Sparse Estimation of Cox Proportional Hazards Models via Approximated Information Criteria

We propose a new sparse estimation method for Cox (1972) proportional hazards models by optimizing an approximated information criterion. The main idea involves approximation of the inline image norm with a continuous or smooth unit dent function. The proposed method bridges the best subset selection and regularization by borrowing strength from both. It mimics the best subset selection using a penalized likelihood approach yet with no need of a tuning parameter. We further reformulate the problem with a reparameterization step so that it reduces to one unconstrained nonconvex yet smooth programming problem, which can be solved efficiently as in computing the maximum partial likelihood estimator (MPLE). Furthermore, the reparameterization tactic yields an additional advantage in terms of circumventing postselection inference. The oracle property of the proposed method is established. Both simulated experiments and empirical examples are provided for assessment and illustration

Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research

In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of random forest in circumstances where the regression assumptions are often violated in big data applications. Random forest is a model averaging procedure where each tree is constructed based on a bootstrap sample of the data set. In particular, we emphasize the ease of application, low computational cost, high predictive accuracy, flexibility, and interpretability of random forest machinery. Our overall recommendation is that institutional researchers look beyond classical regression and single decision tree analytics tools, and consider random forest as the predominant method for prediction tasks. The proposed points of view are detailed and illustrated through a simulation experiment and analyses of data from real institutional research projects. Accessed 3,712 times on https://pareonline.net from January 13, 2018 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Constructing Multivariate Survival Trees: The MST Package for R

Multivariate survival trees require few statistical assumptions, are easy to interpret, and provide meaningful diagnosis and prediction rules. Trees can handle a large number of predictors with mixed types and do not require predictor variable transformation or selection. These are useful features in many application fields and are often required in the current era of big data. The aim of this article is to introduce the R package MST. This package constructs multivariate survival trees using marginal model and frailty model based approaches. It allows the user to control and see how the trees are constructed. The package can also simulate high-dimensional, multivariate survival data from marginal and frailty models

Defect-enriched iron fluoride-oxide nanoporous thin films bifunctional catalyst for water splitting

Developing cost-effective electrocatalysts operated in the same electrolyte for water splitting, including oxygen and hydrogen evolution reactions, is important for clean energy technology and devices. Defects in electrocatalysts strongly influence their chemical properties and electronic structures, and can dramatically improve electrocatalytic performance. However, the development of defect-activated electrocatalyst with an efficient and stable water electrolysis activity in alkaline medium remains a challenge, and the understanding of catalytic origin is still limited. Here, we highlight defect-enriched bifunctional eletrocatalyst, namely, three-dimensional iron fluoride-oxide nanoporous films, fabricated by anodization/fluorination process. The heterogeneous films with high electrical conductivity possess embedded disorder phases in crystalline lattices, and contain numerous scattered defects, including interphase boundaries, stacking faults, oxygen vacancies, and dislocations on the surfaces/interface. The heterocatalysts efficiently catalyze water splitting in basic electrolyte with remarkable stability. Experimental studies and first-principle calculations suggest that the surface/edge defects contribute significantly to their high performance