Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models
Journal of the American Statistical AssociationPublished 1 December 1997Open access
J. Keith Ord, Anne B. Koehler, R. D. Snyder
Citations242
SJR quartileQ1
SJR score4.10
SNIP3.08
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TL;DR
A class of nonlinear state-space models, characterized by a single source of randomness, is introduced, and a method for computing prediction intervals is proposed and evaluated on both simulated and real data.
Abstract
A class of dynamic, nonlinear, statistical models is introduced for the analysis of univariate time series. A distinguishing feature of the models is their reliance on only one primary source of randomness: a sequence of independent and identically distributed normal disturbances. It is established that the models are conditionally Gaussian. This fact is used to define a conditional maximum likelihood method of estimation and prediction.
Keywords
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