Results 91 to 100 of about 1,714,069 (326)
Towards Semi-supervised Learning with Non-random Missing Labels [PDF]
, 2023 Yue Duan +5 more
openalex +1 more source
An Investigation of Missing Data Methods for Classiffcation Trees [PDF]
, 2006 There are many different missing data methods used by classification tree algorithms, but few studies have been done comparing their appropriateness and performance.
Ding, Yufeng, Simonoff, Jeffrey S.
core
Molecular Oncology, EarlyView.This study indicates that Merkel cell carcinoma (MCC) does not originate from Merkel cells, and identifies gene, protein & cellular expression of immune‐linked and neuroendocrine markers in primary and metastatic Merkel cell carcinoma (MCC) tumor samples, linked to Merkel cell polyomavirus (MCPyV) status, with enrichment of B‐cell and other immune cell
Richie Jeremian +10 more
wiley +1 more source
A Perspective on the Missing at Random Problem: Synthetic Generation and Benchmark Analysis
IEEE AccessProgressively more advanced and complex models are proposed to address problems related to computer vision, forecasting, Internet of Things, Big Data and so on. However, these disciplines require preprocessing steps to obtain meaningful results.
Juan-Francisco Cabrera-Sanchez +3 more
doaj +1 more source
YAP1::TFE3 mediates endothelial‐to‐mesenchymal plasticity in epithelioid hemangioendothelioma
Molecular Oncology, EarlyView.The YAP1::TFE3 fusion protein drives endothelial‐to‐mesenchymal transition (EndMT) plasticity, resulting in the loss of endothelial characteristics and gain of mesenchymal‐like properties, including resistance to anoikis, increased migratory capacity, and loss of contact growth inhibition in endothelial cells.
Ant Murphy +9 more
wiley +1 more source
Semiparametric regression analysis with missing response at random [PDF]
We develop inference tools in a semiparametric partially linear regression model with missing response data. A class of estimators is defined that includes as special cases: a semiparametric regression imputation estimator, a marginal average estimator ...
Oliver Linton +2 more
core
Second-Order Inference for the Mean of a Variable Missing at Random [PDF]
, 2015 We present a second-order estimator of the mean of a variable subject to missingness, under the missing at random assumption. The estimator improves upon existing methods by using an approximate second-order expansion of the parameter functional, in ...
Carone, Marco +2 more
core +2 more sources
Molecular Oncology, EarlyView.In this exploratory study, we investigated the relationship between the gut microbiota and outcome in patients with metastatic hormone receptor‐positive breast cancer, treated in a randomized clinical trial with chemotherapy alone or chemotherapy in combination with immune checkpoint blockade.
Andreas Ullern +7 more
wiley +1 more source
Group feature screening for ultrahigh-dimensional data missing at random
AIMS MathematicsStatistical inference for missing data is common in data analysis, and there are still widespread cases of missing data in big data. The literature has discussed the practicability of two-stage feature screening with categorical covariates missing at ...
Hanji He , Meini Li, Guangming Deng
doaj +1 more source