scholarly journals A multiple imputation method based on weighted quantile regression models for longitudinal censored biomarker data with missing values at early visits

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
MinJae Lee ◽  
Mohammad H. Rahbar ◽  
Matthew Brown ◽  
Lianne Gensler ◽  
Michael Weisman ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Multiple Imputation ◽  
Regression Models ◽  
Missing Values ◽  
Imputation Method ◽  
Multiple Imputation Method ◽  
Biomarker Data ◽  
Weighted Quantile Regression

scholarly journals Asymptotic Standard Errors of Parameter Scale Transformation Coefficients in Test Equating Under the Nominal Response Model

2020 ◽  
pp. 014662162096574
Author(s):  
Zhonghua Zhang
Keyword(s):  
Multiple Imputation ◽  
Imputation Method ◽  
Delta Method ◽  
Scale Transformation ◽  
Standard Errors ◽  
Response Model ◽  
Test Equating ◽  
Nominal Response Model ◽  
Multiple Imputation Method ◽  
Nominal Response

Researchers have developed a characteristic curve procedure to estimate the parameter scale transformation coefficients in test equating under the nominal response model. In the study, the delta method was applied to derive the standard error expressions for computing the standard errors for the estimates of the parameter scale transformation coefficients. This brief report presents the results of a simulation study that examined the accuracy of the derived formulas and compared the performance of this analytical method with that of the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion standard errors as well as those yielded by the multiple imputation method under all the simulation conditions.

scholarly journals Reconstruction of spatially detailed global map of NH<sub>4</sub><sup>+</sup> and NO<sub>3</sub><sup>−</sup> application in synthetic nitrogen fertilizer

2016 ◽  
Author(s):  
Kazuya Nishina ◽  
Akihiko Ito ◽  
Naota Hanasaki ◽  
Seiji Hayashi
Keyword(s):  
Multiple Imputation ◽  
Nitrogen Fertilizer ◽  
Time Series Data ◽  
Fertilizer Application ◽  
Imputation Method ◽  
N Fertilizer ◽  
Series Data ◽  
Specific Information ◽  
Model Studies ◽  
Multiple Imputation Method

Abstract. This paper provides a method for constructing a new historical global nitrogen fertilizer application map (0.5° × 0.5° resolution) for the period 1961–2010 based on country-specific information from Food and Agriculture Organization statistics (FAOSTAT) and various global datasets. This new map incorporates the fraction of NH4+ (and NO3−) in N fertilizer inputs by utilizing fertilizer species information in FAOSTAT, in which species can be categorized as NH4+ and/or NO3−-forming N fertilizers. During data processing, we applied a statistical data imputation method for the missing data (19 % of national N fertilizer consumption) in FAOSTAT. The multiple imputation method enabled us to fill gaps in the time-series data using plausible values using covariates information (year, population, GDP, and crop area). After the imputation, we downscaled the national consumption data to a gridded cropland map. Also, we applied the multiple imputation method to the available chemical fertilizer species consumption, allowing for the estimation of the NH4+/NO3− ratio in national fertilizer consumption. In this study, the synthetic N fertilizer inputs in 2000 showed a general consistency with the existing N fertilizer map (Potter et al., 2010) in relation to the ranges of N fertilizer inputs. Globally, the estimated N fertilizer inputs based on the sum of filled data increased from 15 Tg-N to 110 Tg-N during 1961–2010. On the other hand, the global NO3− input started to decline after the late 1980s and the fraction of NO3− in global N fertilizer decreased consistently from 35 % to 13 % over a 50-year period. NH4+ based fertilizers are dominant in most countries; however, the NH4+/NO3− ratio in N fertilizer inputs shows clear differences temporally and geographically. This new map can be utilized as an input data to global model studies and bring new insights for the assessment of historical terrestrial N cycling changes. Datasets available at doi:10.1594/PANGAEA.861203.

scholarly journals Comparison of Selected Multiple Imputation Methods for Continuous Variables – Preliminary Simulation Study Results

2019 ◽  
Vol 6 (339) ◽  
pp. 73-98
Author(s):  
Małgorzata Aleksandra Misztal
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Missing Values ◽  
Imputation Accuracy ◽  
Imputation Method ◽  
Data Sets ◽  
Continuous Variables ◽  
Imputation Methods ◽  
Study Results ◽  
Almost All

The problem of incomplete data and its implications for drawing valid conclusions from statistical analyses is not related to any particular scientific domain, it arises in economics, sociology, education, behavioural sciences or medicine. Almost all standard statistical methods presume that every object has information on every variable to be included in the analysis and the typical approach to missing data is simply to delete them. However, this leads to ineffective and biased analysis results and is not recommended in the literature. The state of the art technique for handling missing data is multiple imputation. In the paper, some selected multiple imputation methods were taken into account. Special attention was paid to using principal components analysis (PCA) as an imputation method. The goal of the study was to assess the quality of PCA‑based imputations as compared to two other multiple imputation techniques: multivariate imputation by chained equations (MICE) and missForest. The comparison was made by artificially simulating different proportions (10–50%) and mechanisms of missing data using 10 complete data sets from the UCI repository of machine learning databases. Then, missing values were imputed with the use of MICE, missForest and the PCA‑based method (MIPCA). The normalised root mean square error (NRMSE) was calculated as a measure of imputation accuracy. On the basis of the conducted analyses, missForest can be recommended as a multiple imputation method providing the lowest rates of imputation errors for all types of missingness. PCA‑based imputation does not perform well in terms of accuracy.

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