Time Series Analysis of Irregularly Observed Data

1. Front Matter

   Pages N2-viii

   PDF

2. Introduction

   • Emanuel Parzen

   Pages 1-8

3. On the estimation of ARIMA Models with Missing Values

   • Craig F. Ansley, Robert Kohn

   Pages 9-37

4. Statistical Inference for Irregularly Observed Processes

   • David R. Brillinger

   Pages 38-57

5. Large Sample Properties of Estimation in Time Series Observed at Unequally Spaced Times

   • W. Dunsmuir

   Pages 58-77

6. Time Series Regression with Periodically Correlated Errors and Missing Data

   • W. Dunsmuir

   Pages 78-107

7. Missing Observations in Dynamic Econometric Models: A Partial Synthesis

   • A. C. Harvey, C. R. McKenzie

   Pages 108-133

8. A Hilbert Transform Method for Estimating Distributed Lag Models With Randomly Missed or Distorted Observations

   • Melvin J. Hinich, Warren E. Weber

   Pages 134-157

9. Fitting Multivariate Models to Unequally Spaced Data

   • Richard H. Jones

   Pages 158-188

10. State Space Modeling of Nonstationary Time Series and Smoothing of Unequally Spaced Data

    • Genshiro Kitagawa

    Pages 189-210

11. Direct Quadratic Spectrum Estimation with Irregularly Spaced Data

    • Donald W. Marquardt, Sherry K. Acuff

    Pages 211-223

12. Spectral and Probability Density Estimation From Irregularly Observed Data

    • Elias Masry

    Pages 224-250

13. A Strategy to Complete a Time Series with Missing Observations

    • Robert B. Miller, Osvaldo Ferreiro

    Pages 251-275

14. Multiple Time Series Analysis of Irregularly Spaced Data

    • P. M. Robinson

    Pages 276-289

15. Some Applications of the EM Algorithm to Analyzing Incomplete Time Series Data

    • R. H. Shumway

    Pages 290-324

16. Some Aspects of Continuous-Discrete Time Series Modelling

    • Victor Solo

    Pages 325-345

17. Inferring the Attainment of National Ambient Air Quality Standards using Missing Value Time Series Techniques

    • Anthony D. Thrall, C. Shepherd Burton

    Pages 346-352

18. The Complementary Model in Continuous/Discrete Smoothing

    • Howard L. Weinert

    Pages 353-363

With the support of the Office of Naval Research Program on Statistics and Probability (Dr. Edward J. Wegman, Director), The Department of Statistics at Texas A&M University hosted a Symposium on Time Series Analysis of Irregularly Observed Data during the period February 10-13, 1983. The symposium aimed to provide a review of the state of the art, define outstanding problems for research by theoreticians, transmit to practitioners recently developed algorithms, and stimulate interaction between statisticians and researchers in subject matter fields. Attendance was limited to actively involved researchers. This volume contains refereed versions of the papers presented at the Symposium. We would like to express our appreciation to the many colleagues and staff members whose cheerful help made the Symposium a successful happening which was enjoyed socially and intellectually by all participants. I would like to especially thank Dr. Donald W. Marquardt whose interest led me to undertake to organize this Symposium. This volume is dedicated to the world wide community of researchers who develop and apply methods of statistical analysis of time series. r:;) \J Picture Caption Participants in Symposium on Time Series Analysis of Irregularly Observed Data at Texas A&M University, College Station, Texas, February 10-13, 1983 First Row: Henry L. Gray, D. W. Marquardt, P. M. Robinson, Emanuel Parzen, Julia Abrahams, E. Masry, H. L. Weinert, R. H. Shumway.

• Analysis
• Fitting
• Series
• Time
• Time series
• Zeitreihenanalyse
• best fit
• expectation–maximization algorithm

• Department of Statistics, Texas A & M University, College Station, USA

  Emanuel Parzen

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