Results 11 to 20 of about 549,387 (258)
On using a non-probability sample for the estimation of population parameters
Lietuvos Matematikos Rinkinys, 2023 We aim to find a way to effectively integrate a non-probability (voluntary) sample under the data framework, where the study variable is also observed in a probability sample of some statistical survey.
Ieva Burakauskaitė, Andrius Čiginas
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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
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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 +2 more
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A Tutorial for Handling Suspected Missing Not at Random Data in Longitudinal Clinical Trials [PDF]
Tutorials in Quantitative Methods for Psychology, 2023 Missing data in longitudinal randomized clinical trials, even if assumed to be missing at random (MAR), can result in biased parameter estimates and incorrect treatment conclusions.
Peugh, James L. +2 more
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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
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Multiple Imputation of Binary Multilevel Missing not at Random Data [PDF]
Journal of the Royal Statistical Society Series C: Applied Statistics, 2020 SummaryWe introduce a selection model-based multilevel imputation approach to be used within the fully conditional specification framework for multiple imputation. Concretely, we apply a censored bivariate probit model to describe binary variables assumed to be missing not at random.
Hammon, Angelina, Zinn, Sabine
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Regularized Mislevy-Wu Model for Handling Nonignorable Missing Item Responses
Information, 2023 Missing item responses are frequently found in educational large-scale assessment studies. In this article, the Mislevy-Wu item response model is applied for handling nonignorable missing item responses.
Alexander Robitzsch
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Cox Regression with Covariates Missing Not at Random [PDF]
Statistics in Biosciences, 2010 This paper considers estimation under the Cox proportional hazards model with right-censored event times in the presence of covariates missing not at random (MNAR). We propose an approach derived from likelihood estimation utilizing supplementary information.
Victoria J. Cook +2 more
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Causal inference with confounders missing not at random [PDF]
Biometrika, 2019 Summary It is important to draw causal inference from observational studies, but this becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. In this article we propose a novel framework for nonparametric identification of causal effects with
Yang, S, Wang, L, Ding, P
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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 +4 more
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