Time Series Analysis of Irregularly Observed Data: Proceedings of a Symposium Held at Texas A & M University, College Station, Texas February 10–13, 1983
Springer New York, Sep 5, 1984 - Mathematics - 363 pages
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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Parzen Emanuel Introduction
Hinich Helvin J and Weber Warren E A Hilbert transform
Jones Richard H Fitting multivariate models to unequally
7 other sections not shown
Other editions - View all
Time Series Analysis of Irregularly Observed Data: Proceedings of a ...
Limited preview - 2012
alias-free amplitude modulated applied approach approximate ARMA models assumed assumption asymptotic normality autoregressive Bayes estimators Brillinger central limit theorem computed conditional expectations consider consistent estimator continuous convergence covariance matrix defined denote derived diagonal discrete distribution Dunsmuir elements EM algorithm equally spaced equation example finite frequency Gaussian given independent interval irregularly observed irregularly spaced data iterations Jones Kalman filter likelihood function linear log-likelihood Masry maximum likelihood estimates method missing data missing observations missing values multivariate nonstationary º º º observational error obtained paper parameters Parzen periodogram point process problem procedure random recursions regression residuals Robinson sample standard errors sampling schemes Section Series Analysis series models signal space model space representation spectral analysis spectral density spectral estimates spectrum stationary process stationary time series Statist Stochastic Processes unequally spaced data univariate variables variance vector white noise zero