Exact maximum likelihood for incomplete data from a correlated gaussian process
- 1 January 1984
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics - Theory and Methods
- Vol. 13 (10) , 1275-1288
- https://doi.org/10.1080/03610928408828754
Abstract
In many situations in which a variable is measured at locations in time or space the observed data can be regarded as incomplete, the missing data sites perhaps completing a regular pattern such as a rectangular grid. In this paper general methods not dependent on the sequential nature of time are considered for estimating the parameters of Gaussian processes. An example is given.Keywords
This publication has 18 references indexed in Scilit:
- Maximum likelihood estimation of models for residual covariance in spatial regressionBiometrika, 1984
- The likelihood function for a stationary Gaussian autoregressive-moving average process with missing observationsBiometrika, 1982
- Parameter estimation for a stationary process on a d-dimensional latticeBiometrika, 1982
- Optimum Balanced Block and Latin Square Designs for Correlated ObservationsThe Annals of Statistics, 1981
- Missing observations in time seriesCommunications in Statistics - Theory and Methods, 1981
- Maximum Likelihood Fitting of ARMA Models to Time Series With Missing ObservationsTechnometrics, 1980
- A subclass of lattice processes applied to a problem in planar samplingBiometrika, 1979
- A Monte Carlo study of autoregressive integrated moving average processesJournal of Econometrics, 1978
- The Moving Average Model for Spatial InteractionTransactions of the Institute of British Geographers, 1978
- Interpolating Time Series with Application to the Estimation of Holiday Effects on Electricity DemandJournal of the Royal Statistical Society Series C: Applied Statistics, 1976