Time Series Analysis of Irregularly Observed Data

Proceedings of a Symposium held at Texas A & M University, College Station, Texas February 10–13, 1983

  • Emanuel Parzen
Conference proceedings

Part of the Lecture Notes in Statistics book series (LNS, volume 25)

Table of contents

  1. Front Matter
    Pages N2-viii
  2. Emanuel Parzen
    Pages 1-8
  3. Craig F. Ansley, Robert Kohn
    Pages 9-37
  4. Donald W. Marquardt, Sherry K. Acuff
    Pages 211-223
  5. Robert B. Miller, Osvaldo Ferreiro
    Pages 251-275

About these proceedings


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

Editors and affiliations

  • Emanuel Parzen
    • 1
  1. 1.Department of StatisticsTexas A & M UniversityCollege StationUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 1984
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-96040-1
  • Online ISBN 978-1-4684-9403-7
  • Series Print ISSN 0930-0325
  • Buy this book on publisher's site
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