Results 61 to 70 of about 549,387 (258)
Frontiers in Public HealthBackgroundStronger associations between modifiable risk factors and cognitive function have been found in younger than older adults. This age pattern may be subject to mortality selection and non-ignorable missingness caused by dropouts due to death, but
Qin Ran +4 more
doaj +1 more source
International Journal of Population Data Science, 2017 Background Ethnicity is an important factor to be considered in health research because of its association with inequality in disease prevalence and the utilisation of healthcare.
Tra My Pham +3 more
doaj +1 more source
Random Indicator Imputation for Missing Not At Random Data
Imputation methods for dealing with incomplete data typically assume that the missingness mechanism is at random (MAR). These methods can also be applied to missing not at random (MNAR) situations, where the user specifies some adjustment parameters that describe the degree of departure from MAR.
Jolani, Shahab, van Buuren, Stef
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Identifiable Generative Models for Missing Not at Random Data Imputation
, 2021 Real-world datasets often have missing values associated with complex generative processes, where the cause of the missingness may not be fully observed. This is known as missing not at random (MNAR) data. However, many imputation methods do not take into account the missingness mechanism, resulting in biased imputation values when MNAR data is present.
Ma, Chao, Zhang, Cheng
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Cell wall target fragment discovery using a low‐cost, minimal fragment library
FEBS Letters, EarlyView.LoCoFrag100 is a fragment library made up of 100 different compounds. Similarity between the fragments is minimized and 10 different fragments are mixed into a single cocktail, which is soaked to protein crystals. These crystals are analysed by X‐ray crystallography, revealing the binding modes of the bound fragment ligands.
Kaizhou Yan +5 more
wiley +1 more source
Boosting Prediction with Data Missing Not at Random
Journal of Computational and Graphical StatisticsBoosting has emerged as a useful machine learning technique over the past three decades, attracting increased attention. Most advancements in this area, however, have primarily focused on numerical implementation procedures, often lacking rigorous theoretical justifications.
Yuan Bian, Grace Y. Yi, Wenqing He
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Optimal design when outcome values are not missing at random [PDF]
Statistica Sinica, 2018 Summary: The presence of missing values complicates statistical analyses. In design of experiments, missing values are particularly problematic when constructing optimal designs, as it is not known which values are missing at the design stage. When data are missing at random it is possible to incorporate this information into the optimality criterion ...
Lee, Kim May +2 more
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FEBS Letters, EarlyView.In this study, we found that human cervical‐derived adipocytes maintain intracellular iron level by regulating the expression of iron transport‐related proteins during adrenergic stimulation. Melanotransferrin is predicted to interact with transferrin receptor 1 based on in silico analysis.
Rahaf Alrifai +9 more
wiley +1 more source
A hierarchical Bayesian approach for handling missing classification data
Ecology and Evolution, 2019 Ecologists use classifications of individuals in categories to understand composition of populations and communities. These categories might be defined by demographics, functional traits, or species.
Alison C. Ketz +3 more
doaj +1 more source
BioData Mining, 2021 Background Missing data is a common issue in different fields, such as electronics, image processing, medical records and genomics. They can limit or even bias the posterior analysis.
Ben Omega Petrazzini +4 more
doaj +1 more source