Non-normal path analysis in the presence of measurement error and missing data: a Bayesian analysis of nursing homes' structure and outcomes

2006 ◽  
Vol 25 (21) ◽  
pp. 3632-3647 ◽  
Author(s):  
Byron J. Gajewski ◽  
Robert Lee ◽  
Sarah Thompson ◽  
Nancy Dunton ◽  
Annette Becker ◽  
...  
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Nursing Homes ◽  
Bayesian Analysis ◽  
Path Analysis

scholarly journals Estimating Measurement Error of the Patient Activation Measure for Respondents with Partially Missing Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ariel Linden
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Controlled Trial ◽  
Patient Activation ◽  
Percentage Error ◽  
Missing Responses ◽  
Improve Measurement ◽  
Patient Activation Measure ◽  
Minimum Score ◽  
Missing Items

The patient activation measure (PAM) is an increasingly popular instrument used as the basis for interventions to improve patient engagement and as an outcome measure to assess intervention effect. However, a PAM score may be calculated when there are missing responses, which could lead to substantial measurement error. In this paper, measurement error is systematically estimated across the full possible range of missing items (one to twelve), using simulation in which populated items were randomly replaced with missing data for each of 1,138 complete surveys obtained in a randomized controlled trial. The PAM score was then calculated, followed by comparisons of overall simulated average mean, minimum, and maximum PAM scores to the true PAM score in order to assess the absolute percentage error (APE) for each comparison. With only one missing item, the average APE was 2.5% comparing the true PAM score to the simulated minimum score and 4.3% compared to the simulated maximum score. APEs increased with additional missing items, such that surveys with 12 missing items had average APEs of 29.7% (minimum) and 44.4% (maximum). Several suggestions and alternative approaches are offered that could be pursued to improve measurement accuracy when responses are missing.

scholarly journals DeepTAL: Deep Learning for TDOA-Based Asynchronous Localization Security With Measurement Error and Missing Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 122492-122502 ◽  
Author(s):  
Yuan Xue ◽  
Wei Su ◽  
Hongchao Wang ◽  
Dong Yang ◽  
Yemeng Jiang
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
Measurement Error

scholarly journals The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data

2016 ◽  
Vol 12 (1) ◽  
pp. 65-77
Author(s):  
Michael D. Regier ◽  
Erica E. M. Moodie
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Em Algorithm ◽  
Optimal Solution ◽  
Bias Reduction ◽  
Model Parameters ◽  
Finite Sample ◽  
Imperfect Data ◽  
The Em Algorithm ◽  
Finite Sample Properties

Abstract We propose an extension of the EM algorithm that exploits the common assumption of unique parameterization, corrects for biases due to missing data and measurement error, converges for the specified model when standard implementation of the EM algorithm has a low probability of convergence, and reduces a potentially complex algorithm into a sequence of smaller, simpler, self-contained EM algorithms. We use the theory surrounding the EM algorithm to derive the theoretical results of our proposal, showing that an optimal solution over the parameter space is obtained. A simulation study is used to explore the finite sample properties of the proposed extension when there is missing data and measurement error. We observe that partitioning the EM algorithm into simpler steps may provide better bias reduction in the estimation of model parameters. The ability to breakdown a complicated problem in to a series of simpler, more accessible problems will permit a broader implementation of the EM algorithm, permit the use of software packages that now implement and/or automate the EM algorithm, and make the EM algorithm more accessible to a wider and more general audience.

scholarly journals Estimating Income Poverty in the Presence of Missing Data and Measurement Error

2011 ◽  
Vol 29 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Cheti Nicoletti ◽  
Franco Peracchi ◽  
Francesca Foliano
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Income Poverty

scholarly journals Measurement error of the Mini-Mental State Examination among individuals with dementia that reside in nursing homes

2020 ◽  
Author(s):  
Carl Hörnsten ◽  
Håkan Littbrand ◽  
Gustaf Boström ◽  
Erik Rosendahl ◽  
Lillemor Lundin-Olsson ◽  
...  
Keyword(s):  
Measurement Error ◽  
Nursing Homes ◽  
Mental State ◽  
Mini Mental State Examination ◽  
State Examination
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