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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2answers
36 views

how to check missing data is missing at random or not?

I have a survey data, in which there are some missing data (not answered questions). I threw away those where the whole page(s) questions were missed, but there are still some with unanswered ...
3
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1answer
32 views

Is it advisable to include variables that are not in the full model in the imputation model?

I have a dataset with several missing values. I know that the missing is MNAR. I'm trying to use MICE to impute the data; then apply a survival model on the imputed data. The MICE paper advises that ...
0
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0answers
2 views

Randomly delete/replacing X% of the data using R (or other tool) [closed]

I am quite new to R other statistical tools. In order to measure the performance of an imputation technique, I want to randomly delete or replacing X% of the data so I can compare it with the original ...
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4answers
34 views

Good references on learning how to deal with missing data/imputation

Could you recommend up-to-date and well-supported references on the topic of data imputation?
1
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2answers
80 views

Choosing a better model and dealing with missing data?

I am trying to create a logistic regression model to predict whether a customer given a loan will be a bad or a good customer: bad meaning missing a certain amount of payments and good meaning ...
0
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1answer
25 views

What imputation methods can be used for missing not at random covariate values in a survival analysis?

I'm new to survival analysis and trying to understand how to use it properly. My dataset is a time series dataset where most dependent variable values are available, 2 dependent variable values are ...
3
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1answer
44 views
0
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0answers
9 views

Is there imputation algorithm that accounts for ranking in the data?

I've been using mice but it only works for low ranks (3 and lower). So if i have 5 columns I want those to be imputed as values from 1 to 5, no repeats. If I have 4 columns I want those to be imputed ...
1
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0answers
9 views

Imputation introduces negative values when using imputePCA() from the missMDA package in R?

I am testing out various imputation methods on my data and would like to use imputePCA. It imputes the missing values with no error messages, but when I check the completeObs matrix some of the ...
0
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1answer
10 views

Is there a way to set the desired range of an imputation algorithm?

Goal: I am interested to learn if there is a way to set the range of an imputation algorithm for Missing Not at Random (NMAR) data, such as Multiple Imputation or Maximum Likelihood Estimation. ...
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0answers
13 views

Imputing skewed variable?

I have a data set with missing values in the IVs. I intend to use MI and in particular PMM for the numerical variables. One of them is very skewed and has many 0s so I can't log tranform it. My ...
0
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1answer
40 views

How to perform SVD to impute missing values, a concrete example

This might be a very stupid question, but I have read the great comments regarding how to deal with missing values before applying svd, but I would like to know how it is going to work, if I apply it ...
0
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1answer
10 views

Imputation for Time Series of Accumulated Value

I have a regular time series of accumulated values of a variable (usage) with some missing (sometimes consecutive) intervals. Is there an imputation method that methodologically considers this ...
0
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0answers
28 views

Should data be normalized before or after imputation of missing data?

I am working on a metabolomics data set of 81 samples x 407 variables with ~17% missing data. I would like to compare a number of imputation methods to see which is best for my data. Is there a ...
1
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1answer
49 views

Is imputation needed for $0$'s in regression?

I am working on a dataset of 2000 records using SAS Enterprise Miner in order to predict insurance payment (compensation) from insurer, a motor insurance company, to its customers. Though there are no ...
1
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0answers
19 views

Why do I get an error when trying to impute missing data using PMM in MICE package in R?

I am trying to compare imputation methods for an 81 samples x 407 variables data set with ~17% missing values. Some of the variables will be correlated, some highly, that is the nature of the data. ...
0
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1answer
22 views

Which Imputation method to use in MI

I'm making a predictive model. I'm thinking of using MI but not sure which imputation method to use. Is there some metrics or graphs one can compute on the data to see which method is best for which ...
0
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0answers
8 views

RBF SVM image classification with missing features

I have been working on a image classification problem (face recognition especifically) and my test set has some missing values: for some face images only the upper half is avaliable and for others the ...
1
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1answer
22 views

When is it ok to MI Data with MNAR predictor without further instructions

I have a data set with predictors that are mostly MAR(supposedly), however I do also have one that is likely to be MNAR in the sense that the missing of that predictor depends on an unobserved ...
1
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1answer
30 views

Opinion on when to impute data

I work with longitudinal data that tends to be “messy.” For example, we collect eye-tracking and physiological measures at multiple time points in young children. This causes data to be missing not ...
0
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0answers
27 views

Unequal timepoints longitudinal data with missing values

I have a longitudinal data with unequal time points with missing values. I am looking for methods to impute the missing data. I looked at R packages NORM and AMELIA II and SAS procedures PROC MI. All ...
1
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0answers
22 views

Flexible prediction with neural network or other method

I want to use neural network for my first time, but I need to check if it fits for my case. So, my idea is to teach a model on data like Y = f(X1, X2...XN) and ...
3
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1answer
49 views

How to deal with dropouts from a waiting list control group?

Many treatment studies compare a treatment group with a waiting list control group, for example to adjust for spontaneous remissions. Unfortunately, many more participants drop out from the waiting ...
0
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1answer
33 views

Imputation using regression

I have a table of 4 variables where some of the data is missing and I need to impute the missing values, my table is as follows: ...
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0answers
19 views

Distinguishing between zeros and missing data

I have a panel data set with 12,000 observations of daily counts of visitors to a number of recreational sites. The data has been given to me with missing values recorded as zeroes. There are also "...
0
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1answer
52 views

Problems with Missing values

I have a data set for a predictive model(predicting survival rate with certain acute medical condition on some animals) with 25 predictors where around 30% of the predictors are complete, 3 predictors ...
0
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2answers
28 views

Holt-Winters for Imputation

I have found Holt-Winters seasonal method a very decent method for forecast, specifically for cases where more recent observations are more representative of the near future. The method equally sounds ...
15
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5answers
3k views

A 6th response option (“I don't know”) was added to a 5-point Likert scale. Is the data lost?

I need a little bit of help salvaging the data from a questionnaire. One of my colleagues applied a questionnaire, but inadvertently, instead of using the original 5-point Likert scale (strongly ...
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0answers
10 views

Can I use estimated relationships from one model to impute predicted response in another model?

I simplified the problem below to the core issue: I need to estimate the future expected incremental response (Sales/Web visits) for a company (Call it Company B) due to introducing a new medium, ...
0
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1answer
37 views

Popular (single) imputation methods for ordinal variable

I am setting up a monte carlo simulation study in R for a comparison between several imputation methods for ordinal variables. So far, I am planning to use the following imputation methods: Multiple ...
0
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0answers
10 views

What are some graphical devices for observing effects of imputing data?

I am interested in various imputation techniques and how certain imputation techniques work better than others. Say I had some data set with missing values. I usually try to determine the reason why ...
0
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0answers
16 views

Randomly erase data given sparseness

I would like to sparsify a data frame given the sparseness value. That is, I would like to randomly delete some data from the dataframe to later use an imputation algorithm to impute the missing ...
3
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1answer
43 views

Missing value imputation with nearest neighbour

I'm using k-nearest neighbour imputation method for missing values. This method has two tuning parameters: k and the distance metric. I see two options for applying this imputation method: Inside ...
1
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0answers
26 views

How to test whether social network properties predict a binary outcome?

I'm looking to see if whether social network properties (such as different measures of centrality) predict a binary choice. The first part of the question is, what is the best method to do this? I ...
0
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0answers
49 views

Multiple imputation for predictive analysis using mice package in R

I am using the mice package to impute some missing values, and it works nicely. I am facing a tricky strategic question though. Basically, I am working on predictors of myocardial infarction (time 3)...
0
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1answer
79 views

K-nearest neighbour imputation of missing values

I have a dataset where the columns correspond to features and the rows correspond to data points. I have around 5'000 data points and 8 features. Now, I would like to impute the missing values with ...
0
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0answers
14 views

Imputation and sampling weights

a student of mine has around 1650 cases with missing data (under 5% per variable). She wanted to do a single regression imputation in SPSS (I know that this is not the best method but that does not ...
0
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0answers
144 views

Difference between imputation and interpolation?

When dealing with data sets that have missing values, imputation replaces missing values with substituted values while interpolation replaces missing values with calculated values within some range. ...
0
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0answers
14 views

SPSS Mixed model with imputed data and model LLC scores

I am supposed to build several models (it is multilevel analysis of students from different schools). I used multiple imputation for missing data after running MVA and pattern analysis. Now I have 5 ...
1
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0answers
63 views

Data Imputation with Amelia on large dataset: Taking very long time [closed]

I have 761,592 obs for 31 variables on users behaviours towards online ads. Out of 31 variables, 28 are categorical. Many cat. variables have more than 10 categories. I am using Amelia for missing ...
0
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2answers
81 views

Framework of multiple imputation

I read this paper about ("Multiple Imputation For Missing Data: What Is It And How Can I Use It?")(http://www.csos.jhu.edu/contact/staff/jwayman_pub/wayman_multimp_aera2003.pdf) Does any one have ...
0
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0answers
15 views

Imputation of predictors missing data for logistic modelling

I never used imputation of missing data and I would like to understand the effect of imputation in a specific scenario. Lets suppose that I have a dataset whit some predictors variable and one binary ...
0
votes
0answers
17 views

Lower Denomination Imputation

Before I start, I will point out that I am very new to imputing data and so any advice would be greatly appreciated. Apologies if there is an obvious answer that I am overlooking. I have a data set ...
0
votes
2answers
80 views

Change mean imputation in MICE package

MICE Steps The chained equation process can be broken down into six general steps: Step 1: A simple imputation, such as imputing the mean, is performed for every missing value in the dataset. These ...
3
votes
1answer
55 views

Financial time series data: Imputing before or after calculating returns?

I've got several time series of daily prices ($(p_t^j)_{t=1,\dots,n}$ ) of different tradable cards $j=1,\dots,k$. I'd like to calculate the time series of the (log)returns $r_t= \log(\frac{p_t}{p_{t-...
0
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0answers
50 views

compare different Imputation method by RMSE

My original dataset : ...
0
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2answers
98 views

How to create a variable that is present in test data set but not in train?

Im try to do a classification but i have a variable production budget which is present in test dataset and not in train. so how do i proceed. could i impute that variable somehow. i dont want to drop ...
3
votes
0answers
20 views

Imputation of a (weird) multivariate outcome

I am working with a dataset in which the outcome of interest is a vector of dates of particular events: (date_1,date_2,date_3,...,date_n). Some of these outcome vectors are completely missing, but I ...
0
votes
1answer
227 views

How to know which imputation is best for impute my dataset from Multiple imputation by using mice

I used mice package to impute the missing value as follows: ...
0
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0answers
17 views

Multiple imputation: manual calculation of new values

I need to impute some data but I find myself in the impossibility of using the model I will eventually use for my analysis as a model of imputation. I have reasons to believe that the var to be ...