Results 1 to 10 of about 1,716,816 (278)

Bayesian Estimation of Log-Normal Distribution Under Ranked Set Sampling With Missing Data

IEEE Access, 2021
In this paper, joint Bayesian estimation of two parameters of a log-normal distribution is obtained based on simple random sampling (SRS) and ranked set sampling (RSS) with complete and missing data.
Fengxi Zong, Rubing Li
doaj   +1 more source

Joint Models for Incomplete Longitudinal Data and Time-to-Event Data

Mathematics, 2022
Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data.
Yuriko Takeda, Toshihiro Misumi, Kouji Yamamoto   +2 more
doaj   +1 more source

Filling the gap in food and nutrition security data: What imputation method is best for Africa's food and nutrition security?

Lithuanian Journal of Statistics, 2022
Our study presents the methods adopted to produce accurate imputed values for Africa's food security and nutrition (FSN). We focused primarily on the following five imputation methods for handling missing data: Mean Imputation; Multiple Imputed values ...
Adusei Bofa, Temesgen Zewotir
doaj   +1 more source

Impact on Cronbach's alpha of simple treatment methods for missing data [PDF]

Tutorials in Quantitative Methods for Psychology, 2016
The scientific treatment of missing data has been the subject of research for nearly a century. Strangely, interest in missing data is quite new in the fields of educational science and psychology (Peugh & Enders, 2004; Schafer & Graham, 2002). It is now
Béland, Sébastien, Pichette, Francois, Jolani, S.   +2 more
doaj   +1 more source

Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity

BMC Medical Research Methodology, 2023
Background Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming ...
Jiaqi Tong, Fan Li, Michael O. Harhay, Guangyu Tong   +3 more
doaj   +1 more source

A simplified eye-opener on managing missing data and in evaluation of non-response bias in medical research: a narrative review

Journal of the College of Community Physicians, 2018
Using correct methods for prevention, analysis and treatment of missing data is essential in preserving the validity of scientific research. In spite of this, issues related to missing data and non-response bias are found to be inadequately discussed in ...
P. K. B. Mahesh, Wasantha Gunathunga, Mahendra arnold, S. B. Munasinghe, Sinha De Silva   +4 more
doaj   +1 more source

What impact do assumptions about missing data have on conclusions? A practical sensitivity analysis for a cancer survival registry

BMC Medical Research Methodology, 2017
Background Within epidemiological and clinical research, missing data are a common issue and often over looked in publications. When the issue of missing observations is addressed it is usually assumed that the missing data are ‘missing at random’ (MAR).
M. Smuk, J. R. Carpenter, T. P. Morris
doaj   +1 more source

Estimating Gaussian Copulas with Missing Data with and without Expert Knowledge

Entropy, 2022
In this work, we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data.
Maximilian Kertel, Markus Pauly
doaj   +1 more source

Estimating linear functionals in nonlinear regression with responses missing at random [PDF]

, 2009
We consider regression models with parametric (linear or nonlinear) regression function and allow responses to be ``missing at random.'' We assume that the errors have mean zero and are independent of the covariates.
Müller, Ursula U.
core   +4 more sources

Regularized approach for data missing not at random [PDF]

Statistical Methods in Medical Research, 2017
It is common in longitudinal studies that missing data occur due to subjects’ no response, missed visits, dropout, death or other reasons during the course of study. To perform valid analysis in this setting, data missing not at random (MNAR) have to be considered.
Chi-Hong, Tseng, Yi-Hau, Chen
openaire   +2 more sources