Single Imputation Via Chunk-Wise PCA

2021 ◽  
pp. 75-82
Author(s):  
Alfonso Iodice D’Enza ◽  
Francesco Palumbo ◽  
Angelos Markos
Keyword(s):  
Single Imputation

scholarly journals Application of the Single Imputation Method to Estimate Missing Wind Speed Data in Malaysia

2013 ◽  
Vol 6 (10) ◽  
pp. 1780-1784 ◽  
Author(s):  
Nurulkamal Masseran ◽  
Ahmad Mahir Razali ◽  
Kamarulzaman Ibrahim ◽  
Azami Zaharim ◽  
Kamaruzzaman Sopian
Keyword(s):  
Wind Speed ◽  
Imputation Method ◽  
Wind Speed Data ◽  
Single Imputation

scholarly journals The Relative Impacts of Design Effects and Multiple Imputation on Variance Estimates: A Case Study with the 2008 National Ambulatory Medical Care Survey

2014 ◽  
Vol 30 (1) ◽  
pp. 147-161 ◽  
Author(s):  
Taylor Lewis ◽  
Elizabeth Goldberg ◽  
Nathaniel Schenker ◽  
Vladislav Beresovsky ◽  
Susan Schappert ◽  
...  
Keyword(s):  
Missing Data ◽  
Medical Care ◽  
Multiple Imputation ◽  
Data Uncertainty ◽  
Item Nonresponse ◽  
Single Imputation ◽  
Design Effects ◽  
Physician Care ◽  
Care Survey ◽  
Ambulatory Medical Care

Abstract The National Ambulatory Medical Care Survey collects data on office-based physician care from a nationally representative, multistage sampling scheme where the ultimate unit of analysis is a patient-doctor encounter. Patient race, a commonly analyzed demographic, has been subject to a steadily increasing item nonresponse rate. In 1999, race was missing for 17 percent of cases; by 2008, that figure had risen to 33 percent. Over this entire period, single imputation has been the compensation method employed. Recent research at the National Center for Health Statistics evaluated multiply imputing race to better represent the missing-data uncertainty. Given item nonresponse rates of 30 percent or greater, we were surprised to find many estimates’ ratios of multiple-imputation to single-imputation estimated standard errors close to 1. A likely explanation is that the design effects attributable to the complex sample design largely outweigh any increase in variance attributable to missing-data uncertainty.

scholarly journals Incomplete Data Analysis

2021 ◽  
Author(s):  
Bo-Wei Chen ◽  
Jia-Ching Wang
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Data Analysis ◽  
Future Development ◽  
Missing Values ◽  
Regression Tree ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Numerical Examples ◽  
Single Imputation

This chapter discusses missing-value problems from the perspective of machine learning. Missing values frequently occur during data acquisition. When a dataset contains missing values, nonvectorial data are generated. This subsequently causes a serious problem in pattern recognition models because nonvectorial data need further data wrangling before models are built. In view of such, this chapter reviews the methodologies of related works and examines their empirical effectiveness. At present, a great deal of effort has been devoted in this field, and those works can be roughly divided into two types — Multiple imputation and single imputation, where the latter can be further classified into subcategories. They include deletion, fixed-value replacement, K-Nearest Neighbors, regression, tree-based algorithms, and latent component-based approaches. In this chapter, those approaches are introduced and commented. Finally, numerical examples are provided along with recommendations on future development.

scholarly journals Missing data in FFQs: making assumptions about item non-response

2016 ◽  
Vol 20 (6) ◽  
pp. 965-970 ◽  
Author(s):  
Karen E Lamb ◽  
Dana Lee Olstad ◽  
Cattram Nguyen ◽  
Catherine Milte ◽  
Sarah A McNaughton
Keyword(s):  
Missing Data ◽  
Current Practice ◽  
Epidemiological Studies ◽  
Nutritional Epidemiology ◽  
Systematic Evaluation ◽  
Simulation Studies ◽  
Imputation Methods ◽  
Single Imputation ◽  
Mean Imputation ◽  
Large Numbers

AbstractObjectiveFFQs are a popular method of capturing dietary information in epidemiological studies and may be used to derive dietary exposures such as nutrient intake or overall dietary patterns and diet quality. As FFQs can involve large numbers of questions, participants may fail to respond to all questions, leaving researchers to decide how to deal with missing data when deriving intake measures. The aim of the present commentary is to discuss the current practice for dealing with item non-response in FFQs and to propose a research agenda for reporting and handling missing data in FFQs.ResultsSingle imputation techniques, such as zero imputation (assuming no consumption of the item) or mean imputation, are commonly used to deal with item non-response in FFQs. However, single imputation methods make strong assumptions about the missing data mechanism and do not reflect the uncertainty created by the missing data. This can lead to incorrect inference about associations between diet and health outcomes. Although the use of multiple imputation methods in epidemiology has increased, these have seldom been used in the field of nutritional epidemiology to address missing data in FFQs. We discuss methods for dealing with item non-response in FFQs, highlighting the assumptions made under each approach.ConclusionsResearchers analysing FFQs should ensure that missing data are handled appropriately and clearly report how missing data were treated in analyses. Simulation studies are required to enable systematic evaluation of the utility of various methods for handling item non-response in FFQs under different assumptions about the missing data mechanism.

Filling the Missing Data of Air Pollutant Concentration Using Single Imputation Methods

2015 ◽  
Vol 754-755 ◽  
pp. 923-932 ◽  
Author(s):  
Norazian Mohamed Noor ◽  
A.S. Yahaya ◽  
N.A. Ramli ◽  
Mohd Mustafa Al Bakri Abdullah
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Linear Interpolation ◽  
Peninsular Malaysia ◽  
Air Pollutant ◽  
Imputation Methods ◽  
Single Imputation ◽  
Averaging Time

Hourly measured PM10 concentration at eight monitoring stations within peninsular Malaysia in 2006 was used to conduct the simulated missing data. The gap lengths of the simulated missing values are limited to 12 hours since the actual trend of missingness is considered short. Two percentages of simulated missing gaps were generated that are 5 % and 15 %. A number of single imputation methods (linear interpolation (LI), nearest neighbour interpolation (NN), mean above below (MAB), daily mean (DM), mean 12-hour (12M), mean 6-hour (6M), row mean (RM) and previous year (PY)) were calculated to fill in the simulated missing data. In addition, multiple imputation (MI) was also conducted to compare between the single imputation methods. The performances were evaluated using four statistical criteria namely mean absolute error, root mean squared error, prediction accuracy and index of agreement. The results show that 6M perform comparably well to LI. Thus, this show that the effect of smaller averaging time gives better prediction. Other single imputation methods predict the missing data well except for PY. RM and MI performs moderately with the increasing performance in higher fraction of missing gaps whereas LR makes the worst methods for both simulated missing data percentages.

scholarly journals Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload

2021 ◽  
pp. 188-196 ◽  
Author(s):  
Lauren C. Benson ◽  
Carlyn Stilling ◽  
Oluwatoyosi B.A. Owoeye ◽  
Carolyn A. Emery
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Multiple Imputation ◽  
Perceived Exertion ◽  
Pearson Correlation ◽  
Imputation Method ◽  
Rating Of Perceived Exertion ◽  
Imputation Methods ◽  
Single Imputation ◽  
The Individual

Missing data can influence calculations of accumulated athlete workload. The objectives were to identify the best single imputation methods and examine workload trends using multiple imputation. External (jumps per hour) and internal (rating of perceived exertion; RPE) workload were recorded for 93 (45 females, 48 males) high school basketball players throughout a season. Recorded data were simulated as missing and imputed using ten imputation methods based on the context of the individual, team and session. Both single imputation and machine learning methods were used to impute the simulated missing data. The difference between the imputed data and the actual workload values was computed as root mean squared error (RMSE). A generalized estimating equation determined the effect of imputation method on RMSE. Multiple imputation of the original dataset, with all known and actual missing workload data, was used to examine trends in longitudinal workload data. Following multiple imputation, a Pearson correlation evaluated the longitudinal association between jump count and sRPE over the season. A single imputation method based on the specific context of the session for which data are missing (team mean) was only outperformed by methods that combine information about the session and the individual (machine learning models). There was a significant and strong association between jump count and sRPE in the original data and imputed datasets using multiple imputation. The amount and nature of the missing data should be considered when choosing a method for single imputation of workload data in youth basketball. Multiple imputation using several predictor variables in a regression model can be used for analyses where workload is accumulated across an entire season.

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