33
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What are the disadvantages of using mean for missing values?
Example with normal data. Suppose the real data are a random sample of size $n=200$ from $\mathsf{Norm}(\mu=100, \sigma=15),$ but you don't know $\mu$ or $\sigma$ and seek to estimate them. In the ...
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20
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How to fill in missing data in time series?
The answer will depend on your study design (e.g., cross-sectional time series? cohort time series, serial cohorts time series?). Honaker and King have developed an approach that is useful for cross-...
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16
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Hot deck imputation, ''it preserves the distribution of the item values'', how can that be?
Hot-deck imputation of missing values is one of the simplest single-imputation methods.
The method - which is intuitively obvious - is that a case with missing value receives valid value from a case ...
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15
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Imputation of missing values for PCA
A recent paper which reviews approaches for dealing with missing values in PCA analyses is "Principal component analysis with missing values: a comparative survey of methods" by Dray & ...
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13
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Imputation by regression in R
Even though this thread is a bit old, I am sure some people are still trying to find a solution in this thread. Therefore I want to add an example how you could use the mice package for regression ...
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12
votes
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Can I replace NAs based on response variable?
In short, you should look at multiple imputation (==replacement) techniques, first put forward by Rubin in 1987.
In more detail: replacing by a single value assumes certainty about this replaced ...
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12
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Methods to work around the problem of missing data in machine learning
The technique you describe is called imputation by sequential regressions or multiple imputation by chained equations. The technique was pioneered by Raghunathan (2001) and implemented in a well ...
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11
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Can I delete missing data?
If you decide to "delete" the missing data prior to analysis, that is called a "complete-case analysis" (i.e., you are only using data points that have complete information). That ...
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10
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Can I delete missing data?
It is important to think about the mechanism leading to missing data. There are three kind of missing data that can happen:
Missing completely at random (MCAR). It means that the probability that an ...
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10
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Choosing $m$ value when using multiple imputation (MI)
I believe our current best practice is to use the two-step procedure described in von Hippel (2020) and his Statistical Horizons article, which is to estimate the fraction of missing information (FMI),...
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9
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A data set with missing values in multiple variables
@Tim gave a nice response. To add to that, the best thinking about dealing with missing values (MVs) began with Donald Rubin and Roderick Little in their book Statistical Analysis with Missing Data, ...
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9
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K-Nearest Neighbor imputation explanation
The $k$ nearest neighbors algorithm can be used for imputing missing data by finding the $k$ closest neighbors to the observation with missing data and then imputing them based on the the non-missing ...
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9
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What are the disadvantages of using mean for missing values?
Using the mean for missing values is not ALWAYS a bad thing. In econometrics, this is a recommended course of action in some cases provided you understand what the consequences may be and in what ...
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8
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Imputation of missing data before or after centering and scaling?
It really depends on the Imputation technique being used.
For example if we Impute using distance based measure (eg. KNN), then it is recommended to first standardize the data and then Impute. That is ...
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Imputation methods for time series data
Your approach sounds very theoretical.
Did you analyze the imputations of the packages you mentioned?
Often imputation packages have requirements (e.g. MCAR data), but will still do a reasonable ...
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8
votes
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Impute binary outcome variable for GLM using Stan in R
Each value of y_miss can either be 0 or 1, so you need to marginalize over them with a statement such as
...
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8
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How to decide whether missing values are MAR, MCAR, or MNAR
First let's understand each part:
MCAR
Missing completely at random - Whether or not an observation is missing IS NOT determined by the value of that observation (i.e. a missing value in an income ...
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Justification for imputation with over 50% missing data
A mixed effects model also implicitly imputed, so if you're fine with that, why would you not be fine with making the imputation more explicitly clear?
The main question is what you intend to impute. ...
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7
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How to fill in missing data in time series?
you can use imputeTS package in R . I believe the data you are working on is uni-variate time series.The imputeTS package specializes on (univariate) time series imputation. It offers several ...
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How to use restricted cubic splines with the R mice imputation package
You are right that the imputation model needs to be as rich or richer than the outcome model. The fact that imputation based on full maximum likelihood estimation and imputation done by ...
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7
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missForest Data imputation vs. MICE using RF as imputation method?
I think one of the differences is that missForest is, at least in its original form, a method for single imputation, i.e. imputing a single best imputation. It ...
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What are the disadvantages of using mean for missing values?
Another possible disadvantage with using the mean for missing values is that the reason the values are missing in the first place could be dependent on the missing values themselves. (This is called ...
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Choosing $m$ value when using multiple imputation (MI)
While they don't provide a strict criterion in their study, Graham et al., 2007 did a Monte Carlo simulation of different $m$ values and came up with a table of estimates based off that data. Here $\...
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7
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Would it be preferable to use statistical imputation instead of a subject matter expert's subjective estimate for missing data?
I would avoid the term "missing data" here.
You are using number of medications as a proxy for disease complexity. For some patients, you can't directly know the number of medications, but ...
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6
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Should data be normalized before or after imputation of missing data?
In my opinion, since you are using kNN imputation, and kNN is based on distances you should normalize your data prior to imputation kNN. The problem is, the normalization will be affected by NA values ...
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Is multicollinearity problematic for imputation models?
The way I read van Buuren's background on imputation, and this part specifically, is that for multiple imputation models the goal is to use as much information as you have in order to obtain the ...
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Why do I need to run a model on multiple imputed datasets?
The imputed values on your datasets obtained through multiple imputation are predictions from statistical models themselves, and vary according to probabilistic distributions as any predictions from ...
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proof of missing at random
There is no "proof" for MAR. There are statistical tests for the more restrictive "missing completely at random" (MCAR) mechanism but not for MAR (and the utility of the tests is ...
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Best way to impute missing values in a time series
Actually, there will be many intervals when you have no observations... namely, all the intervals between two recorded detections. You could ask the exact same question about a detection-less interval ...
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5
votes
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Missing data - Regression imputation
Your linear regression can't predict on the missing data if it doesn't have a predictor. So your value is not imputed.
Although it does involve regressions, Multivariate Imputation by Chained ...
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