Results 31 to 40 of about 764,403 (277)
Journal of Statistical Software, 2014 Researchers are often faced with analyzing data sets that are not complete. To prop- erly analyze such data sets requires the knowledge of the missing data mechanism.
Mortaza Jamshidian +2 more
doaj +1 more source
Generating Synthetic Missing Data: A Review by Missing Mechanism
IEEE Access, 2019 The performance evaluation of imputation algorithms often involves the generation of missing values. Missing values can be inserted in only one feature (univariate configuration) or in several features (multivariate configuration) at different ...
Miriam Seoane Santos +5 more
doaj +1 more source
Health and Quality of Life Outcomes, 2010 Background Missing items are common in quality of life (QoL) questionnaires and present a challenge for research in this field. The development of sound strategies of replacement and prevention requires accurate knowledge of their type and determinants ...
Coste Joël, Peyre Hugo, Leplège Alain
doaj +1 more source
Turkish Journal of Agriculture: Food Science and Technology, 2015 The purpose of this study was to evaluate the performance of multiple imputation method in case that missing observation structure is at random and completely at random from the approach of general linear mixed model.
Gazel Ser, Cafer Tayyar Bati
doaj +1 more source
An efficient $k$-means-type algorithm for clustering datasets with incomplete records [PDF]
, 2018 The $k$-means algorithm is arguably the most popular nonparametric clustering method but cannot generally be applied to datasets with incomplete records.
Lithio, Andrew, Maitra, Ranjan
core +4 more sources
Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data [PDF]
, 2016 We propose a residual-based empirical distribution function to estimate the distribution function of the errors of a heteroskedastic nonparametric regression with responses missing at random based on completely observed data, and we show this estimator
Chown, Justin
core +1 more source
CPT: Pharmacometrics & Systems Pharmacology, 2023 Real‐world data derived from electronic health records often exhibit high levels of missingness in variables, such as laboratory results, presenting a challenge for statistical analyses.
Arjun Sondhi +6 more
doaj +1 more source
Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics
BMC Bioinformatics, 2022 When analyzing large datasets from high-throughput technologies, researchers often encounter missing quantitative measurements, which are particularly frequent in metabolomics datasets.
Jonathan P. Dekermanjian +4 more
doaj +1 more source
PLoS ONE, 2021 BackgroundMachine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous ...
Alpha Forna +3 more
doaj +1 more source
Evaluation of missing data mechanisms in two and three dimensional incomplete tables
, 2018 The analysis of incomplete contingency tables is a practical and an interesting problem. In this paper, we provide characterizations for the various missing mechanisms of a variable in terms of response and non-response odds for two and three dimensional
Ghosh, S., Vellaisamy, P.
core +1 more source