Results 101 to 110 of about 762,527 (191)

Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods

Sensors
In geriatric healthcare, missing data pose significant challenges, especially in systems used for frailty monitoring in elderly individuals. This study explores advanced imputation techniques used to enhance data quality and maintain model performance in
Gabriel-Vasilică Sasu, Bogdan-Iulian Ciubotaru, Nicolae Goga, Andrei Vasilățeanu   +3 more
doaj   +1 more source

Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models

Journal of the American Statistical Association
This paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if the number of missing entries is small enough compared to the panel size, then they can be estimated well even when missing is not at random. Taking advantage
Jungjun Choi, Ming Yuan
openaire   +3 more sources

Paper 246-2009 Getting the Most out of the SAS ® Survey Procedures: Repeated Replication Methods, Subpopulation Analysis, and Missing Data Options in SAS ® v9.2 [PDF]

This paper presents practical guidance on three common survey data analysis techniques: repeated replication methods for variance estimation, subpopulation analyses, and techniques for handling missing data.
Ann Arbor, Patricia A. Berglund
core  

Effective and efficient handling of missing data in supervised machine learning

Data Science and Management
The prevailing consensus in statistical literature is that multiple imputation is generally the most suitable method for addressing missing data in statistical analyses, whereas a complete case analysis is deemed appropriate only when the rate of ...
Peter Ayokunle Popoola, Jules-Raymond Tapamo, Alain Guy Honoré Assounga   +2 more
doaj   +1 more source

Accounting for Nonresponse Heterogeneity in Panel Data [PDF]

The paper proposes a technique for the estimation of possibly nonlinear panel data models in the presence of heterogeneous unit nonresponse. Attrition or unit nonresponse in panel data usually renders parameter estimators inconsistent unless the ...
Joachim Inkmann
core  

Missing Data and Multiple Imputation: An Unbiased Approach [PDF]

The default method of dealing with missing data in statistical analyses is to only use the complete observations (complete case analysis), which can lead to unexpected bias when data do not meet the assumption of missing completely at random (MCAR).
Alexander, D., Foy, M., Law, J., Mason, S., Mendez, C., Meyers, V., VanBaalen, M., Wear, M.   +7 more
core   +1 more source

The randomized marker method for single-case randomization tests: Handling data missing at random and data missing not at random

Behavior Research Methods, 2022
Tamal Kumar De, P. Onghena
semanticscholar   +1 more source

Missing Values in Empirical Research: Theory and Practice. Part 39 of a Series on the Evaluation of Scientific Publications. [PDF]

Dtsch Arztebl Int
Schaefer E, Lang A, Piedboeuf-Potyka K, Kuss O.   +3 more
europepmc   +1 more source

Assessing imputation techniques for missing data in small and multicollinear datasets: insights from craniofacial morphometry. [PDF]

BMC Med Res Methodol
Abdullah NA, Hariri F, Hisam MNF, Binti Hassan SF.   +3 more
europepmc   +1 more source

Detection of rare medical events in electronic health records using machine learning: Current practices and suggestions - A scoping review. [PDF]

PLoS One
Gebeyehu B, Kleinberg B, Van Deun K, de Vries E.   +3 more
europepmc   +1 more source