Bayesian nonparametric smoothers for regular processes

Abstract
This report is about the analysis of stochastic processes of the formR = S + N, whereSis a “smooth” functional andNis noise. The proposed methods derive from the assumption that the observedR‐values and unobserved values ofR, the assumed inferential objectives of the analysis, are linearly related through Taylor series expansions of observed about unobserved values. The expansion errors and all otherprioriunspecified quantities have a joint multivariate normal distribution which expresses the prior uncertainty about their values. The results include interpolators, predictors, and derivative estimates, with credibility‐interval estimates automatically generated in each case. An analysis of an acid‐rain wet‐deposition time series is included to indicate the efficacy of the proposed method. It was this problem which led to the methodological developments reported in this paper.

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