Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Random forest (RF) regression is an extremely popular tool for analyzing high-dimensional
data. Nonetheless, its benefits may be lessened in sparse settings due to weak predictors …

Cross-validation is one of the most popular model and tuning parameter selection methods
in statistics and machine learning. Despite its wide applicability, traditional cross-validation …

The application of generalized linear mixed models presents some major challenges for
both estimation, due to the intractable marginal likelihood, and model selection, as we …

Penalized variable selection methods have been extensively studied for standard time-to-
event data. Such methods cannot be directly applied when subjects are at risk of multiple …

The absence of observable innovation data for a firm often leads us to exclude or classify
these firms as non-innovators. We assess the reliability of six methods for dealing with …

We analyze the relationship of retail investor sentiment and the German stock market by
introducing four distinct investor pessimism indices (IPIs) based on selected aggregate …

This article investigates the development of geopolymers as a modern, environmentally
sustainable binder with ceramic-like properties, offering exceptional thermal and fire …

Background Fluid management during continuous renal replacement therapy (CRRT)
requires accuracy in the prescription of desired patient fluid balance (FBGoal) and precision …

The increase in availability of species datasets means that approaches to species
distribution modelling that incorporate multiple datasets are in greater demand. Recent …