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 -- Bickel's 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 Huber's 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 Newton's Algorithm for Estimating Mixing Distribution -- 19.1 Introduction -- 19.2 Newton's Estimate of Mixing Distributions -- 19.3 Review of Newton's 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.