Results 111 to 120 of about 5,211,856 (273)
DIMENSION REDUCTION TRANSFORMATIONS IN DISCRIMINANT ANALYSIS [PDF]
Dimension reduction transformations in discriminant analysis are introduced. Their properties, as well as sufficient conditions for their characterization, are studied.
Santiago Velilla, Adolfo Hernández
core
New multidimensional approaches to poverty measurement in Bosnia and Herzegovina
Croatian Review of Economic, Business and Social Statistics, 2017 Unlike the standard unidimensional poverty indices, based mostly on monetary poverty measures, multidimensional poverty indices may include numerous non-monetary poverty indicators. This study utilized fuzzy and Alkire – Foster (AF) and fuzzy methodology
Delalić Adela +2 more
doaj +1 more source
Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect. [PDF]
Stat Sin, 2021 Ghosh T, Ma Y, de Luna X.
europepmc +1 more source
Molecular Oncology, EarlyView.Loss of NAPRT promotes lung tumor initiation and growth through a noncanonical mechanism, independent of its role in NAD+ biosynthesis. Mechanistically, NAPRT depletion activates the mTORC2‐driven AKT/β‐catenin signaling axis to enhance clonogenic and invasive phenotypes. Furthermore, lung‐specific Naprt deletion significantly increases tumor burden in
Myung Joon Oh +11 more
wiley +1 more source
Subset selection in dimension reduction methods [PDF]
Dimension reduction methods play an important role in multivariate statistical analysis, in particular with high-dimensional data. Linear methods can be seen as a linear mapping from the original feature space to a dimension reduction subspace.
Luca Scrucca
core
Markov Boundary Discovery with Ridge Regularized Linear Models
Journal of Causal Inference, 2016 Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association ...
Strobl Eric V., Visweswaran Shyam
doaj +1 more source
Testing predictor contributions in sufficient dimension reduction
The Annals of Statistics, 2004 We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the conditional distribution of the response given the predictors. Predictor effects need not be limited to the mean function and smoothing is not required.
openaire +4 more sources
Molecular Oncology, EarlyView.We established the avian chorioallantoic membrane (CAM) assay as a scalable in vivo model for studying circulating tumor cells (CTCs). Human gastrointestinal tumors spontaneously released genetically validated CTCs that were detected across multiple platforms, demonstrating that the CAM model provides an accessible tool for investigating early cancer ...
Dennis Roth +17 more
wiley +1 more source
Sufficient Dimension Reduction OLS with singularity
, 2016 Recently, there are large volume of data, mainly called Big data, accumulated from a variety of industries in real-time and there is need for a method for storing, handling and processing those data have been raised.
김민선
core +1 more source
Ultrahigh-dimensional sufficient dimension reduction for censored data with measurement error in covariates. [PDF]
J Appl Stat, 2022 Chen LP.
europepmc +1 more source