Biased sampling, over-identified parameter problems and beyond /
|Author / Creator:||Qin, Jing, author.|
|Imprint:||Cham : Springer, 2017.|
|Description:||1 online resource|
|Series:||ICSA book series in statistics|
ICSA book series in statistics.
Economic theory & philosophy.
Probability & statistics.
MATHEMATICS -- Probability & Statistics -- General.
|URL for this record:||http://pi.lib.uchicago.edu/1001/cat/bib/11307989|
Table of Contents:
- 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.
- 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.
- 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.
- 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.
- 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.