Results 261 to 270 of about 764,403 (277)

ARE HEALTH INSURANCE ITEM ALLOCATIONS IN THE AMERICAN COMMUNITY SURVEY MISSING COMPLETELY AT RANDOM?

Journal of Frailty & Aging, 2013
Background:Item allocation (the assignment of plausible values to missing or illogical responses insurvey studies) is at times necessary in the production of complete data sets. In the American Community Survey(ACS), missing responses to health insurance coverage questions are allocated. Objectives:Because allocationrates may vary
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Taking ‘Don’t Knows’ as Valid Responses: A Multiple Complete Random Imputation of Missing Data

Quality and Quantity, 2006
Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts awareness of the biasing effects of missing data and has also provided a convenient solution. Imputation methods replace non-response with estimates of the unobserved scores. In many instances, however, non-response to a stimulus
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Completing Hedge Fund Missing Net Asset Values Using Kohonen Maps and Constrained Randomization

2005
Analysis of financial databases is sensitive to missing values (no reported information, provider errors, outlier filters...). Risk analysis and portfolio asset allocation require cylindrical and complete samples. Moreover, return distributions are characterised by non-normalities due to heteroskedasticity, leverage effects, volatility feedbacks and ...
Maillet, Bertrand, Merlin, Paul
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Taking Don't Knows as Valid Responses : A Complete Random Imputation of Missing Data [PDF]

, 2004
Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts' awareness of the biasing effects of missing data and has also provided a convenient solution. Imputation methods replace non-response with estimates of the unobserved scores.
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Estimating Missing values in Randomized Complete Block design using EM Algorithm

INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES
This research explores the application of the Expectation-Maximization (EM) algorithm to address missing data challenges in Randomized Complete Block Designs (RCBD). Traditional imputation methods often introduce biases, prompting the need for a robust solution.
O.P. Sheoran, Vinay Kumar, Rohit Kundu
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Missing data completion method based on KNN and random forest

Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021
Songyu Zhang, Yuchen Zhou, Jinghua Yan, Fanliang Bu   +3 more
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Missing Completely at Random

2021
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A Comparison of Missing Value Estimations in Randomized Complete Block Design

The Journal of King Mongkut's University of Technology North Bangkok, 2021
Chayada Kaewchaicharoenkit, Boonorm Chomtee, Wandee Wanishsakpong   +2 more
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Dual strategy based missing completely at random type missing data imputation on the internet of medical things

International Journal of Intelligent Engineering Informatics, 2023
P. Iris Punitha, J. G. R. Sathiaseelan
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CHARACTERIZING AND COMPLETING NON-RANDOM MISSING VALUES

Intelligent Decision Making Systems, 2009
L. BEN OTHMAN, F. RIOULT, S. BEN YAHIA, B. CRÉMILLEUX   +3 more
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