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书名：	Frontiers in Statistics
作者：	Koul, Hira L.
出版时间：	2006-07-17
ISBN：	9781860946707(P-ISBN) ，9781860948886(O-ISBN)
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书目详情：

Contents 1. Our Steps on the Bickel Way 1.1 Introduction 1.2 Doing Well at a Point and Beyond 1.3 Robustness Transformations Oracle-free Inference and Stable Parameters 1.4 Distribution Free Tests Higher Order Expansions and Challenging Projects 1.5 From Adaptive Estimation to Semiparametric Models 1.6 Hidden Markov Models 1.7 Non- and Semi-parametric Testing 1.8 The Road to Real Life References Bickels Publication Part I. Semiparametric Modeling 2. Semiparametric Models: A Review of Progress since BKRW 1993)2.1 Introduction 2.2 Missing Data Models 2.3 Testing and Profile Likelihood Theory 2.4 Semiparametric Mixture Model Theory 2.5 Rates of Convergence via Empirical Process Methods 2.6 Bayes Methods and Theory 2.7 Model Selection Methods 2.8 Empirical Likelihood 2.9 Transformation and Frailty Models 2.10 Semiparametric Regression Models 2.11 Extensions to Non-i.i.d. Data 2.12 Critiques and Possible Alternative Theories References 3. Efficient Estimator for Time Series 3.1 Introduction 3.2 Characterization of Efficient Estimators 3.3 Autoregression Parameter 3.4 Innovation Distribution 3.5 Innovation Density 3.6 Conditional Expectation 3.7 Stationary Distribution 3.8 Stationary Density 3.9 Transition Density References 4. On the Efficiency of Estimation for a Single-index Model 4.1 Introduction 4.2 Estimation via Outer Product of Gradients 4.3 Global Minimization Estimation Methods 4.4 Sliced Inverse Regression Method 4.5 Asymptotic Distributions 4.6 Comparisons in Some Special Cases 4.7 Proofs of the Theorems References 5. Estimating Function Based Cross-Validation 5.1 Introduction 5.2 Estimating Function Based Cross-Validation 5.3 Some Examples 5.4 General Finite Sample Result 5.5 Appendix References Part II. Nonparametric Methods 6. Powerful Choices: Tuning Parameter Selection Based on Power 6.1 Introduction: Local Testing and Asymptotic Power 6.2 Maximizing Asymptotic Power 6.3 Examples 6.4 Appendix References 7. Nonparametric Assessment of Atypicality 7.1 Introduction 7.2 Estimating Atypicality 7.3 Theoretical Properties 7.4 Numerical Properties 7.5 Outline of Proof of Theorem 7.1 References 8. Selective Review on Wavelets in Statistics 8.1 Introduction 8.2 Wavelets 8.3 Nonparametric Regression 8.4 Inverse Problems 8.5 Change-points 8.6 Local Self-similarity and Non-stationary Stochastic Process 8.7 Beyond Wavelets References 9. Model Diagnostics via Martingale Transforms: A Brief Review 9.1 Introduction 9.2 Lack-of-fit Tests 9.3 Censoring 9.4 Khamaladze Transform or Bootstrap References Part III. Statistical Learning and Bootstrap 10. Boosting Algorithms: with an Application to Bootstrapping Multivariate Time Series 10.1 Introduction 10.2 Boosting and Functional Gradient Descent 10.3 L2-Boosting for High-dimensional Multivariate Regression 10.4 L2-Boosting for Multivariate Linear Time Series References 11. Bootstrap Methods: A Review 11.1 Introduction 11.2 Bootstrap for i.i.d Data 11.3 Model Based Bootstrap 11.4 Block Bootstrap 11.5 Sieve Bootstrap 11.6 Transformation Based Bootstrap 11.7 Bootstrap for Markov Processes 11.8 Bootstrap under Long Range Dependence 11.9 Bootstrap for Spatial Data References 12. An Expansion for a Discrete Non-Lattice Distribution 12.1 Introduction 12.2 Proof of Theorem 12.1 12.3 Evaluation of the Oscillatory Term References Part IV. Longitudinal Data Analysis 13. An Overview on Nonparametric and Semiparametric Techniques for Longitudinal Data 13.1 Introduction 13.2 Nonparametric Model with a Single Covariate 13.3 Partially Linear Models 13.4 Varying-Coefficient Models 13.5 An Illustration 13.6 Generalizations 13.7 Estimation of Covariance Matrix References 14. Regressing Longitudinal Response Trajectories on a Covariate 14.1 Introduction and Review 14.2 The Functional Approach to Longitudinal Responses 14.3 Predicting Longitudinal Trajectories from a Covariate 14.4 Illustrations References Part V. Statistics in Science and Technology 15. Statistical Physics and Statistical Computing: A Critical Link 15.1 MCMC Revolution and Cross-Fertilization 15.2 The Ising Model 15.3 The Swendsen-Wang Algorithm and Criticality 15.4 Instantaneous Hellinger Distance and Heat Capacity 15.5 A Brief Overview of Perfect Sampling 15.6 Hubers Bounding Chain Algorithm 15.7 Approximating Criticality via Coupling Time 15.8 A Speculation References 16. Network Tomography: A Review and Recent Developments 16.1 Introduction 16.2 Passive Tomography 16.3 Active Tomography 16.4 An Application 16.5 Concluding Remarks References Part VI. Financial Econometrics 17. Likelihood Inference for Diffusions: A Survey 17.1 Introduction 17.2 The Univariate Case 17.3 Multivariate Likelihood Expansions 17.4 Connection to Saddlepoint Approximations 17.5 An Example with Nonlinear Drift and Diffusion Specifications 17.6 An Example with Stochastic Volatility 17.7 Inference When the State is Partially Observed 17.8 Application to Specification Testing 17.9 Derivative Pricing Applications 17.10 Likelihood Inference for Diffusions under Nonstationarity References 18. Nonparametric Estimation of Production Efficiency 18.1 The Frontier Model 18.2 Envelope Estimators 18.3 Order-m Estimators 18.4 Conditional Frontier Models 18.5 Outlook References Part VII. Parametric Techniques and Inferences 19. Convergence and Consistency of Newtons Algorithm for Estimating Mixing Distribution 19.1 Introduction 19.2 Newtons Estimate of Mixing Distributions 19.3 Review of Newtons Result on Convergence 19.4 Convergence Results 19.5 Other Results 19.6 Simulation References 20. Mixed Models: An Overview 20.1 Introduction 20.2 Linear Mixed Models 20.3 Generalized Linear Mixed Models 20.4 Nonlinear Mixed Effects Models References 21. Robust Location and Scatter Estimators in Multivariate Analysis 21.1 Introduction 21.2 Robustness Criteria 21.3 Robust Multivariate Location and Scatter Estimators 21.4 Applications 21.5 Conclusions and Future Works References 22. Estimation of the Loss of an Estimate 22.1 Introduction 22.2 Kullback-Leibler Loss and Exponential Families 22.3 Mean Square Error Loss 22.4 Location Families 22.5 Approximate Solutions 22.6 Convergence of the Loss Estimate References Subject Index Author Index

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