Results 71 to 80 of about 3,518,988 (354)
Bootstrapping with Models: Confidence Intervals for Off-Policy Evaluation
AAAI Conference on Artificial Intelligence, 2016 In many reinforcement learning applications, it is desirable to determine confidence interval lower bounds on the performance of any given policy without executing said policy.
Josiah P. Hanna, P. Stone, S. Niekum
semanticscholar +1 more source
Modeling hepatic fibrosis in TP53 knockout iPSC‐derived human liver organoids
Molecular Oncology, EarlyView.This study developed iPSC‐derived human liver organoids with TP53 gene knockout to model human liver fibrosis. These organoids showed elevated myofibroblast activation, early disease markers, and advanced fibrotic hallmarks. The use of profibrotic differentiation medium further amplified the fibrotic signature seen in the organoids.
Mustafa Karabicici +8 more
wiley +1 more source
Molecular Oncology, EarlyView.Detection of extrachromosomal circular DNA (eccDNA) in plasma samples from EGFR‐mutated non‐small cell lung cancer patients. Plasma was collected before and during treatment with the EGFR‐tyrosine kinase inhibitor osimertinib. Plasma eccDNA was detected in all cancer samples, and the presence of the EGFR gene on eccDNA serves as a potential biomarker ...
Simone Stensgaard +5 more
wiley +1 more source
Journal of Probability and Statistics, 2015 Model averaging is a technique used to account for model uncertainty, in both Bayesian and frequentist multimodel inferences. In this paper, we compare the performance of model-averaged Bayesian credible intervals and frequentist confidence intervals ...
Daniel Turek
doaj +1 more source
Effect of confidence interval construction on judgment accuracy
Judgment and Decision Making, 2020 Three experiments (N = 550) examined the effect of an interval construction elicitation method used in several expert elicitation studies on judgment accuracy.
David R. Mandel +3 more
doaj +1 more source
Screening for lung cancer: A systematic review of overdiagnosis and its implications
Molecular Oncology, EarlyView.Low‐dose computed tomography (CT) screening for lung cancer may increase overdiagnosis compared to no screening, though the risk is likely low versus chest X‐ray. Our review of 8 trials (84 660 participants) shows added costs. Further research with strict adherence to modern nodule management strategies may help determine the extent to which ...
Fiorella Karina Fernández‐Sáenz +12 more
wiley +1 more source
On a linear method in bootstrap confidence intervals
Statistica, 2007 A linear method for the construction of asymptotic bootstrap confidence intervals is proposed. We approximate asymptotically pivotal and non-pivotal quantities, which are smooth functions of means of n independent and identically distributed random ...
Andrea Pallini
doaj +1 more source
The fallacy of placing confidence in confidence intervals
Psychonomic Bulletin & Review, 2015 Interval estimates – estimates of parameters that include an allowance for sampling uncertainty – have long been touted as a key component of statistical analyses.
R. Morey +4 more
semanticscholar +1 more source
Cytoplasmic p21 promotes stemness of colon cancer cells via activation of the NFκB pathway
Molecular Oncology, EarlyView.Cytoplasmic p21 promotes colorectal cancer stem cell (CSC) features by destabilizing the NFκB–IκB complex, activating NFκB signaling, and upregulating BCL‐xL and COX2. In contrast to nuclear p21, cytoplasmic p21 enhances spheroid formation and stemness transcription factor CD133.
Arnatchai Maiuthed +10 more
wiley +1 more source
Confidence intervals using contrasts for regression model [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2009 A graph of confidence intervals can be used to report results from a regression model with explanatory variables asfactors. In this paper we describe a method for computing and displaying confidence intervals using weighted sum contraststo compare ...
Phattrawan Tongkumchum, Don McNeil
doaj