In order to improve the prediction accuracy of the model for the given dataset, this study sought to present a double approach variable selection method when pairwise interactions between the explanatory variables exist and to choose the smallest explanatory variable set (consideri...
running head: predictive models: interactions with exposures 1 an analytic approach for interpretable predictive models in high dimensional data, in the pr... An analytic approach for interpretable predictive models in high‐dimensional data in the presence of interactions with exposures SR Bhatnagar,Y...
In addition, they show, using real data, how the methods affect decisionsdoi:10.1016/0169-7439(94)00009-3Clifford H. SpiegelmanC.Y. WangChemometrics and Intelligent Laboratory SystemsSPIEGELMAN, C. and WANG, C. Y. Z1994.. Detecting interactions using low dimensional searches in high dimensional...
The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data ...
A review of machine learning and statistical approaches for detecting SNP interactions in high-dimensional genomic data In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more ... S Uppu,A Krishna,R ...
Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary comp
(2024)). The main function uses a data-driven approach for characterizing interactions of different orders based on solving a set of linear equations constructed from Kramers-Moyal coefficients derived from statistical moments of N-dimensional multivariate time series. It makes use of the method ...
The finite element method is used to explain the cause of the phenomenon by three-dimensional modeling. The simulation results show that the addition of polyurethane plate increases the deformation of thin piezoelectric ceramics, so as to increase the piezoelectric response of the energy harvesting ...
The mathematical and statistical properties of high-dimensional data spaces are often poorly understood or inadequately considered. This can be particularly challenging for the common scenario where the number of data points obtained for each specimen greatly exceed the number of specimens. ...
(mitochondria), SYTO 14 (nucleoli and cytoplasmic RNA), and phalloidin (actin). Images are processed using the open-source CellProfiler software to extract thousands of features of each cell’s morphology such as shape, intensity, and texture statistics, thus forming a high-dimensional profile for...