Results 51 to 60 of about 620,297 (279)
On Weighted Multivariate Sign Functions
, 2020 Multivariate sign functions are often used for robust estimation and inference. We propose using data dependent weights in association with such functions.
Chatterjee, Snigdhansu +1 more
core
The multivariate Bernoulli detector: change point estimation in discrete survival analysis
BiometricsAbstract Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffers from biased estimation.
Willem van den Boom +3 more
openaire +4 more sources
Molecular Oncology, EarlyView.Combining PTEN protein assessment and transcriptomic profiling of prostate tumors, we uncovered a network enriched in senescence and extracellular matrix (ECM) programs associated with PTEN loss and conserved in a mouse model. We show that PTEN‐deficient cells trigger paracrine remodeling of the surrounding stroma and this information could help ...
Ivana Rondon‐Lorefice +16 more
wiley +1 more source
Factors affecting the accuracy of chest compression depth estimation [PDF]
Clinical and Experimental Emergency Medicine, 2014 Objective We aimed to estimate the accuracy of visual estimation of chest compression depth and identify potential factors affecting accuracy. Methods This simulation study used a basic life support mannequin, the Ambu man.
Jung Hee Kang +12 more
doaj +1 more source
Next‐generation proteomics improves lung cancer risk prediction
Molecular Oncology, EarlyView.This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj +4 more
wiley +1 more source
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
Journal of Statistical Software, 2007 Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing.
Tarn Duong
doaj
IEEE Open Journal of the Communications SocietyExisting performance analysis of wireless systems based on reconfigurable intelligent surfaces (RIS) over fading channels has primarily focused on providing statistical insights into the sum and product of random variables, typically represented as a ...
Vinay Kumar Chapala, S. M. Zafaruddin
doaj +1 more source
Tumor mutational burden as a determinant of metastatic dissemination patterns
Molecular Oncology, EarlyView.This study performed a comprehensive analysis of genomic data to elucidate whether metastasis in certain organs share genetic characteristics regardless of cancer type. No robust mutational patterns were identified across different metastatic locations and cancer types.
Eduardo Candeal +4 more
wiley +1 more source
Correlation of the differential expression of PIK3R1 and its spliced variant, p55α, in pan‐cancer
Molecular Oncology, EarlyView.PIK3R1 undergoes alternative splicing to generate the isoforms, p85α and p55α. By combining large patient datasets with laboratory experiments, we show that PIK3R1 spliced variants shape cancer behavior. While tumors lose the protective p85α isoform, p55α is overexpressed, changes linked to poorer survival and more pronounced in African American ...
Ishita Gupta +10 more
wiley +1 more source
Multivariate Tail Probabilities: Predicting Regional Pertussis Cases in Washington State
Entropy, 2021 In disease modeling, a key statistical problem is the estimation of lower and upper tail probabilities of health events from given data sets of small size and limited range.
Xuze Zhang +2 more
doaj +1 more source