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E-book
Author Chen, Hua Yun

Title Semiparametric Odds Ratio Model and Its Applications
Published Milton : CRC Press LLC, 2021

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Description 1 online resource (334 p.)
Contents Cover Page -- Half-Title Page -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- 1 Odds Ratio Parameter and Its Utilities -- 1.1 Relative risk and odds ratio parameter -- 1.2 Odds ratio parameters in a J × K contingency table -- 1.3 Odds ratio representations of conditional and joint distributions for a J × K contingency table -- 1.4 Maximum likelihood estimators of odds ratio parameters in a J × K contingency table -- 1.5 Link to logit model and logistic regression -- 1.6 Odds ratio parameters in stratified J × K tables -- 1.7 Common odds ratio parameter
1.8 Odds ratio representations for a J × K\times M contingency table -- 1.9 Summary and discussion -- 1.10 Exercises -- 2 Odds Ratio Function and Its Modeling -- 2.1 Odds ratio function -- 2.2 Odds ratio decomposition of density functions -- 2.3 Odds ratio representation of densities -- 2.4 Conditional odds ratio function -- 2.5 Odds ratio representation of a joint conditional density -- 2.6 Odds ratio representation of a complex joint density -- 2.7 Relationship between conditional and unconditional odds ratio functions -- 2.8 Hierarchical odds ratio representation for a joint density
2.9 Odds ratio functions embedding in a density function -- 2.10 Modeling odd ratio functions in a family of densities -- 2.11 Semiparametric odds ratio model -- 2.12 Extension to relax the positivity assumption for the odds ratio representation -- 2.13 Literature on odds ratio functions and relevant statistical models -- 2.14 Exercises -- 3 Estimation and Inference on Semiparametric Odds Ratio Model -- 3.1 An introduction to likelihood-based approaches -- 3.2 Pseudo-likelihood approaches -- 3.2.1 Pairwise and group-wise pseudo-likelihood approaches -- 3.2.2 Asymptotic theory for U-statistics
3.2.3 Asymptotic distributions of the pseudo-likelihood estimators -- 3.3 Permutation likelihood approach -- 3.3.1 Approximations using simple Monte Carlo or asymptotics -- 3.3.2 Adaptive Monte Carlo approximation to permutation likelihood -- 3.3.3 Metropolis algorithm for sampling permutations for estimation -- 3.3.4 Permutation likelihood for the joint model -- 3.4 Maximum semiparametric likelihood approach -- 3.4.1 Maximum likelihood estimator for one group of outcomes -- 3.4.2 Computation of the maximum likelihood estimator -- 3.4.3 Maximum likelihood estimator for two groups of outcomes
3.4.4 Maximum likelihood for more than two groups of outcomes -- 3.4.5 Large sample behavior of the maximum likelihood estimator -- 3.5 Comparison of different likelihood approaches -- 3.6 The R package SPORM for semiparametric odds ratio model -- 3.7 Simulation study using SPORM -- 3.7.1 Univariate outcome -- 3.7.2 Multivariate outcomes as a group -- 3.7.3 Multivariate outcomes -- 3.8 Data analysis using SPORM -- 3.9 Exercises -- 4 Estimation and Inference on Conditional Odds Ratio Function -- 4.1 A general formulation of the problem -- 4.2 Permutation approach for stratified sample
Notes Description based upon print version of record
4.2.1 Permutation likelihood approach
Form Electronic book
ISBN 9781351049733
1351049739