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Multiple imputation refers to a set of stochastic imputation routines aimed at preserving the multivariate features of the data

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Beta regression and LASSO on multiply imputed data

As the title implies, I want to perform Beta regression with LASSO on a multiply imputed dataset. It seems to me that the general procedure for doing this should be a generalization of the group lasso ...
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Multiple Imputation of Mutually Dependent Data

I'm trying to construct summary variables for proportion of time spent in different employment statuses over an individual's working life (e.g. % of time spent unemployed between ages 18-21). My ...
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How to test multiple regression assumptions when multiple imputation has been used?

I used multiple imputation on SPSS to deal with missing data in my study. I then carried out multiple regression from the imputed and original data-sets, using a split-file. I now have output for each ...
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22 views

Intermittently missing assessments in survival analysis- Can I use Multiple imputation?

I'm analysing a longitudinal data with intermittently missing data. The problem is to find the time to clearance from carriage of a bacteria sp. But because not all children were sampled every month ...
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63 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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67 views

What should I do after multiple imputation in the data?

I have a data set with missing observations. I used VIM package in R for imputation. After imputation, I will try to run a ...
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16 views

Multiple Imputation of time series data

I have many groups with a different number of members (learners). The members of each group came together in different time intervals whereupon not all members took part in each of their group ...
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15 views

Single imputation to multiple imputation: Can this be done properly with 'missForest' package?

I am currently working on devising a proper multiple imputation scheme utilizing the missForest package in R (see https://academic.oup.com/bioinformatics/article/28/...
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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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11 views

How to perform linear regression accounting for covariate?

I want to perform linear regression between 2 variables (A, B), but i want to account for some other variables (C, D). If i perform linear regression on SPSS and include A as a dependent variable and ...
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1answer
25 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
31 views

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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Multiple Imputation: Pooling the variance and covariance of regression coefficients

After conducing linear regression models in SPSS with multiply imputed data, the pooled output does not include the covariances for regression coefficients. To probe the regression interactions by ...
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Mulitple imputation using external data

I am wondering if multiple imputation could be used to cope with imprecise rather than missing data. That is to say: I observe the outcome Y with no missing value; I have an exposure X, which is known ...
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40 views

Multiple imputations in different samples- how to combine the imputed datasets?

I would like to do a mixed-effect regression analysis by pooling the datasets of two different studies. The two datasets have equal variables, in the same order. I have already used the mice package ...
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48 views

Why don't people impute missing exposure data in database studies?

Investigators doing studies in large databases (e.g., EMR) in which there is often a lot of missing data usually (in my experience) want to exclude all subjects missing the exposure or outcome of ...
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Multiple Imputation on multi-site data

Suppose I would like to assess the relationship between Y and X (i.e. Y|X) on data collected from several different sites (i.e. 5) with one covariate Z and multiple auxiliary covariates which may ...
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21 views

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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Choosing Among Multiply Imputed Datasets

I am using multiple imputation to estimate treatment effects in a dataset that contains missing data. In some of my imputed datasets, the algorithm used in the analysis fails to converge; it's not ...
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28 views

Flag outliers first or conduct multiple imputation first?

I am working with a data set in which the dependent variable, Y, is constructed from three variables (y1, y2, y3) that each have missing data. To address this issue with multiple imputation, I've used ...
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When to use Multiple Imputation over Maximum Likelihood for Missing Data and vice-versa?

I've seen these being called the best techniques for dealing with missing data. But I'm wondering when to use one over the other and why? Edit: Why is this getting downvotes? I'm legitimately ...
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18 views

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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23 views

Per protocol or Imputation when missing is small (<5%)

if ~2% of my data is missing on the outcome (continuous scale), out of a total of 200, two in control and three in intervention group, do I need to impute? Or can I make a case that with such small ...
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93 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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Multiple imputation with composite variables

In my analyses, I often use urinary concentrations as measure of exposure to various compounds. As these are generally spot urines, they are 'adjusted' for dilution using urinary creatinine ...
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22 views

multilevel multiple imputation

I have been using the mitml package in R for multilevel multiple imputation of longitudinal data, there are 2 functions available for imputation, panImpute and jomoImpute , both use the joint modeling ...
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Procedure for identifying predictors via LASSO on imputed data

First time poster here, hello Everyone! I've tried to make my question as concise as possible. I am looking to identify the best set of predictors for rehospitalisation in my data set of 60 ...
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34 views

Multiple Imputation of Multilevel data

I am using Mice package in R for multiple imputation of a multilevel data where repeated measures are nested within individuals. But there is a bug in mice for which we need to convert the group ...
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Multiple Imputation in R; for one variable only certain date ranges

So I have a problem and I'm not sure there's a way to do this. I have 13 survey questions, and there are responses from 2008 to 2016. However, 3 of the items were only asked starting in 2009. Only ...
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82 views

Multiple imputation with a Cox model [closed]

I'm doing a study on 52 patients with breast cancers and looking for predictive factors of death. I have 17 variables to test for predictive value with some with 20% of missing data (some categorical ...
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59 views

Combining Gradient boosted trees after multiple imputation

Currently I am working with a gradient boosted tree model fit onto a multiple imputed dataset. For those who don't know multiple imputation: It predicts missing values and imputes that value with ...
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18 views

Combining multiple imputation with penalization

I have used Frank Harrell's excellent rms package both for penalization (pentrace and setting ...
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1answer
28 views

Hypothesis testing with randomised algorithm

Imagine I have some data D, and with a randomised algorithm I construct a parameter $\theta$. I know the distribution of $\theta$ under the null hypothesis of the data. For example, the original data ...
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20 views

Multiple imputation: How to obtain a consolidated data set from the m imputed data set

Sometimes it is necessary to compare results in contingency tables of the original data (without missings) with a single final dataset obtained from the m imputed datasets, but taking into account the ...
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validity of tobit estimates after multiple imputation

I want to estimate tobit marginal effects using multiply imputed data, however I see that tobit is not among the estimation commands supported by Stata's MI prefix - I understand that the validity of ...
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35 views

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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94 views

Generating frequency table and survival curve after multiple imputation

I'm using the MICE package to generate 10 imputed datasets. After that, I know I should perform analysis on each dataset (propensity score matching, Chi-square, and Cox-regression in my case) and ...
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What is the risk of not including all of my model predictors in an imputation process?

I have a model that I want to run but a lot of my predictors have missing values. So I ran an imputation process using mice package in R - it took me 22.5 hours! ...
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How to deal with undetectable outcome values? (data missing not at random)

I conducted a sound propagation experiment in which recorded maned wolves calls were broadcasted at different sites(x3), hours(x6: 17h,18h,23h,05h,06h,11h), and with different speaker position (x2: ...
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74 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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Error Detection and missing data imputation in Wireless Sensor Networks

I'm working on wireless sensor networks and I wish to be capable of detecting if there are any outliers in the sensed data as well as imputing missing ones. I read a lot of articles which made me more ...
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Is it valid to use Random Forest imputation in blocks and combine results into a final dataset?

I have an extremely large dataset (26 variables and 105,556 observations) with missing values and I would like to use Random Forest imputation to impute some missing values. Since the dataset is so ...
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Larger standard error after multiple imputations

Missing data experts, I am working on a presentation demonstrating the benefits of using multiple imputations to handle data missingness. To demonstrate its benefits, I simulate two data sets; one ...
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338 views

MCAR test for large number variables and small sample size

I have a dataset with 101 observations and 402 columns (those columns comprise several multiple-item questionnaires). Among those 402 columns, 10 of them are categorical and the remaining are ...
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96 views

Likelihood ratio test for multiply imputed datasets?

I have a set of generalized linear models fit to 5 multiply imputed datasets. I am interested in testing the statistical significance of a set of predictors, coded from dummy variables. Rubin's ...
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When using multiple imputation, is it more informative to report descriptive statistics for the imputed dataset, the original dataset or both?

My understanding is that multiple imputation is a method for dealing with bias/lack of power that results from missing data. However, it is not a method for replacing individual values. As a result, I ...
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171 views

Rubin's formula for variance in varying domains

I am using multiple imputation on a binary variable (employment status). I have to estimate the number of employed units in several different domains, and the respective variances using Rubin's rule: ...
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79 views

Binary classification with multiple imputations

I work on a binary classification problem with proteomic data, where the goal is to select the best subset of proteins which contribute to better classification (AUC). However, the problem is that ...
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68 views

Restricting a sample on a multiply imputed variable

Apologies if this is more of a statistical question. I am currently dealing with a multiple imputation problem that I am attempting to address in Stata. After the imputation stage, I would like to ...
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Evaluating a Fractional Logit model after multiple imputation

In the model I'm estimating the dependent variable falls between 0 and 1 so I'm using a fractional logit model. However, because of issues with the data I had to multiply impute it, and use ...