Tagged Questions

11 views

Mean imputation for control variable with small number missing values

I have a dataset of survey data where ~4% of responses for one of my demographic control variables (AGE) are missing. For the dependent and independent variables that I am interested in, the number of ...
14 views

Imputing missing values in a count time series with variable effort with the goal of trend estimation

I have a time series monitoring data set that looks like below: The response is a count. ...
21 views

Missing value treatment

I have a data set with 18% of AGE variable missing which is an important variable for analysis. Should I try regression imputation or should I drop those observations? Does even regression ...
66 views

Missing data at random

How does one tell if a dataset is missing data at random? I've been reading up on how to impute missing values, and was wondering what techniques can be used to tell if data is really missing at ...
23 views

Missing values for different dependent variables

I did an experiment across four weeks to collect data on different dependent variables to answer diverse sub-questions. Since on each dependent variable, different participants did not show up and ...
55 views

Imputation in normalized signals

I'm currently analyzing a variety of signals. The problem I have is that I have several "missing" values. These "missing" values represent the absence of signal, they are not errors in sampling or ...
38 views

Handling Missing Values During Test Phase

I was searching for methods for handling missing values in case of Regression task. There are already few threads but I couldn't find what I was looking for. Suppose I have 4 independent categorical ...
29 views

Fitting missing points to a dataset

I've been assigned to fit some missing values into a large dataset, and I've come across a problem. I've finally got a function describing my data (for simplicity say y=2x^2). Now, for every value of ...
154 views

Imputing a missing variable based on common variables with another data set

I have 2 data sets: $A$ and $B$. The variables are common to both data sets with the exception of two, which are both missing in A. Let's call those two additional variables: $b_1$ and $b_2$. We ...
235 views

What is the advantage of imputation over building multiple models in regression?

I wonder if someone could provide some insight into if an why imputation for missing data is better than simply building different models for cases with missing data. Especially in the case of ...
565 views

Imputation with Random Forests

I have two questions on using random forest (specifically randomForest in R) for missing value imputation (in the predictor space). 1) How does the imputation algorithm work - specifically how and ...
130 views

Multiple imputations via MICE package: is there an upper limit for number of imputations?

When performing mulitple imputations with the MICE package in R, I found some resources (http://www.statisticalhorizons.com/more-imputations) that recommend around 10 imputations. I was wondering if ...
192 views

Hot deck imputation: validity of double imputation and selection of deck variables for a regression

Background: I had a data set containing 212 observations with a lots of missing values. Most of the IVs and DVs are categorical (DVs are ordinal) in nature. There are 3 DVs and about 30 IVs. My ...
153 views

Confusion related to calculation of conditional distribution

I have this confusion related to the calculation of a conditional distribution suppose $y_n = N(0,w)$ $p(o_n|y_n) = N(D.y_n,\phi)$ How do I calculate $p(y_n|o_n)$ Actually I was reading this ...
508 views

Should I use missing value using imputation or listwise or pairwise deletion methods?

I have 60,000 data and around 45% of them is missing and the missing values are random. Can I simply use listwise or pairwise deletion or do I have to use imputation? If imputation is recommended ...
94 views

Strategies for Recovering Missing Data

I'm working on the following missing data problem to learn more about stats, probability, and machine learning, but I'm not really making progress solving it: I have a group of unordered, non-unique ...
380 views

Adjustment for missing values of the categorical variables in a data set

I Have a data set containing about 40 categorical variables. I am trying to factor analyze them. But each categorical variable contains a good number of missing values. Some of them are simply because ...
382 views

How to impute an ordinal variable with MICE but prevent it from taking one value?

I have an ordinal variable, overall_tumor_grade, that can take on values of 1, 2, 3, or X if the measurement is indeterminable. There are some NAs that I want to impute using the mice package in R, ...
112 views

Why does a model perform worse after reintroducing observations with missing data imputed?

My dataset contains about 12% missing data, and much of the missing data is grouped along observations (not randomly scattered or along columns). I optimized a regression method after removing the ...
261 views

How to impute data without missing at random?

Recently I got a global longitudinal data from several countries, and each county has one outcome variable and two predictors from 1995 to 2008. I found one of the predictors is always missing in each ...
177 views

MCAR assumption is plausible should I do MI?

My data consists of measurements on patients with cancer and the variables are some indicators regarding the cancer as well as the stage and the grade of the cancer and some personal info about the ...
598 views

Missing rates and multiple imputation

Is there a limit which is the least acceptable when using multiple imputation (MI)? For example can I use MI if the missing values in a variable are the 20% of the cases while and other variables ...
235 views

Dealing with R type of variables when doing multiple imputation with the mi package

Volume 45 of the Journal of Statistical Software contains articles about packages that deal with imputation of missing data, one of which is the mi package. In the PDF article regarding that package ...
162 views

Imputing/instrumenting for missing variables in a case-control study

I'm combining two surveys in a case-control design. Survey B is drawn from the "case" population, and includes all the variables I need for analysis, plus some extras. Survey A samples a general ...
371 views

R function to use for multiple imputation and determining if data is MAR or MCAR

Can anyone tell me which R function to use for multiple imputation? Also, what should I do to determine if the missing data are MAR or MCAR or not?
2k views

Multiple imputation on single subscale item or subscale scores?

Recently I am conducting a research on the relationship between motivation/attitude variables (Gardner's model) and English language proficiency in the Philippines. I encountered a problem: missing ...
220 views

Best imputation method for stochastic noisy data?

What is the best imputation method for a dataset consisting of stochastic data? For example, let's say you have a table of security returns. In some cases the missings are random, in other cases are ...