| ISBN： | 9789811048548 : |
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| 编目源： | N$T N$T N$T GW5XE EBLCP OCLCF SHCD |
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| 语种代码： | eng |
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| 个人名称： | Qin, Jing |
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| 题名： | Biased sampling, over-identified parameter problems and beyond / / Jing Qin. |
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| 出版发行项： | Singapore : Springer, [2017] |
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| 载体形态： | xvi, 624 p ; ; 24 cm. |
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| 格式化内容附注： | Preface; Acknowledgements; Contents; 1 Examples and Basic Theories for Length Biased Sampling Problems; 1.1 Length Biased Sampling Examples; 1.2 Basic Properties of Length Biased Sampling Problems; 1.3 Stochastic Ordering; 1.4 Lorenz Curve; 1.5 Characterization of Length Biased Distribution; 2 Brief Introduction of Renewal Process; 2.1 Basic Concepts; 2.2 Forward and Backward Recurrence Times; 2.3 Basic Results on Poisson Process; 3 Heuristical Introduction of General Biased Sampling with Various Applications ; 3.1 Natural Selection Biased Sampling Problems |
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| 格式化内容附注： | 3.2 Modelling Based Selection Biased Sampling Problems4 Brief Review of Parametric Likelihood Inferences; 4.1 Kullback -- Leibler Information and Entropy Concepts; 4.2 Issues in Maximum Likelihood Estimation; 4.3 Popular Inference Methods in the Presence of Nuisance Parameters; 4.4 Quasi-likelihood Methods in Linear Regression Models; 4.5 Composite Likelihoods and Corrected Likelihoods; 4.6 Variable Selection and Akaike Criterion; 4.7 Two Useful Maximization Algorithms; 4.8 Likelihood Based Inference with Inequality Constraints; 5 Optimal Estimating Function Theory |
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| 格式化内容附注： | 5.1 Godambe's Optimality Criterion5.2 Applications of Godambe's Theory in Missing Covariate Problems; 5.3 Godambe's Theory in Length Biased Sampling AFT Models; 5.4 Ancillarity and Fisher Information with Nuisance Parameters; 5.5 Projection Methods in Parametric Models; 5.6 Reduce Sensitivity with Respect to Nuisance Parameters; 6 Projection Methods in General Semiparametric Models; 6.1 Projection Method for the Mean Estimation and Linear Regression Model; 6.2 Information Contained in the Conditional Expectation Model; 6.3 Projection Method in a Two Sample Density Ratio Model |
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| 格式化内容附注： | 6.4 Information Calculation in Over-identified Semiparametric Models6.5 Information Calculation for Missing Data Problems; 6.6 A Non-root n Consistent Estimator Example; 7 Generalized Method of Moments; 7.1 Basic Concepts on Generalized Method of Moments; 7.2 An Optimal Result Based on an Embed Exponential Family; 7.3 Applications of GMM; 8 Empirical Likelihood with Applications; 8.1 Definition of Empirical Likelihood and Basic Properties; 8.2 General Theory of Empirical Likelihood in Estimating Equations; 8.3 Miscellaneous Topics on Empirical Likelihood |
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| 格式化内容附注： | 8.4 Hybrid Likelihoods and Utilization Auxiliary Information8.5 Combine Summarized Information: A More Flexible Method in Meta Analysis; 9 Kullback -- Leibler Likelihood and Entropy Family ; 9.1 Minimize Kullback -- Leibler Divergence Subject to Moment Constraints; 9.2 Entropy Family in the Presence of Covariates; 9.3 Some Miscellaneous Results; 9.4 Entropy Family with Fixed Margins in Discrete Case; 9.5 Generalized Empirical Likelihoods; 9.6 Inference for Exponential Family with Specified Mean Function; 10 General Theory on Biased Sampling Problems. |
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