Weighted Empirical Processes in Dynamic Nonlinear Models

1. Front Matter

   Pages i-xvii

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2. Introduction

   • Hira L. Koul

   Pages 1-14

3. Asymptotic Properties of W.E.P.’s

   • Hira L. Koul

   Pages 15-68

4. Linear Rank and Signed Rank Statistics

   • Hira L. Koul

   Pages 69-98

5. M, R and Some Scale Estimators

   • Hira L. Koul

   Pages 99-137

6. Minimum Distance Estimators

   • Hira L. Koul

   Pages 138-228

7. Goodness-of-fit Tests in Regression

   • Hira L. Koul

   Pages 229-293

8. Autoregression

   • Hira L. Koul

   Pages 294-357

9. Nonlinear Autoregression

   • Hira L. Koul

   Pages 358-407

10. Appendix

    • Hira L. Koul

    Pages 408-413

11. Bibliography

    • Hira L. Koul

    Pages 414-425

12. Back Matter

    Pages 427-429

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The role of the weak convergence technique via weighted empirical processes has proved to be very useful in advancing the development of the asymptotic theory of the so called robust inference procedures corresponding to non-smooth score functions from linear models to nonlinear dynamic models in the 1990's. This monograph is an ex­ panded version of the monograph Weighted Empiricals and Linear Models, IMS Lecture Notes-Monograph, 21 published in 1992, that includes some aspects of this development. The new inclusions are as follows. Theorems 2. 2. 4 and 2. 2. 5 give an extension of the Theorem 2. 2. 3 (old Theorem 2. 2b. 1) to the unbounded random weights case. These results are found useful in Chapters 7 and 8 when dealing with ho­ moscedastic and conditionally heteroscedastic autoregressive models, actively researched family of dynamic models in time series analysis in the 1990's. The weak convergence results pertaining to the partial sum process given in Theorems 2. 2. 6 . and 2. 2. 7 are found useful in fitting a parametric autoregressive model as is expounded in Section 7. 7 in some detail. Section 6. 6 discusses the related problem of fit­ ting a regression model, using a certain partial sum process. Inboth sections a certain transform of the underlying process is shown to provide asymptotically distribution free tests. Other important changes are as follows. Theorem 7. 3.

• boundary element method
• estimator
• fitting
• probability
• statistics

• Department of Statistics and Probability, Michigan State University, East Lansing, USA

  Hira L. Koul

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