Results 11 to 20 of about 1,714,069 (326)

Block-Conditional Missing at Random Models for Missing Data [PDF]

Statistical Science, 2010
Two major ideas in the analysis of missing data are (a) the EM algorithm [Dempster, Laird and Rubin, J. Roy. Statist. Soc. Ser. B 39 (1977) 1--38] for maximum likelihood (ML) estimation, and (b) the formulation of models for the joint distribution of the
John D. Kalbfleisch, Roderick J. A. Little, Roderick J. A. Little Is, Yan Zhou   +3 more
core   +4 more sources

Missing.... presumed at random: cost-analysis of incomplete data [PDF]

Health Economics, 2002
When collecting patient-level resource use data for statistical analysis, for some patients and in some categories of resource use, the required count will not be observed. Although this problem must arise in most reported economic evaluations containing
Bang, Box, Brick, Buck, Carides, Coast, Diggle, Duan, Etzioni, Fenn, Gelfand, Greene, Hanson, Horton, Johnston, Keoghane, Leigh, Lin, Little, Little, Polsky, Roberts, Rubin, Rubin, Rubin, Schafer, Schafer, Tanner, van Buuren, Willan, WinBUGS, Wu   +31 more
core   +5 more sources

Missing at random, likelihood ignorability and model completeness

The Annals of Statistics, 2004
This paper provides further insight into the key concept of missing at random (MAR) in incomplete data analysis. Following the usual selection modelling approach we envisage two models with separable parameters: a model for the response of interest and a
Copas, John B., Lu, Guobing
core   +3 more sources

Missing at random: a stochastic process perspective. [PDF]

Biometrika, 2022
SummaryWe offer a natural and extensible measure-theoretic treatment of missingness at random. Within the standard missing-data framework, we give a novel characterization of the observed data as a stopping-set sigma algebra. We demonstrate that the usual missingness-at-random conditions are equivalent to requiring particular stochastic processes to be
Farewell DM, Daniel RM, Seaman SR.
europepmc   +6 more sources

Diagnosing and Handling Common Violations of Missing at Random. [PDF]

Psychometrika, 2023
Ignorable likelihood (IL) approaches are often used to handle missing data when estimating a multivariate model, such as a structural equation model. In this case, the likelihood is based on all available data, and no model is specified for the missing data mechanism.
Ji F, Rabe-Hesketh S, Skrondal A.
europepmc   +7 more sources

On Testing the Missing at Random Assumption [PDF]

, 2006
Most approaches to learning from incomplete data are based on the assumptionthat unobserved values are missing at random (mar). While the mar assumption, as such, is not testable, it can become testable in the context of other distributional assumptions, e.g. the naive Bayes assumption.
Manfred Jaeger
  +6 more sources

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