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Questions tagged [data-imputation]

Refers to a general class of methods used to "fill in" missing data. Methods used for doing this typically are related to interpolation (http://en.wikipedia.org/wiki/Interpolation) and require assumptions about why the data is missing (e.g. "missing at random")

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imputation FOR random forests

I was wondering what imputation method you would recommend for data to be fed into a random forest model for a classification problem. If you google for "imputation for random forests", you get a lot ...
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Does one impute auxiliary variables (multiple imputation)

Auxiliary variables may have themselves missing values. According to the following website, one (can) include(s) auxiliary variables also as variables to be imputed. Is this common practice? https://...
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Regression when dependent and independent variables come from different datasets

I am trying to figure our the most robust way to combine two different sets and run a regression. The first dataset gives me an outcome value for each of several categorical treatment variables, each ...
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MissForest for SurveyData

Hello fellow data scientist, I currently reading the paper by Stekhoven & Brühlmann about MissForest. I was wondering how to deal with variables that are restricted by domain knowlege. I.e. no ...
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Are there any clustering algorithms that do not exclude/impute missing data?

From my understanding, clustering algorithms require complete data. Based on this, if there are missing values in my dataset I have two options: Impute missing information using some sort of ...
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Pool redundancy analysis results from multiple imputations of missing data

I have a dataset that includes missing values, and I would like to carry out redundancy analysis using multiple imputation to fill in the missing values. So far, I have successfully created multiple ...
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36 views

Dealing with measurements falling outside of the theoretical range/boundaries of the data

Imagine I am measuring a bounded variable (with a maximum possible value above which the data doesn't make sense) and I end up with the following dataset with my measurements and measurement errors as ...
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how to impute the missing values by woe

we have calculated the woe values but how to impute the missing values by woe values. if we do that the column scale is getting disturbed. So how to proceed with imputing the missing values
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Multiple Imputation in SPSS for RCT

I have conducted a randomised controlled trial design (2 groups - experimental and control) with data collection at two time points (T1 and T2). I want to use the Multiple Imputation Method in SPSS to ...
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missForest Data imputation vs. MICE using RF as imputation method?

Is the missForest package a special case of MICE using Random Forest as imputation (for just a single imputation)? The missForest algorithm is described here: https://academic.oup.com/bioinformatics/...
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37 views

Problems estimating a “Bayesian version of FIML”

I am anticipating that my question exposes some basic ignorance about how mcmc works, but here we go: In an attempt to deal with missing data I am trying to simultaneously estimate a regression model ...
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Best resources on imputation in R [duplicate]

This is my first question at stats. I need to impute some factors and numbers in my data set in R. What are my best options regarding packages and also a source to read more about the theory.
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SPSS correlation matrix from multiple imputation dataset, p-values and df

I use a multiple imputation dataset to create a matrix of correlations between a few variables of interest. The pooled correlation matrix does not show significance or df, unlike the original matrix ...
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What value to impute for informative NA values in R without misleading model

I'm building a model (random forest) in R to predict a rare event (scoring a goal in soccer). I have event-level data, which provides a log of all the actions (pass, tackle, foul, save, shot, goal) ...
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What type of imputation should I use?

I am learning how to handle missing values in a dataset. I have a table with ~1million entries. At the moment I am trying to deal with a small number of missing values. My data concerns a bicycle-...
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20 views

How to run multiple imputation by predictive mean matching in multivariate missing data?

I'm running predictive mean matching for multivariate missing data for my final project, but how does the algorithm of predictive mean matching work on some variables if there are missing value in the ...
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1answer
50 views

How to handle or impute large number of missing values?

I am trying to use this dataset to build a predictive model. The hubway.db file contains 3 tables. One of which is is bike_trips...
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Missing values in a variable depending on the values of another variable

I'm working on a public procurement dataset where I have information on all the participants that presented offers in 358 tenders. I'm analysing relationships between all the companies of the dataset (...
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28 views

Missing data imputation

I'm working on a public procurement dataset where I have information on all the participants that presented offers in 358 tenders. I'm analysing relationships between all the companies of the dataset (...
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How can missing values in the target variable be substituted using Python?

I have a dataset with some missing values in the target variable (label). Can I use clustering to find those missing label values? What other methods can be applied to resolve such an issue in Python? ...
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SAS vs. Stata: Interpreting Tobit scale and sigma Coefficients

I'm trying to replicate a SAS script that imputes censored wage observations using a Tobit model in Stata. However, I'm confused ...
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Machine Learning, Imputing values that should be blank

Sometimes data sets contain variables that indicate the presence of an event and the value that represented the event. As an example say a teacher wants to predict the grades of his students. Some of ...
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Is it ever recommended to use mean/multiple imputation when using tree-based predictive models?

Everytime that I am making some predictive model and I have missing data I impute categorical variables with something like "UNKNOWN" and numerical variables with some absurd number that will never be ...
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71 views

Target Encoding: missing value imputation before or after encoding

I want to perform a target encoding for my categorical features although I am not sure when to perform the data imputation if any of them has missing values. Let's say I have a few continuous features,...
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How do I create scale scores from item-level multiply imputed data in R?

I have been able to create 5 multiply imputed datasets using the mice package on R. I computed at the item level so now I have to calculate scale scores for each of the 5 imputed datasets before ...
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SPSS regression imputation

I have a dataset of 45 observations (participants), with variables on demographic data and standardized tests. Two standardized test variables are such that they have missing values on only one ...
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Can anyone help me with step by step procedure in MATLAB for missing data imputation using Singular Value Decomposition (SVD [duplicate]

I need step by step procedure for imputing data using SVD in Matlab. Please provide resources if any !
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Are VAE used for missing data imputation in multivariate time series? If not, what is used?

Multivariate time series are, to the best of my understanding, one of the few cases where Deep Learning still hasn't had its AlexNet moment. I'm especially interested to the case where most of the ...
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Degrees of freedom after multiple imputation

Goodmorning everyone, In my research project, I made use of multiple imputation to replace missing values.SPSS lets me then run most of the tests on the imputed data set and provides output for 5 ...
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Large discrepancy between complete-cases and imputed data

I would like to conduct a survival analysis using a dataset with approximately 12,000 participants (1100 events). However, complete data are available for only 9500 participants (820 events). I have ...
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How do I impute data that is only partially missing?

I want to impute some missing data. I am interested in the number of months someone was unemployed between ages 18-21. This variable is bounded at 0-48. However, for some individuals, I have partial ...
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Is Structurally Missing Data a subset of Missing at Random Data?

I'm quite familiar with MCAR, MAR and MNAR (NMAR) data but I have just come across a new (for me) term: Structurally Missing Data (SMD). According to this page, Structurally missing data is data ...
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Polynomial regression for missing value imputation

I am trying to impute missing values by fitting higher degree polynomial. I have highly autocorralated time series meaning each value at t must be close to t-1. There are some noise and missing ...
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Model to Impute strata data according to higher level data

I've got regional level data and I want to impute said data on a county level strata (smaller strata with respect to the regional strata). I know I can do that if I have a series of variables on the ...
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nnet with predictors with missing values

I got about 10 Variables with customer spendings and another 10 variables with background information (like homecountry, average income etc.). I want to use nnet to impute missing values in the ...
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1answer
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Recommended methods for replacing missing data?

We conducted a pre-post attitudinal survey measuring “Attitudes toward STEM” (28-items; α > .90) and “Multi-Ethnic Identity” (12-items; α > .90) among 50 middle schoolers. Students skipped items ...
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How to localize points from an incomplete distance matrix in R?

Suppose you have 3 shops and 2 supply units, and you only know the 6 pairwise (Euclidean, assuming 2D) distances between each shop and each supply unit, but not the pairwise distances between the ...
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1answer
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Imputing and handling class imbalance

I have data with missing values. My $y$ is imbalanced (20% to 80%). a) is it at all possible to balance (e. via Smote) and Impute (e. via Mice) or will the results become too unreliable? b) if a) ...
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instance propagation

I have read about label propagation where you have aggregated labels (y_g) but instance level features (x_i). Is there also literature about problems where features are (also) aggregated, and we want ...
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Partial imputation of missing dates

I'm working with dataframes (one for each of 185 locations) that shows sums of occurrences for each calendar date. There are no 0 values for occurrences in the entire dataset. There are several ...
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1answer
40 views

Missing data in regression

I am researching the effect of different marketing mix variables (e.g., price promotion, innovation) on the market share. More specifically, I want to analyze the effect of different marketing mix ...
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1answer
253 views

Predicting spendings overall and spendings for subcategories

I have a Dataset containing information about spendings of customers in various shops. There are 10 spending variables related to some categories (like spendings on clothing, spendings on hardware, ...
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1answer
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Regression with missing Y’s

I use publicly available EU-Silc data to estimate the market price of social dwellings (subsidized dwellings). However my X variables are almost perfectly available,...
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Imputation and nested cross-validation

I am planning to do a nested cross-validation analysis using regularized regression. The inner loop will be used for model tuning and the outer loop for model assessment (test set). Because some data ...
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1answer
28 views

Can I correct for randomly missing data where missingness is has a known relationship to the error term?

Suppose I have a population of observations I want to model as being drawn from some distributional family, which I believe adequately represents the true distribution. My goal is to estimate the ...
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Imputed values into Shapefile / fortified shapefile

I currently have a shape file with approximately 20 numeric variables. Several of these variables have missing values. As this is a shapefile I do not think using the median or mean as a form of ...
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How to use MICE in R to fill missing values in test set?

It seems that MICE does not have a "predict" function which allows to use a fitted mids object to predict the missing values in test data set. I can certainly ...
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1answer
47 views

Imputing nested time series data with R

Does anyone know what is the superior algorithm to impute data in time series? I had strong dropouts over time because it was free to participants how many times to participate in my study (otherwise ...
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1answer
51 views

Proper Imputation and bias-correction on degrading signal with Kalman Filtering?

A signal degrades in its quality. Some signals are far more robust to degradation while others are not. We will simulate degradation by randomly removing values from a function and then applying ...
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Missing data imputation that can handle large data

I am looking for a reasonably scaling missing data imputation approach for big data (e.g. a well-scaling version of kNN - the standard versions we tried so far just ran out of memory) that fulfills ...