Summarizing the effect of many covariates through a few linear combinations is an effective
way of reducing covariate dimension and is the backbone of (sufficient) dimension …

Sufficient dimension reduction is a rapidly develo** research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

In this paper, we review three families of methods in linear sufficient dimension reduction
through optimization. Through minimization of general loss functions, we cast classical …

Establishing cause-effect relationships from observational data often relies on untestable
assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from …

Conformal prediction has been a very popular method of distribution-free predictive
inference in recent years in machine learning and statistics. Its popularity stems from the fact …

Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces
covariates with a minimal set of their linear combinations without loss of information on the …

We give a commentary on the challenges of big data for Statistics. We then narrow our
discussion to one of those challenges: dimension reduction. This leads to consideration of …

Abstract Model average techniques are very useful for model-based prediction. However,
most earlier works in this field focused on parametric models and continuous responses. In …

The supplementary material comprises some examples of solutions for Theorem 3.2,
alongside additional simulations, real data results, and implementation details. Moreover, it …

Establishing cause–effect relationships from observational data often relies on untestable
assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from …