Retrospective Statistical Inference
In this article, we explore a new paradigm for statistical inference. The approach centers around the point estimate based on observed data, simulating replicates using the estimate as the truth to produce clones of the estimate, with inference deriving from the clone distribution. It avoids prospective finite-dimensional model assumptions, but it makes no probabilistic claims concerning the truth; it suggests an alternative system of uncertainty quantification that is operable in nonparametric function estimation. The procedures are demonstrated using examples of smoothing spline ANOVA models in nonparametric regression. The paradigm also applies in parametric regression, where the proposed inference closely resembles traditional inference operation-wise. Conceptual discussions are scattered throughout.