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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11 views

Displaying data characteristics after multiple imputation

I have original data which I run a few commands on to get a feel for the data. For example, I have men and women, and in each group, I have the percent in each cancer type (eg brain, lung). In the ...
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3answers
90 views

How do we decide on how to fill missing values in data?

I have a data set with NA values in many predictor variables. How do we impute the best values ? I have 302 variables in total. Out of them 236 belong to some abstract category, 37 to other, 9 to ...
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1answer
29 views

Impute with the Mean or Median? Instrumental Variables

I am using instrumental variables and I have missing data. In r, I don't believe you can use the MICE package with the AER package. Therefore, I am going to impute with either the mean or median ...
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1answer
29 views

Imputation and linear regression analysis paradox

Missing values, especially in small datasets, can introduce biases into your model. There are several data imputation methods (MICE, Amelia II), which use EM algorithms to "fill in" the missing ...
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0answers
15 views

Advice on imputation of multiple time series

Background In the first year of the study 60 streams had temperature data loggers installed (temperature measured every 30 seconds). The second year only 30 of these same streams had data loggers. ...
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0answers
9 views

Gap-filling biophysical sensor time series

I am exploring imputation methods for filling gaps in time series from multiple co-located biophysical sensors. At a given site, we have about 25 sensors measuring things like temperature, humidity, ...
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1answer
22 views

How to handle data with 2 variables that have same missingness pattern?

I've not had much academic coursework on imputation, and I can't find anything online or in any texts regarding how one could handle missing data where there are two (or more, possibly?) variables ...
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1answer
25 views

Regression analysis with non-integer event rates

I am working as part of a team on a large dataset which has been subject to imputation analysis. One of my colleagues has pointed out that the when carrying out regressions that can provide odds ...
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0answers
53 views

Imputation in R: How to impute univariate data in R?

I am trying classification(2 classes) using Random Forest. Classes are - Red, Green. My dataset contains 1 numeric attributes(called X), and 51 binary attributes to classify a document into red and ...
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16 views

Is there an online method to perform multiple imputation?

I have a dataset with a lot of missing data and I am using multiple imputations (with Amelia in R) before performing analysis on it. This dataset is used to train a classification model and to ...
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0answers
18 views

Bootstrapping and classification tables after multiple imputation

I have used the mice code to do my multiple imputation and it gave me gave me an output for my model as well as a new appended dataset using the "long" code. However, I tried to use this new bigger ...
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17 views

Tests for imputed time-series dataset

I am currently dealing with serial measurements, changes of some parameter in patients over time. Values are mostly annual, distance between samples for each person is about a year. I want to perform ...
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1answer
58 views

Independent variable has a known non-causal relationship with the dependent variable; is it still okay to regress?

To further elaborate on my question, assume that I have a time series dataset of Tax X and Tax Y, where in Tax X is paid by 100% of the sample while Tax Y is paid by 75%. Both taxes differ with ...
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29 views

Poisson Regression - missing data, imputation, distribution of fitted values

I'm working on a school project concerning Poisson regresion. I'm trying to build a model for number of cars in household base on American Community Survey. Among explanatory variables are value of ...
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0answers
13 views

What is the computational complexity of mean imputation?

I'm trying to figure out what the complexity of mean imputation is (in $\mathcal{O}$ notation). Assuming a data set with $m$ observations and $n$ features, I would say the complexity is ...
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0answers
14 views

Estimating the opinion of a user by looking at opinions of other users

First of all, a bit of background: i am not a statistics expert but i am an enthusiast about data analysis. I have this list of "items" and for each item i have a list of "users" and the vote that ...
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3answers
92 views

Missing data and imputation in general

Handling missing data is a bit confusing for me. My questions are: Is it better to calculate imputations than simply leave out NAs and leave it to the (appropriate) model to handle it? Is there a ...
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1answer
105 views

R caret and NAs

I very much prefer caret for its parameter tuning ability and uniform interface, but I have observed that it always requires complete datasets (i. e. without NAs) even if the applied "naked" model ...
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2answers
189 views

Which is better, replacement by mean and replacement by median?

I'm doing a project that involves replacing missing values in a set of data (first time doing this). This is involves using to methods "replacement by mean" and "replacement by median" to fill in the ...
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1answer
42 views

Imputation Procedures

I don't completely understand how to impute in the following situation. Consider the following example. In this example we have a dataframe of students. For each student we have an IQ score and a ...
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2answers
186 views

Using Kalman filters to impute Missing Values in Time Series

I am interested in how Kalman Filters can be used to impute missing values in Time Series Data. Is it also applicable if some consecutive time points are missing? I cannot find much on this topic. Any ...
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0answers
23 views

How does missing data (not at random) affect Bayesian models?

When I was a student learning about Bayesian models, we were taught that missing data was not a problem because they would be imputed. However I am wondering about how missing not at random (MNAR) ...
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0answers
82 views

Multiple imputation of conditional variables

I need to impute the missing values of a dataset of medical data in which several variables only make sense if another variable has a specific value. In the questionnaire the data come from they were ...
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1answer
92 views

Imputation of missing data before or after centering and scaling?

I want to impute missing values of a dataset for machine learning (knn imputation). Is it better to scale and center the data before the imputation or afterwards? Since the scaling and centering ...
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60 views

Imputing missing gaps in irregular time series

I am currently working with time series data that was sampled at irregular time intervals. There are some gaps of missing data, i.e. a bunch of subsequent observations are missing every now and then. ...
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1answer
143 views

Amelia (error if I include categorical vars)

If I include a categorical variable in amelia (e.g., p3[,4]), I am getting the following error: a.out <- amelia(p3[,-5], noms=p3[,4],m = 10, ts = "time", cs = "Username") Error in if (any(vars > ...
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66 views

Multiple Imputation and Matrix Completion

It is quite common that data sets will contain missing values in them. Suppose we want to try to fill in the missing values. For this we have techniques such as single/multiple imputation and matrix ...
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31 views

univariate imputation

I have a problem imputing a variable tobacco use for a survey dataset with replicate weights. Stata does not have a code for this due to statistical issues related to this matter: ...
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0answers
21 views

Multiple Imputation for Spatial Models

I'm trying to estimate various spatial models (SAR, SDM, SEM) but have missing data throughout my variables. The mice package in R gives a straightforward solution when none of the variables with a ...
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0answers
92 views

R MICE imputation failing

I am really baffled about why my imputation is failing in R's Mice 2.22 package. I am attempting a very simple operation with the following data frame: ...
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0answers
41 views

Taking only a single data set from a multiple imputation?

If I use a package like R's mice to do multiple imputation, then only select the first of the resulting imputations and use that as a single imputation -- ignoring ...
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0answers
30 views

EM algorithm - data imputation

Suppose we have iid sample of random vector $(X,Y,Z)$, where $x_i, y_i, z_i$ denotes corresponding realizations for the components. Assume also, that $(X,Y,Z)$ has a three-dimensional Gaussian ...
4
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1answer
133 views

Missing data not at random - Advice needed on method

I have been developing a logistic regression model based on retrospective data from a national trauma database of head injury in the UK. The key outcome is 30 day mortality (denoted as "Survive" ...
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1answer
226 views

How to handle missing data in a small $n$ large $k$ machine learning scenario?

I have a sample size $N=130$ and $1000$ variables. I am using machine learning techniques (SVM) for analysing the data. Some variables in the dataset have values that are so huge that they must be ...
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32 views

Imputation for small cells?

I have a question about the data requirements for imputation. Specifically, is there a rule of thumb about what proportion of the data have to be non-missing for the imputation to be "valid?" I am ...
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1answer
12 views

Group variables seeing the class

I need to perform a classification model (logistic regression, pnn or neural network). I'm doing the part of data preparation in R. I have a nominal variable, that has more than 800 levels. I need to ...
2
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1answer
622 views

What are the pros and cons of using median imputation to handle missing value?

I have to choose between median or mean imputation to handle missing values. I feel median imputation will work better because it is a number that is already present in the data set and is less ...
2
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0answers
51 views

Imputation of missing values for doing PCA in R [duplicate]

I have a dataset with approximately 4000 rows and 150 columns. I want to predict the values of a single column (= target). The data is on cities (demography, social, economic, ... indicators). A lot ...
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1answer
742 views

Why is this multiple imputation low quality?

Consider the following R code: ...
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2answers
127 views

Measuring longitudinal data where individuals have missing observations

We have a longitudinal panel of X users with their online spending patterns and are trying to measure certain metrics within the panel. We have time series information about the users such as their ...
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0answers
107 views

R- yaimpute - standarize value before KNN for imputation

I need to impute data before running a logistic regression. I'm trying the package yaimpute but I realized that it doesn't standarize before imputing. I created a sample matrix to view so: ...
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2answers
85 views

Semantics rules? A classification challenge

Suppose we make interviews on a large number of households in which we ask, among other things, the sex and age of the individuals living in the household, and also who among these individuals is the ...
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35 views

Missing data and evaluating parametric assumptions when using MPlus

I am conducting in my study a paired samples t-test and a path analysis using MPlus. I have read that MPlus uses FIML, which is a strong method for dealing with missing data, and because of it I don't ...
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0answers
28 views

Are pooled results from multiple imputation equivalent to a posterior mean?

I am fairly new to multiple imputation and trying to be sure I understand the approach. Say I have a data set with missing values, so I create 5 imputed data sets using multiple imputation by ...
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1answer
150 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
51 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 ...
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1answer
36 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
108 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
96 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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60 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 ...