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.

Inhalt
On the estimation of ARIMA Models with missing values.- Statistical inference for irregularly observed processes.- Large sample properties of estimation in time series observed at unequally spaced times.- Time series regression with periodically correlated errors and missing data.- Missing observations in dynamic econometric models: a partial synthesis.- A Hilbert transform method for estimating distributed lag models with randomly missed or distorted observations.- Fitting multivariate models to unequally spaced data.- State space modeling of nonstationary time series and smoothing of unequally spaced data.- Direct quadratic spectrum estimation with irregularly spaced data.- Spectral and probability density estimation from irregularly observed data.- A strategy to complete a time series with missing observations.- Multiple time series analysis or irregularly spaced data.- Some applications of the EM algorithm to analyzing incomplete time series data.- Some aspects of continuous-discrete time series modelling.- Inferring the attainment of national ambient air quality standards using missing value time series techniques.- The complementary model in continuous/discrete smoothing.