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A comparison study of nonparametric imputation methods
2010
Statistics and computing
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Empirical studies show that the nearest neighbor imputation method is most effective among these imputation methods for estimating a finite population mean and for classifying the species of the iris flower ...
Two common approaches to imputing the missing values are the nonparametric regression weighting method and the Horvitz-Thompson (HT) inverse weighting approach. ...
this comparison study. ...
doi:10.1007/s11222-010-9223-y
fatcat:yicehjcltffenehecrizw4do54
Nonparametric autocovariance estimation from censored time series by Gaussian imputation
2009
Journal of nonparametric statistics (Print)
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Comparison of nonparametric and parametric imputations In general, it is well known that a nonparametric estimate loses some efficiency in comparison to a parametric estimate when a correctly specified ...
Section 2 describes the nonparametric estimation of the autocovariance function and its implementation within the imputation method. Section 3 reports the results of a simulation study. ...
doi:10.1080/10485250802570964
pmid:20072705
pmcid:PMC2804993
fatcat:soetrvoiabbg7eg2jr5xow3oee
Nonparametric multiple imputation for receiver operating characteristics analysis when some biomarker values are missing at random
2011
Statistics in Medicine
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The proposed imputation methods provide a platform for a full range of ROC analysis, and hence are more flexible than existing methods that primarily focus on estimating the area under the ROC curve (AUC ...
While a direct application of standard nonparametric imputation is robust to model misspecification, its finite sample performance suffers from curse of dimensionality as the number of auxiliary variables ...
While we focus on the K-NN nonparametric imputation, other nonparametric methods can be readily adopted for imputation, say, kernel methods [22] . ...
doi:10.1002/sim.4338
pmid:22025311
pmcid:PMC3205437
fatcat:cxefzryqtvfonphnlownbnanhy
Investigation of Missing Responses in Q-Matrix Validation
2018
Applied Psychological Measurement
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Number of attributes and items have an impact on performance of both methods as well. Results of a real data example are also discussed in the study. ...
Results of the simulation study show that both validation methods perform better when missing responses are imputed using EM imputation or logistic regression instead of being treated as incorrect and ...
Estimates of both validation methods for complete data (i.e., MR = 0%) were used as the baseline for comparison. Missing imputation approaches. ...
doi:10.1177/0146621618762742
pmid:30559573
pmcid:PMC6291893
fatcat:y4p4rut33bga3ae6abyligmb4a
Nonparametric comparison of two survival functions with dependent censoring via nonparametric multiple imputation
2009
Statistics in Medicine
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Based on the imputing risk set, a nonparametric multiple imputation method, Kaplan-Meier imputation, is used to impute a future event or censoring time for each censored observation. ...
We extend our previous work on estimation using auxiliary variables to adjust for dependent censoring via multiple imputation, to the comparison of two survival distributions. ...
The simulation study shows that the use of this nonparametric multiple imputation method can lead to a valid log-rank or Wilcoxon test even in the presence of dependent censoring. ...
doi:10.1002/sim.3480
pmid:18991250
fatcat:d3aprvbj2nfa5f7twz4w6knvxy
Kernel smoothing as an imputation technique for right-censored data
2020
Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering
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A simulation study is conducted to show the benefits of the method, and it is also compared with Ordinary Least Squares (OLS) based imputation, which is one of the widely used imputation methods and works ...
This study introduces the kernel smoothing method as an imputation method that takes into account the structure of the data and the individual effects of the accessible data points with kernel weights. ...
A simulation study is conducted to show the benefits of the method, and it is also compared with OLS based imputation method, which is one of the widely used imputation methods and studies similar to the ...
doi:10.18038/estubtda.817979
fatcat:72c7hbwzwndprhzejtimg6hpda
Nonparametric imputation method for nonresponse in surveys
[article]
2017
arXiv
pre-print
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A new imputation method for item nonresponse in surveys is proposed based on a nonparametric estimation of the functional dependence between the variable of interest and the auxiliary variables. ...
Many imputation methods are based on statistical models that assume that the variable of interest is a noisy observation of a function of the auxiliary variables or covariates. ...
This research was supported by the Swiss National Science Foundation, project number P1NEP2 151904 (CH) and the Natural Science and Engineering Research Council of Canada (RVC). ...
arXiv:1603.05068v2
fatcat:mbmx5oiymfdfvakb2qoe4jnpui
AN EFFECTIVE TECHNIQUE OF MULTIPLE IMPUTATION IN NONPARAMETRIC QUANTILE REGRESSION
2014
Journal of Mathematics and Statistics
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In this study, we consider the nonparametric quantile regression model with the covariates Missing at Random (MAR). ...
We propose an effective and accurate two-stage multiple imputation method for the model based on the quantile regression, which consists of initial imputation in the first stage and multiple imputation ...
The work was supported by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (Grant No. 10XNL018). ...
doi:10.3844/jmssp.2014.30.44
fatcat:iu2o4whxzjgfvl2ihms3vmg5sy
TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits
2019
American Journal of Human Genetics
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Therefore, to improve on this, we employ a nonparametric Bayesian method that was originally proposed for genetic prediction of complex traits, which assumes a data-driven nonparametric prior for cis-eQTL ...
The nonparametric Bayesian method is flexible and general because it includes both of the parametric imputation models used by PrediXcan and FUSION as special cases. ...
ROS/MAP study data were provided by the Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL. ...
doi:10.1016/j.ajhg.2019.05.018
pmid:31230719
pmcid:PMC6698804
fatcat:tp4ugbbt45eg5cbekl6xwlfcje
The Utility of Nonparametric Transformations for Imputation of Survey Data
2014
Journal of Official Statistics
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., they may be easily applied in conjunction with a wide array of imputation techniques), the proposed methodology is applied here with an algorithm for imputation in the USDA's Agricultural Resource Management ...
Missing values present a prevalent problem in the analysis of establishment survey data. ...
Further, the nonparametric transformations result in imputations that appear to adequately preserve the quantities studied here (though there may be evidence of a moderate decrease in the variance of P784 ...
doi:10.2478/jos-2014-0043
fatcat:r5bc73l7rrhb5becihot7yveyy
Imputation Using the Singular Value Decomposition: Variants of Existing Methods, Proposed and Assessed
2020
International Journal of Innovative Computing, Information and Control
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The aim of this study is to present new variants of the basic method and to determine which iterative scheme produces the higher quality imputations. ...
For this a simulation study was performed, and from incomplete matrices the quality of the imputations was assessed by estimating their uncertainty and by other criteria such as variance, bias and mean ...
The authors of this paper acknowledge the High Performance Computing Center -ZINE of Pontificia Universidad Javeriana for assistance during the simulation study. ...
doi:10.24507/ijicic.16.05.1681
fatcat:37zmgym4o5butopkkgdupcbjje
Estimation of Right-Censored Data with Partially Linear Models: A Comparison for Different Censorship Solution Methods
2019
Insights in Biomedicine
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In this paper, to handle the right-censored data which is the most common kind of censored data, three different solution methods are introduced. ...
Therefore, it is hard modelling and analyzing datasets accurately especially in medical and clinical studies. ...
kNN imputation method: Imputation is a class of methods that focuses to fill the censored observations with estimated ones. ...
doi:10.36648/2572-5610.4.1.55
fatcat:nk4oyrcilrbp5kqll6ngj4nvta
Estimating individual tree growth with nonparametric methods
2003
Canadian Journal of Forest Research
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or underestimates Examination of different non-parametric imputation methods to reduce regional biases in growth estimates Comparison of localized estimates to estimates obtained with nonspatial imputation ...
• Local non-parametric growth estimates for Kuusamo in Finnish
Lapland
• Localization of growth estimates using non-parametric imputation
methods
• Comparison of different non-parametric growth imputation ...
doi:10.1139/x02-162
fatcat:7qc462wcsveonlnod5vymx36zu
Nonparametric spectral density estimation with missing observations
2009
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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The practical performance of the method is illustrated by a simulation study. ...
In this paper this principle is applied to develop a new method for nonparametric spectrum estimation with missing data. ...
SIMULATION STUDY A simulation study was conducted to evaluate the practical performance of the MCSC-Algorithm. ...
doi:10.1109/icassp.2009.4960265
dblp:conf/icassp/LeeZ09
fatcat:sqk242eqqbfmlnnxzvt5e4t4ke
Variance estimation when donor imputation is used to fill in missing values
2009
Canadian journal of statistics
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We evaluate its performance in a simulation study when nearest-neighbour imputation is used. ...
Our variance estimator is valid irrespective of the magnitude of the sampling fractions and the complexity of the donor imputation method. ...
k
μ and 2
k
σ is clearly superior to
Table 2 : 2 Comparison of variance estimation methods when k σ are estimated nonparametrically. k
μ and 2
RB in %
RRMSE in %
Method
LIN
y
NLIN ...
doi:10.1002/cjs.10019
fatcat:msut4kpi5vf55ilqr3rev2hxdu
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