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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26 views

Error in imputing data in R [closed]

**i am imputing my data (all items) using VIMGUI package in R. But in the output section i am getting errors, which i am not able to understand, where the problem lies. The function which i run in R ...
1
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0answers
17 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 ...
1
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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 ...
1
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0answers
13 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 ...
0
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3answers
67 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 ...
0
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0answers
27 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 ...
2
votes
2answers
106 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 ...
0
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1answer
37 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 ...
1
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2answers
110 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 ...
0
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0answers
19 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) ...
0
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0answers
61 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 ...
0
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1answer
57 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 ...
1
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0answers
36 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. ...
0
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1answer
71 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 > ...
1
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0answers
36 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 ...
0
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0answers
24 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: ...
2
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0answers
16 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 ...
0
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0answers
66 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: ...
1
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0answers
35 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 ...
0
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0answers
23 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 ...
2
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0answers
74 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
133 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 ...
1
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0answers
31 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 ...
0
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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
417 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 ...
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0answers
30 views

Impute missing data of a variable

I'm currently working on spatial spillovers in agriculture at the municpal level with cross-section data. But, I do have missing values for the investment in capital at the municipalities though the ...
2
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0answers
50 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 ...
8
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1answer
386 views

Why is this multiple imputation low quality?

Consider the following R code: ...
0
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1answer
87 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 ...
0
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0answers
84 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
83 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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0answers
27 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
25 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 ...
1
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1answer
111 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
votes
2answers
39 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
34 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
90 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
92 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
55 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
62 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 ...
1
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1answer
388 views

R regression with categorical response variable

I have four variables, two are categorical and two are numeric: ...
0
votes
1answer
100 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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2answers
155 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 ...
0
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0answers
20 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
73 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 ...
1
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0answers
261 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 ...
1
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0answers
31 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
46 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 ...
3
votes
1answer
232 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 ...
0
votes
1answer
131 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 ...