… The chapter also reviews models of changes in regime and develops the parallel between
such models and linear state-space models. The chapter concludes with a brief discussion of …

… be done many times, model parameter estimation in the … , state estimation for a nonlinear
state space model with unknown parameters, and seasonal adjustment with a nonlinear model …

… of inference in statespace models, consider … state process {Xn} given the observations {Yn}
is only feasible analytically for simple models such as the linear Gaussian state-space model. …

… state of the art in state space model based signal processing, and discusses the advantages
of adopting a state space … data is made explicit by the state space approach, which can be …

… by a model of the form (1) where all disturbances u, and v, (/ = 1,..., n) and the initial state XQ
… Estimation, filtering and smoothing in state space models with incompletely specified initial …

… When a finite dimensional state space model does not suffice to capture time series behavior,
we attempt to approximate the series by another of lesser complexity which admits a finite …

… models of movement is a major challenge for animal ecology. Here, we review a statistical
approach, state–space … The statistical robustness and predictive ability of state–space models …

… State Space Model, and some of the experimental findings, all in a nutshell. By showing the
relationship between the proposed model … elements of the State Space Model are presented …

… of the state and observation variables is multivariate Gaussian. We will use the term statespace
model to refer to this simple form of the model. For such models, the state transition …

… a linear state space model with errors … state vector given the mixture and coefficient indicator
variables and simultaneously generates all the indicator variables conditional on the state …