Time Series Analysis of Irregularly Observed Data
Lecture notes in statisticsPublished 1 January 1984
Emanuel Parzen
Citations127
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A disposable protective bib includes a disposable substantially rectangular sheet of soft flexible material having a hole and slit adapted to accommodate the user's neck adjacent one extremity and a transversely extending pocket member for catching spilled and dropped food adjacent the other extremity of the sheet.
Abstract
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
Keywords
Computer Science
Lecture notes in statisticsFitting Multivariate Models to Unequally Spaced Data
62 Citations1984Richard H. Jones
Using state space representations, the author’s previous work on fitting continuous time autoregressions to unequally spaced univariate data is extended to several multivariate models of practical importance, including optimal control when drug therapy is involved.
Lecture notes in statisticsSpectral and Probability Density Estimation From Irregularly Observed Data
25 Citations1984Elias Masry
Lecture notes in statisticsMissing Observations in Dynamic Econometric Models: A Partial Synthesis
21 Citations1984Andrew Harvey, Colin McKenzie
A number of methods for carrying out the maximum likelihood estimation of a dynamic econometric model with missing observations are examined and it is argued that in all cases the necessary computations can be carried out most efficiently by putting the model in state space form and applying the Kalman filter.
Lecture notes in statisticsStatistical Inference for Irregularly Observed Processes
19 Citations1984David R. Brillinger
Various writers have set down block diagrams illustrating how scientific enquiry proceeds and how statistics impinges on that process.
Lecture notes in statisticsA Strategy to Complete a Time Series with Missing Observations
16 Citations1984Robert B. Miller, Osvaldo Ferreiro
Lecture notes in statisticsSome Applications of the EM Algorithm to Analyzing Incomplete Time Series Data
16 Citations1984Robert H. Shumway
The EM algorithm is reviewed here within the time series context and applied to the parameter estimation and smoothing problem for missing data state-space models and linear estimation (deconvolution) in a frequency domain regression model.
Lecture notes in statisticsDirect Quadratic Spectrum Estimation with Irregularly Spaced Data
10 Citations1984Donald W. Marquardt, Sherry K. Acuff
The Direct Quadratic Spectrum Estimation method is versatile in handling data that have irregular spacing or missing values; the method is computationally stable, is robust to isolated outlier observations in irregularly spaced data, is capable of fine frequency resolution, makes maximum use of all available data, and is easy to implement on a computer.
Lecture notes in statisticsState Space Modeling of Nonstationary Time Series and Smoothing of Unequally Spaced Data
7 Citations1984Genshiro Kitagawa
Lecture notes in statisticsThe Complementary Model in Continuous/Discrete Smoothing
5 Citations1984Howard L. Weinert
This work considers the problem of smoothing a continuous-time random process using irregularly spaced noisy samples to derive the Hamiltonian system of the random process generated by a linear state model.
Lecture notes in statisticsSome Aspects of Continuous-Discrete Time Series Modelling
4 Citations1984Victor Solo
By emphasizing how rational spectrum models of time series can be parameterized by means of covariances a discussion of the aliasing problem is obtained and this “covariance” parameterization is also well suited to likelihood construction and generation of interpolates, derivatives and forecasts.
Lecture notes in statisticsTime Series Regression with Periodically Correlated Errors and Missing Data
4 Citations1984William T. M. Dunsmuir
Lecture notes in statisticsA Hilbert Transform Method for Estimating Distributed Lag Models With Randomly Missed or Distorted Observations
3 Citations1984Melvin J. Hinich, Warren E. Weber
Lecture notes in statisticsInferring the Attainment of National Ambient Air Quality Standards using Missing Value Time Series Techniques
1 Citations1984Anthony D. Thrall, C.S. Burton
