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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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 ...
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20 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 ...
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16 views

Listwise deletion then imputation?

I have a data set described in this post and as mentioned I have two predictors with 25% and 20% missingness that is partially due to the fact that they can only be measured when they are above ...
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1answer
46 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 ...
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23 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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4 views

Simple restrictions/constraint for multiple imputation (MICE) in R [migrated]

I want to perform multiple imputation for a set of variables using the MICE package in R. ...
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15 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 ...
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1answer
44 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 ...
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2answers
19 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 ...
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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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7 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, ...
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22 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 ...
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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 ...
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11 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 ...
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0answers
30 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 ...
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22 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 ...
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43 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 ...
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1answer
50 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 ...
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13 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 ...
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70 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. ...
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12 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 ...
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55 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 ...
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2answers
73 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 ...
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14 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 ...
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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 ...
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2answers
69 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 ...
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44 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= ...
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45 views

compare different Imputation method by RMSE

My original dataset : ...
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2answers
75 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 ...
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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 ...
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1answer
152 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: ...
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16 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 ...
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55 views

How to use cross validation when you have missing data & rare events?

I am trying to use repeated cross validation to test my classifier. Moreover, I want to use imputation due to missing values and downsampling due to unbalanced data (I have 88% of my data in the ...
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29 views

SAS HELP: Imputing both continuous and categorical variables in a single dataset

I am trying to impute data for missing observations within a longitudinal dataset following an arbitrary missing data pattern. Both continuous and categorical variables have missing data. I am ...
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20 views

Estimating unexplained variance from multivariate probit output (for imputation protocol)

I have a use case in which survey data underreports program participation, and I need to impute new recipients from within the survey data. There are two (exogenously provided) sub-objectives: ...
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66 views

python classification without having to impute missing values

I have a dataset that is working nicely in weka. It has a lot of missing values represented by '?'. Using a decision tree, I am able to deal with the missing values. However, on sci-kit learn, I see ...
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1answer
187 views

ML covariance estimation from Expectation-Maximization with missing data

Assuming a multivariate normal distribution with missing data, is there a straightforward way to find the maximum likelihood estimate for covariance using an Expectation-Maximization algorithm? NOTE: ...
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44 views

Missing data in time series

What is the minimum percentage of missing values that is permitted in time series models like ARIMA or Exponential smoothing? What is the best method to impute missing time series data?
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30 views

How to combine multiply imputed datasets created with MICE from different cohorts?

I have data from two separate cohorts. If not imputed I would just rbind() the two datasets and analyse. But due to non-random missing imputation was needed for ...
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1answer
77 views

Analysis of imputed datasets in Stata 14

I am relatively new to multiple imputation (and statistical analysis in general), so I apologize if my question seems naïve to more experienced users. I am dealing with a somewhat large dataset ...
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1answer
72 views

Imputation during pre-processing in data mining

I'm trying to comprehend the most appropriate way to utilize machine learning imputation (e.g., KNN) as a pre-processing step in data mining, and I've run into a few questions that don't have clear ...
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1answer
318 views

Uncertainty in random forest imputations from R missForest package

I am in the process of imputing missing values for my data set that contains approximately 20 variables and 3,000 observations. Most of the missing data values are contained in 2 of the variables (one ...
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1answer
627 views

Data imputation with preProcess in caret returns less observations than expected

I wonder why preProcess function from R's caret package used for imputation of dataset's missing values returns less observations than in original dataset? For example: ...
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32 views

Appropriate methodologies for missing value imputation regarding continuous variables in R

I have a data frame in R of 8 continuous variables in the rows and 60 paired observations in the columns. I want to use this data frame in a subsequent analysis along with gene expression data ...
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31 views

filling missing data with other than mean values [duplicate]

What all options are available for filling missing data ? One obvious choice is the mean, but if the percentage of missing data is large, it will decrease the accuracy. So how do we deal with ...
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246 views

Filling missing values for categorical feature

In case of continuous variable, the missing values can be filled by the mean of other values, but in case of categorical feature how should we fill in the missing values?
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84 views

Use Available Pairs Method for Missing Data in OLS

I have renewed interest in handling missing covariate data in OLS using the pairwise covariance matrix estimator, i.e., using all available pairs of variables in computing variances and covariances. ...
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13 views

Which of the following methods captures better estimate of population

Consider the hypothetical setup: X denotes counties in a city. We can have X1, X2,..., Xi. Y denotes hospitals in the city. So let's say for county X1, we have Y11, Y12,..., Y1j, and so on for other ...
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94 views

Cox regression with almost 50% missing data

I am preparing a paper for a medical journal in which I discuss the results of a Cox regression model based on the data retrieved from a database. There are categorical and continuous variables. I ...
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1answer
61 views

Removing the missing values vs. Removing the variables

I know this question seems to be stupid , but in fact it is the first time I deal with a real dataset with a large amount of missing values . The most easiest and fastest way to deal with the missing ...