High-dimensional genomic and proteomic data are now commonplace in cancer research. This Review aims to help biologists understand the properties of high-dimensional data spaces and how these ...

In high-dimensional data analysis, we propose a sequential model averaging (SMA) method to make accurate and stable predictions. Specifically, we introduce a hybrid approach that combines a sequential ...

Data with high-dimensional covariates are now commonly encountered. Compared to other types of responses, research on high-dimensional data with censored survival responses is still relatively limited ...

A dimensional model is a type of data model that is less rigid and structured than other types of models. It is best for a contextual data structure that is more related to the business use or ...

Technical Background and Innovation The emergence of the 'Lingji Smart City' large model relies on nearly a century of accumulated engineering drawing data from the Chongqing Design Institute, ...

This paper shows that the Expectation-Maximization (EM) algorithm for regime-switching dynamic factor models provides satisfactory performance relative to other estimation methods and delivers a good ...

By implementing dimensional modeling, businesses can report, query, and analyze data. Facts, dimensions, and attributes make up the core of dimensional modeling. Facts are categorised as additive, ...

A high-performance AI framework enhances anomaly detection in industrial systems using optimized Graph Deviation Networks and graph attention ...