The Model-Free Prediction Principle expounded upon in this monograph is based on the simple
notion of transforming a complex dataset to one that is easier to work with e.g. i.i.d. or
Gaussian. As such it restores the emphasis on observable quantities i.e. current and future
data as opposed to unobservable model parameters and estimates thereof and yields optimal
predictors in diverse settings such as regression and time series. Furthermore the Model-Free
Bootstrap takes us beyond point prediction in order to construct frequentist prediction
intervals without resort to unrealistic assumptions such as normality.Prediction has been
traditionally approached via a model-based paradigm i.e. (a) fit a model to the data at hand
and (b) use the fitted model to extrapolate predict future data. Due to both mathematical and
computational constraints 20th century statistical practice focused mostly on parametric
models. Fortunately with the advent of widely accessible powerful computing in the late 1970s
computer-intensive methods such as the bootstrap and cross-validation freed practitioners from
the limitations of parametric models and paved the way towards the `big data' era of the 21st
century. Nonetheless there is a further step one may take i.e. going beyond even
nonparametric models this is where the Model-Free Prediction Principle is useful.Interestingly
being able to predict a response variable Y associated with a regressor variable X taking on
any possible value seems to inadvertently also achieve the main goal of modeling i.e. trying
to describe how Y depends on X. Hence as prediction can be treated as a by-product of
model-fitting key estimation problems can be addressed as a by-product of being able to
perform prediction. In other words a practitioner can use Model-Free Prediction ideas in order
to additionally obtain point estimates and confidence intervals for relevant parameters leading
to an alternative transformation-based approach to statistical inference.
