 |u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=5495892  |z Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)  |t 0 
907 |a .b31784549  |b 200401  |c 181001 
998 |a uww  |b    |c m  |d z   |e l  |f eng  |g flu  |h 0 
917 |a YBP DDA 
919 |a .ulebk  |b 2017-02-14 
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