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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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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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30 views

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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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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49 views

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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38 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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170 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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71 views

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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26 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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34 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
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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 ...
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can I fill missing values by using target variable?

I have a 3 column data with 2 features and 1 target variable. But the first features (numeric) have a large number of missing values. If I use kNN to fill in the missing values, I am wondering can I ...
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89 views

How does Excel interpolate / imputate missing values in time-series when fitting a line to a plot?

I have a scatter plot in Excel (upper part of the screenshot) of time-series data. In-between the values that I plot (to the left), are some missings. I fit a (linear) line to those values and display ...
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What is a good reference on the philosophy of data imputation?

I would like to read something interesting that addresses why and when data imputation is advisable. I have only been able to find technical stuff about particular imputation methods but I want to ...
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Shouldn't we consider larger standard errors for effect measures or outcomes that are converted in meta-analysis?

There are methods to convert effect measures in meta-analysis (pdf). There are also methods to convert outcomes; at least, I am aware of the conversion described in Furukawa et al. (2005) from ...
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How do I impute clustered data that is not time-series data?

The goal of my research is to understand whether MRI imaging characteristics can predict tumor pathology. The data consists of resected tumor samples, with multiple samples per patient. On the MRI, we ...
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Why are missing values MNAR harder to impute than MCAR or MAR?

Reading papers related to the imputation of missing values related to the -omics field, systematically imputation algorithms were less accurate when imputing MNAR compared to imputing MCAR. My ...
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Imputing values with linear regression, valid strategy or creating biases?

I am practicing on the titanic competition from kaggle. In the dataset the Age variable has a number of missing values and I am now left with the choice of what to ...
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Measurements to deterministic value

I have a number of measurements of two variables: the number of products, the weight. Sometimes the weight is missing and sometimes the number of products is missing. I want to use the given data to ...
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Imputing missing data with MICE where each observation has different levels

I have a set of observations that each consists of different levels. For example, I ask a $P$ individuals $N$ questions, each question with a possible $k_n$ discrete responses. This produces a table ...
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1answer
21 views

Imputing binary variable when no 0s, only 1s are available

I'm trying to impute missing values for a binary variable (values 0 and 1) with some challenging data (of about 1 million observations). The data can be divided into two groups: in group 1, we know ...
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1answer
183 views

Predictive Mean Matching as Single Imputation?

Multiple imputation is known to be advantageous compared to single imputation. However, in practice there are often non-statistical reasons why multiple imputation can not be used (e.g. the data ...
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2answers
221 views

data imputation of missing values in non-normally distributed explanatory variables

I have been told that mean imputation of missing values is inappropriate when the variables underlying distribution is non-normal. my variable is contiunous (but bound at 100) and most observations ...
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While performing label encoding or imputation, what should i do to the column with mostly 0's as values which is irrelevant to what column is about?

My DataFrame consists of 2919 rows. Now ,For example I have this column "2ndFlrSF" 2ndFlrSF: Second floor's Area in square feet and these are the values in it after i run my Pandas command ...
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Imputation in irregularly spaced time series data

I have irregular time series data containing missing values (called A). What I need is a regular time series with imputed values (called B). A spans roughly 3 years. Some days have multiple ...
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53 views

Imputing missing values in time series with Arima

I am using Arima and Kalman smoothing to impute missing values in univariate time series (similar way to this post: https://stats.stackexchange.com/questions/104565/how-to-use-auto-arima-to-impute-...
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Handling missing data for participants who have not completed any standardised measures and have only provided demographic answers

When managing missing data, how many questions should participant have completed, at a minimum, before imputing the remainder of their missing data? For example, a number of my participants only ...
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1answer
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Imputation to Result in Known Total

I am using R and Amelia to impute missing data for the number of homeless children in several locations. There is information ...
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101 views

Extracting Factor Scores of Latent Variables after CFA in AMOS

I plan to extract the latent variables' factor scores after conducting Confirmatory Factor Analysis (CFA). I will use these factor scores as explanatory variables for my next statistical procedure - ...
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1answer
42 views

Predicting the probabilities of sales opportunities

I want to predict the probabilities of sales opportunities using a binary classification algorithmn. However after using logistic regression my results do not seem realistic. This could be due to ...
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1answer
88 views

Impute binary outcome variable for GLM using Stan in R

My outcome variable is a series of Bernoulli trials where some values are missing y $\in$ {0, 1, NA} How do you impute NA values for an outcome variable in rstan in the context of a GLM, assuming ...
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1answer
100 views

Imputing missing outcome data

I saw the other link (Multiple imputation for outcome variables) discussing missing outcome data imputation for complete case analysis. However, I have missing outcome data as well as missing ...
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Is it possible to preserve a imputation solution after adding a new variable with missing data?

I have imputed a dataset with missing values using MICE in R--all analyses have been completed. After the fact, I wanted to keep everything unchanged (so as to not repeat the analyses) but still add a ...
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Is there a way to estimate regression coefficients?

I'm currently working on a simulation study (based on empirical data) and for this simulation I created a model with multiple interaction terms. The interaction terms are between categorical variables,...
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68 views

Space-Time Principal Component Analysis with Missing Lat/Long Data

Thank you for your help, I am looking to run a space-time Principal Component Analysis on Shotspotter data from Brockton, MA: http://justicetechlab.org/data/. Shotspotter sensors record the timing, ...
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158 views

Overcome NA's in Random Forest and SVM?

I am using clinical data for prediction purposes with SVM and RF. Two of my columns are as following: ...
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40 views

Theoretical question about missing data in time series for prediction

I work with an ARIMA model with external regressors on a data set with data of two years at daily level. A central and important variable (let's call this variable "X") I use as an external regressor (...
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1answer
93 views

Forecast (impute) missing discrete values in multiple time series

I'm looking to forecast (impute) missing discrete values in multiple time series in order to reach a target volume in a consolidated time serie. The context: I have salesmen that are selling ...
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1answer
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Drop data Vs fill data. Which one least hurts the integrity of the data?

I have a dilemma for an analysis I'm currently on. I doing some GARCH modelling of bitcoin and a fiat currency. There are some null values with the fiat datasets in comparison with bitcoin data as ...
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How does imputation work? I'm struggling to understand it

I have a short question. I am implementing Scikit-Learn in Typescript and currently blocked at understanding & implementing imputer (mean and regression strategies). Based on the example given ...