Results 41 to 50 of about 762,527 (191)

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

A systematic approach towards missing lab data in electronic health records: A case study in non‐small cell lung cancer and multiple myeloma

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, Janick Weberpals, Prakirthi Yerram, Chengsheng Jiang, Michael Taylor, Meghna Samant, Sarah Cherng   +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, Elin Shaddox, Debmalya Nandy, Debashis Ghosh, Katerina Kechris   +4 more
doaj   +1 more source

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

Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study.

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, Ilaria Dorigatti, Pierre Nouvellet, Christl A Donnelly   +3 more
doaj   +1 more source

Close-Knit-Regression: An Efficient Technique in Estimating Missing Completely at Random Data

Asian Journal of Probability and Statistics, 2023
The study aimed at using the Close-Knit Regression (CKR) technique to approximate values absent because of the missing completely at random mechanism. Bivariate datasets were generated and simulated for MCAR mechanism at low (10%) and high (60%) rates.
Ahmed Abdulkadir, Bannister Jerry Zachary, Nafisat Yusuf, Kabiru Musa   +3 more
openaire   +1 more source

Missing not at random in end of life care studies: multiple imputation and sensitivity analysis on data from the ACTION study

BMC Medical Research Methodology, 2021
Background Missing data are common in end-of-life care studies, but there is still relatively little exploration of which is the best method to deal with them, and, in particular, if the missing at random (MAR) assumption is valid or missing not at ...
Giulia Carreras, G. Miccinesi, Andrew Wilcock, Nancy Preston, D. Nieboer, L. Deliens, M. Groenvold, U. Lunder, Agnes van der Heide, M. Baccini, Agnes Ida J. Judith A. C. Lea J. Suzanne Hans Marijke Ma van der Heide Korfage Rietjens Jabbarian Polinder , Agnes van der Heide, I. Korfage, J. Rietjens, L. Jabbarian, S. Polinder, Hans van Delden, M. Kars, M. Zwakman, L. Deliens, M. Verkissen, K. Eecloo, K. Faes, K. Pollock, J. Seymour, G. Caswell, Andrew Wilcock, L. Bramley, S. Payne, Nancy Preston, L. Dunleavy, E. Sowerby, G. Miccinesi, F. Bulli, F. Ingravallo, Giulia Carreras, A. Toccafondi, G. Gorini, U. Lunder, B. Červ, A. Simonič, A. Mimić, H. Kodba-Čeh, P. Ozbič, M. Groenvold, C. Arnfeldt, Anna Thit Johnsen   +46 more
semanticscholar   +1 more source

Neither random nor censored: estimating intensity-dependent probabilities for missing values in label-free proteomics

Bioinform., 2023
Motivation Mass spectrometry proteomics is a powerful tool in biomedical research but its usefulness is limited by the frequent occurrence of missing values in peptides that cannot be reliably quantified (detected) for particular samples.
Mengbo Li, G. Smyth
semanticscholar   +1 more source

Missing data imputation using classification and regression trees [PDF]

PeerJ Computer Science
Background Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis.
Cheng-Yang Chen, Yu-Wei Chang
doaj   +2 more sources

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