Environmental time series analysis and forecasting with the Captain toolbox
Environmental Modelling & Software
Signal extraction and filtering by linear semiparametric methods
Computational Statistics & Data Analysis
Exact maximum likelihood estimation of structured or unit root multivariate time series models
Computational Statistics & Data Analysis
Forecasting daily time series using periodic unobserved components time series models
Computational Statistics & Data Analysis
An improved Akaike information criterion for state-space model selection
Computational Statistics & Data Analysis
Computational algorithms for discrete detection and likelihood ratio computation
Information Sciences: an International Journal
Digital matched filters for detecting Gaussian signals in Gaussian noise
Information Sciences: an International Journal
Environmental Modelling & Software
Supplement to “a survey of data smoothing”
Automatica (Journal of IFAC)
Reliable and energy efficient cooperative detection in wireless sensor networks
Computer Communications
| Hi-index | 754.84 |
State variable techniques are used to derive new expressions for the likelihood function for Gaussian signals corrupted by additive Gaussian noise. The continuous time case is obtained as a limit of the discrete time case. The likelihood function is expressed in terms of the conditional expectation of the signal given only past and present observations, multipliers, and integrators (adders). Thus, the likelihood function can be generated in real time using a physically realizable system. Time-varying finite-dimensional Markov models are also discussed as they lead to a direct mechanization for the required conditional expectation. A simple example of a multipath communication system is discussed and an explicit mechanization indicated.