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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24 views

Data imputation for meta analysis using mice package in R

I have a data-set with 32 effect size estimates- only 11 of which report a value for the continuous moderator of interest (the samples anxiety level). A complete case analysis (restricted to the 11 ...
4
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2answers
26 views

Single-Imputation on Age Needed?

I am hoping someone can help me with this answer. I did a Single Imputation on my data set for age (<5% missing). My adviser asked the following "It’s strange to me to impute a demographic ...
0
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1answer
28 views

Data imputation for statistical analysis

Here is the situation: I have an individual level data set $X$ where each row is a person $i$ and each column denote characteristics of $i$. The problem is that my data is missing an important ...
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0answers
43 views

Random Forest for data imputation

Currently I am using Random Forest approach for Missing Values Imputation from missForest package in R. I faced the following problem: the algorithm works much longer than any other imputation ...
0
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1answer
55 views

Imputation model: Pooled model is insignificant. How to interpret?

I have ordinal data on three IVs ranging from 1 to 5 as below: IV1: Not at all Important - Very Important IV2: Not at all Satisfied - Very Satisfied IV3: Performs much Worse - Performs much better ...
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0answers
40 views

Mean imputation/estimation of missing data

Could someone please refer me to papers that have imputed the mean to missing values of a continuous variable? (i.e. papers that have used mean imputation) I have imputed my missing IMD values using ...
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1answer
30 views

Difference between imputation and forecast

what is the difference between imputation and forecasting? All i know, forecasting is the term used in time series analysis, which means predicting the future value by considering the trend of the ...
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1answer
30 views

R regression with categorical response variable

I have four variables, two are categorical and two are numeric: ...
0
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1answer
21 views

how to impute missing values on numpy array created by train_test_split from pandas.DataFrame?

I'm working on the dataset with lots of NA values with sklearn and pandas.DataFrame. I implemented different imputation strategies for different columns of the dataFrame based column names. For ...
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1answer
46 views

Stepwise regression modeling using multiply imputed data sets

After multiply imputing data, it is natural to estimate regression models on the data. When multiple predictors are available, sometimes stepwise regression is used for model building (forward ...
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0answers
17 views

Missing Value in Commodities Prices

I am trying to model the prices of four energetic commodities with ARIMA models in R. Unfortunately the price series is not regular, as for some days, like Christmas, no price is given. My series is ...
0
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1answer
25 views

Overfitting on Missing Value Imputations

When performing Missing Value imputations, should we be concerned about overfitting the data? Why or why not? For example: If I impute a variables missing value using a CART regression tree, should ...
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0answers
21 views

J48 Handling Missing Value with Tree based Imputation

Aloha, currently i have some trouble and question zu implement some kind of special missing value handling in WEKA J48 algorithm using WEKA JAVA API. I want to test the performance of SHAPIRO ...
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0answers
21 views

Predict/impute one cell of matrix using all other cells

The question: I want to predict/impute one missing cell of a matrix using the contents of all other cells. Anyone have ideas on how to do this? The context: The matrix is n people's responses to m ...
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1answer
37 views

Imputation and Distributions

Suppose you impute a variable using a normal distribution with mean 10 and sd 5. Is it better to draw 1000 random samples from this normal distribution, take the average, and then use this to impute ...
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1answer
36 views

How to get only positive values when imputing data?

Suppose age is normally distributed with mean 20 and standard deviation 5. How do you ensure that you get only positive values when you sample age from this distribution? I am trying to impute ...
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1answer
53 views

using cluster information in multiple imputation

i need to impute a dataset all categorical variables before doing analysis. I can just simply impute with mode of all data or a variable. I belief that better option will be to classify the subjects ...
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3answers
189 views

using random forest for missing data imputation in categorical variables ( in R)

I have following type of associated data. The following example step to generate associated variable. p number of variables and n is number of observations. ...
1
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1answer
71 views

Individual and overall RMSE for multivariate data

I have a dataset which contains missing values, and I'm using imputation packages (Rs mi and ...
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3answers
310 views

How to handle with missing values in order to prepare data for feature selection with LASSO?

My situation: small sample size: 116 binary outcome variable long list of explanatory variables: 44 explanatory variables did not come from the top of my head; their choice was based on the ...
0
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1answer
33 views

How do you suppress imputed data and only display results for the pooled data?

I've done 15 imputations and am now running various tests on the new data, but all I'm really interested in is the pooled results at the end. The long list of imputations (especially in a test like an ...
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2answers
53 views

Imputing missing observation in multivariate time series

Suppose I have a dataframe consisting of six time series. In this dataframe, some observations are missing, meaning at some timepoints all time series contain a NA-value. In R, one possible imputation ...
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1answer
34 views

Why is collinearity a problem when imputing missing values?

I'm imputing missing values using R's mice package. My data has three numeric variables and a class variable so I am using a ...
0
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0answers
50 views

Validation - correctly compare and validated imputation models

I've seen a lot of interesting questions here about multiple imputation and also great answers that helped me a lot to impute my data. I've used Predictive Mean Matching, EMB and I would like to use ...
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0answers
67 views

Imputation by regression in R

Say I have below example data, where rows are observations and columns are variables, and NAs stand for missing values. ...
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0answers
36 views

Adding values for missing dates in time-series data using SAS

I am having trouble figuring out how to create entries for missing dates in my time-series data. I'm trying to use proc expand and I'm able to get entries for dates between the already existing dates ...
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1answer
20 views

svm manual implementation to find missing values

how svm algorithm used to impute the missing data in a dataset. I need a manual implementation of svm algorithm to find missing values with example
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2answers
112 views

using neighbor information in imputing data or find off-data (in R)

I have dataset with assumption that nearest neighbors are best predictors. Just a perfect example of two-way gradient visualized- Suppose we have case where few values are missing, we can easily ...
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5answers
262 views

How to perform imputation of values in very large number of data points?

I have a very large dataset and about 5% random values are missing. These variables are correlated with each other. The following example R dataset is just a toy example with dummy correlated data. ...
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1answer
88 views

optimal scaling / CATREG for imputed data

I have a data set with 5 different kinds of nutrient statuses and I want to see whether they are associated with categorical / ordinal grades at school. I have multiple covariates which I will ...
2
votes
2answers
108 views

Fast missing data imputation in R for big data that is more sophisticated than simply imputing the means?

I need a package for missing data imputation in R. But since I am dealing with big data, the number of missing data entries can also be high. The packages which impute using mean or median are of ...
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2answers
79 views

Simultaneous imputation of multiple binary variables in R

I have a dataset with multiple correlated binary variables (0/1). Can anyone point me toward a solution for imputing completely random missing values based on information in the other variables? ...
3
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0answers
90 views

Is the following procedure to measure the quality of an imputation ok?

I'd like to compare different kinds of imputation techniques, i.e. methods which allow to fill missing data fields in a data frame. For now, I'm only using the R package ...
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0answers
21 views

Should I use missing data imputation with a model that already allows incomplete data?

I'm just starting to learn about missing data imputation methods, and I'm confused. In every introduction I've read, the author starts by describing listwise deletion and says that it's a bad idea ...
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2answers
61 views

Missing data due to absent parent

I am using the following regression: $$\text{Test score} = \beta_0+\beta_1\text{Mother's employment}+\beta_2\text{Mother's education}$$ where "Mother's employment" is a set of dummy variables ...
2
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0answers
37 views

What to do when exploratory factor analysis results are different for complete-cases and imputed data?

I have a hundred items that I'm performing EFA on, with around 370 complete cases. Using parallel analysis to determine the number of factors to extract, EFA gave 9 factors, all of which make ...
3
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2answers
91 views

Does it make sense to impute year of birth?

This is data cleaning and preparation stage question for me. I apologize if the question is basic, but I am a beginner. I have a dataset of a bit less than 4500 records. This is a survey and ...
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1answer
36 views

Choosing a regression model based on missing values

I'm trying to predict weight change with an intervention from baseline variables. Literature search yields suggests several predictors. Univariate analyses with weight change as dependent and baseline ...
2
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0answers
82 views

Imputation with mice: recode variables before or after imputation?

I am using mice in R, a chained equations (sequential regression) algorithm, to impute a series of polytomous variables (e.g. ...
0
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0answers
20 views

What are the advantages of using a Neural Network to impute data?

What are the advantages and drawbacks of using neural network methods to impute data? Is the bias and total error any higher than other methods (e.g., median or mean method, nearest neighborhood, or ...
3
votes
1answer
109 views

How to deal with invalid data values such as with age (e.g., -99, 0, F1)?

I have a data set that consists of 15 age values. I want to clean the data before doing anything further. I have a few questions about data cleaning and data integrity. What is the best treatment if ...
0
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0answers
42 views

Imputing categorical variables before binarization

I wish to replace the missing values with mode of that categorical variable. In scikit-learn, we can something like Imputer(strategy="most_frequent", axis=0) but ...
0
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0answers
78 views

Using MICE in R: is it possible to impute only sub-sections of the data?

When using the mice library in R to impute data I encounter the following problem. I have a data matrix with missing information ...
2
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0answers
49 views

Does it make sense to impute missing covariate data when the imputed value is a function of other covariates in the regression model?

We are building a model that adjusts for standard covariates (e.g., age, gender) and for the outcome at baseline. It would be ideal to adjust for each subject's baseline value like so: $$ Y = ...
0
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0answers
33 views

Adjusting time series for methodological change during the time period

The issue is that the national bureau of statistics changed permanently the variable calculation methodology during the overall period. Now I have two official time series for the same variable. One ...
1
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0answers
37 views

Confidence estimation for data points in a recommender system

I have a 100 by 100 matrix. Each cell is either 0, 1, or missing (denoted NA). Rows denote 'users' and columns denote 'items'. My goal is to impute the missing values, and provide a confidence level ...
0
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0answers
32 views

What is the difference between hot deck and single stochastic imputation?

From what I understand hot deck imputation involves 2 parts 1) Choosing the donor pool: This based on variables related to the missing variable. Maybe using a regression technique to test for ...
0
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0answers
236 views

How does knnimpute of the preprocess function work?

I am new to R and I use a script I do not completely understand. It preprocesses a dataset for data mining. At one point, the data (stored in fil) should be ...
2
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0answers
30 views

Calculating boxplots with imputed data

I have imputed a dataset which has over 200 variables, and 20 observations. In worst case, 80% of the data is missing, in the best case 100% is available. 5 out of the 20 participants provided data ...
0
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0answers
44 views

Multiple Imputation and two-level data

I have a question on multiple imputation where one variable is the sum of several sub-groups. I have about 5 variables with a significant level of missingness. However I have a sixth variable which ...