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Some Remarks on the Data Imputation Using “missForest” Method

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Misztal M., University of Lodz D. o. S. M.
Acta Universitatis Lodziensis. Folia Oeconomica
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2013
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vol. 285
EN
Missing data are quite common in practical applications of statistical methods and imputation is a general statistical method for the analysis of incomplete data sets. Stekhoven and Bühlmann (2012) proposed an iterative imputation method (called “missForest”) based on Random Forests (Breiman 2001) to cope with missing values. In the paper a short description of “missForest” is presented and some selected missing data techniques are compared with “missForest” by artificially simulating different proportions and mechanisms of missing data using complete data sets from the UCI repository of machine learning databases.
PL
W pracy Stekhovena i Bühlmanna (2012) zaproponowano nową iteracyjną metodę imputacji (nazwaną „missForest”) opartą na metodzie Random Forests Breimana (2001). W niniejszym artykule omówiono metodę „missForest” i porównano kilka wybranych technik postępowania w sytuacji występowania braków danych z metodą „missForest”. W tym celu wykorzystano podejście symulacyjne generując różne proporcje i mechanizmy powstawania braków danych w zbiorach danych pochodzących głównie z repozytorium baz danych na Uniwersytecie Kalifornijskim w Irvine.
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Imputation of Missing Data Using R Package

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Misztal M., Uniwersytet Łódzki; Wydział Ekonomiczno-Socjologiczny; Instytut Statystyki i Demografii
Acta Universitatis Lodziensis. Folia Oeconomica
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2012
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vol. 269
EN
Missing data are quite common in practical applications of statistical methods. Imputation is general statistical method for the analysis of incomplete data sets. The goal of the paper is to review selected imputation techniques. Special attention is paid to methods implemented in some packages working in the R environment. An example is presented to show how to handle missing values using a few procedures of single and multiple imputation implemented in R.
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Methods for imputation of missing values and their influence on the results of segmentation research

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Gąsior M., Skowron Ł.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2016
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issue 4 (54)
61-71
EN
The lack of answers is a common problem in all types of research, especially in the field of social sciences. Hence a number of solutions were developed, including the analysis of complete cases or imputations that supplement the missing value with a value calculated according to different algorithms. This paper evaluates the influence of the adopted method for the supplementation of missing answers regarding the result of segmentation conducted with the use of cluster analysis. In order to achieve this we used a set of data from an actual consumer research in which the cases with missing values were deleted or supplemented with the use of various methods. Cluster analyses were then performed on those sets of data, both with the assumption of ordinal and ratio level of measurement, and then the grouping quality, as expressed by different indicators, was evaluated. This research proved the advantage of imputation over the analysis of complete cases, it also proved the validity of using more complex approaches than the simple supplementation with an average or median value.
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Jackknife winsorized variance estimator under imputed data

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Sohil F., Sohail M. U., Shabbir J., Gupta S.
Statistics in Transition new series
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2022
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vol. 23
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issue 2
17-32
EN
In the present study, we consider the problem of missing and extreme values for the estimation of population variance. The presence of extreme values either in the study variable, or the auxiliary variable, or in both of them, can adversely affect the performance of the estimation procedure. We consider three different situations for the presence of extreme values and also consider jackknife variance estimators for the population variance by handling these extreme values under stratified random sampling. Bootstrap technique ABB is carried out to understand the relative relationship more precisely.
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