When the data present lack of information (gaps), i.e., are not complete. Hence, it is important to consider this feature when performing a analysis or test.

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1answer
14 views

identify nature of missingness for categorical variables

could you please give me some hints for identifying the nature of missingness for categorical variables' missing value? I mean, I gave a fast search on google scholar but I didn't find anything ...
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0answers
24 views

Handling Sparse Data Frames - algorithm selection

I am new to machine learning/statistical modelling. I am trying to run a classification on a highly sparse dataset with 100 features, most of which are categorical (TRUE/FALSE) with the remaining ...
0
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0answers
13 views

Missing Values in Dependent Variable and the Question on Tobit model (Panel Data)

I would appreciate some help and guidance on this issue. I have a panel data that looks like this. What I want to do is use the FDIflow variable as a dependent ...
0
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1answer
16 views

How do I deal with missing data for a repeated measure collected over time?

I have a real-life data set, with only one "measured" variable (i.e., patient waiting time). This single variable is collected weekly, in different clinics, for different providers, across time. I am ...
2
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0answers
34 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" ...
1
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1answer
44 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 ...
0
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0answers
16 views

Loading data with missing values as numeric data [migrated]

I am trying to impute missing values using the mi package in r and ran into a problem. When I load the data into r, it recognizes the column with missing values as a factor variable. If I convert it ...
-1
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0answers
21 views

How to handle missing values due to skip-logic pattern in SPSS?

I'm about to run analysis for my survey questionnaire in SPSS. But before that, how do I handle missing values due to skip-logic pattern?
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0answers
9 views

Incomplete inhibition curve fitting with Graphpad Prism6

One of my data set shows incomplete inhibition (for high concentrations points, inhibition response goes up to 45%-50% (of control). I am fitting my data using log(inhibitor) vs response -Variable ...
0
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0answers
14 views

Expectation maximisation for right-censored iid data from Normal

This is the data (which are length of ropes), $\textrm{Data}=\{99, 70, o ,89, 88, o, 88,70, o ,o\}$, where $o$ are censored data with value above $100$. Assume that data are from $\textrm{iid} \sim ...
0
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1answer
46 views

Single EM imputation with R (using Amelia or other packages)

I am trying to impute missing values with R. I would like to use the EM algorithm for that. As it seems this algorithm is implemented in the ...
0
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0answers
10 views

Estimating NA values in WinBUGS model

I am trying to fit a WinBUGS model with missing (NA) values as part of the input data. I'd like to use WinBUGS to generate missing estimates for the NA values from the full conditional distributions ...
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0answers
20 views

Imputation for small cells?

I have a question about the data requirements for imputation. I am trying to calculate the price elasticity of demand for service X where there are four possible provider types for that services. In ...
6
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1answer
128 views

Should additional crime reports about someone change our level of doubt about an initial crime report?

Edit: Note that this question is not about multiple unreliable witnesses to the same incident, but rather multiple incidents with only one witness each. Should the accumulation of separate alleged ...
0
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1answer
29 views

Factor analysis with categorical reponses and missing data

I factor analyzing a measure with 55 categorical items (3 categories each). I am use CFA to test a 7 factor model. I have a very large sample (>10,000), but approximately 20% of the sample is missing ...
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0answers
11 views

Conditional Distribution of an Independent Variable for missing data

Let $X=[X_1 X_2 X_3 ... X_p] $be a matrix of p independent variables where $X_i=[x_{i1} ... x_{in}]'$ is a nx1 vector. Let W be a nxn weight matrix based upon queen contiguity (so zero's along the ...
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0answers
30 views

Obtain factor scores in data set with missing values

I would like to obtain factor scores after factor analyzing data that contain missing values. I'm using Stata 13 to run the analysis. Here is the basic code (borrowed from the UCLA site): ...
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0answers
39 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 ...
1
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1answer
32 views

Imputation of a binary variable by Bayesian logistic regression

In the book "Flexible Imputation of Missing Data" by Van Buuren, the following algorithm is presented I think I understand the algorithm as given, but I would like to know what is the "more ...
0
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1answer
40 views

Missing factor levels after logistic regression glm()

I am quite new in the R universe, so please excuse me if the question is too simple.. I would like to perform a logistic regression on a marketing data set (only categorical variables), of the form ...
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0answers
20 views

MAR Vs. MNAR: attribute's missingness determined by the value of another attribute

Suppose I have a training data set that predicts the whether a person is unemployed or not using a decision tree. I have a certain set of attributes that I use to predict this. But for all samples ...
6
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3answers
77 views

Managing 'prefer not to says' in sensitive questionnaires

Consider a questionnaire where we ask someone about their sexuality. The five options, for simplicity, are: Heterosexual Homosexual Bisexual Other 'Prefer not to say' Assume we ask the population. ...
3
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3answers
181 views

How do I handle nonexistent or missing data?

I tried a forecasting method and want to check if my method is correct or not. My study is comparing different kinds of mutual funds. I want to use the GCC index as a benchmark for one of them but ...
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0answers
23 views

(binary) Matrix completion with less known data

Recently, I meet such problems, I call it matrix completion problem. For example, the row denotes the users and the column denotes items. And If one user like the ...
2
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0answers
41 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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0answers
46 views

Identifying multivariate outliers in a large sample with missing data, using SPSS

I'm a psychology PhD student doing analysis on a relatively large set of data, obtained via online surveys. The purpose of the study is largely to determine normative data for a population of adults, ...
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0answers
14 views

How to aggregate daily data, with a large amount of missing data?

I have a conceptual problem in a rather large study of the following design: technical details of a telephone connection are monitored daily, and at the end of the month a questionnaire is filled out ...
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2answers
41 views

Handle missing values in factor variable

I have a huge dataset for a binary classification problem (about 1.5 million rows), and the feature space is quite large (145 dimension). Some of these features are factors (YES, NO), but there is ...
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1answer
83 views

Prediction when survey subsets create dramatically smaller Ns

Suppose you want to predict an outcome using a sample whose N is... 10,000 based on most demographic variables 9,000 based on Survey Question 1 3,000 who answered "Yes" to Question 1 and thus were ...
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0answers
15 views

Determine if missing values are informative(due to treatment) or just noise.

I'm not sure if this is those appropriate way to phrase this question. I have two populations of measurements. In my control population I have a quantitative measure of a proteins abundance. I expect ...
2
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1answer
103 views

Impute missing values using aregImpute

I have a data frame with 61 columns. Some data is missing. I read in Steyerberg's book about aregImpute in Hmisc. I used it with ...
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0answers
26 views

Joint model for ordinal repeated measures data and MNAR dropout using R

I have a dataset consisting of repeated measures data of graded toxicity scores (0-4) in a large number of patients being treated with a anti-cancer drug. We would like to identify predictors for ...
2
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1answer
172 views

Multiple imputation introduces negative values; dataset still valid?

After some detective work in my data sources, I realized the reasons for my previously reported 98% of missing data ratio. After implementing some data collection code fixes, the current missing data ...
0
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2answers
71 views

Error using mice() package in R for handling missing data [closed]

I am doing regression with a data with Y as target variable and 16 feature variables. I had two date feature variables which where as factor. I converted them to date format as shown below: ...
0
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1answer
50 views

NA produced in linear regression model

I have read similar posts to this but my problem is not resolved by the answers given. I want to do a v simple linear regression to see if bite incidence is related to district, zone (vacc or control) ...
0
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1answer
29 views

Missing data on Extreme Value Analysis

I am analyzing (extreme value analysis) the dataset which contain daily rainfall over 100 years of a single location. However there are around 500 missing values on the whole dataset. In this case the ...
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0answers
107 views

Ensuring exploratory study's validity with pseudo-simple random sampling

The context of my questions is as follows. I'm performing a cross-sectional secondary research study, involving open source software (OSS) projects. I collect data (information about the projects) ...
1
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1answer
36 views

Missing data analysis software

Does any standard statistical software like R, SAS or SPSS have procedures or codes to analyze log-linear models for missing data in contingency tables using maximum likelihood estimation (or EM ...
0
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0answers
18 views

Imputing values that depend on each other, such as percentiles or proportions

I have a data set with school level measures including test score percentiles. These percentiles are central to my analysis. For example, one measure is the test score of the 25th and 75th percentile ...
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0answers
23 views

GBM package: Why there is a missing node?

Why there is a missingNode as 3 as there are no missing values? I have the data in the following form: ...
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0answers
31 views

How to handle systematically missing values?

In my situation, one of two sources is not invoked if the confidence reported by the first source is higher than a threshold and hence it is missing in some examples. How can account for such missing ...
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0answers
15 views

Proper way to stack models when some models aren't always applicable?

Suppose you have two (or more) models that you want to ensemble together. However, some of the models are trained specifically on very specialized subsets of your data. If you do the stacking with a ...
3
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1answer
79 views

Missing data which simply cannot exist

I have read 80% of missing data in a single variable and understand the approach for dealing with missing data which simply cannot exist for 1 variable. I am trying to generalise this up to 2 or ...
0
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1answer
101 views

lasso with missing values and categorical variables

I have a dataset with a lot of missing values and mix of continues and categorical variables. I want to use something like group lasso to do features selection. Probably the output is binary 0,1 and ...
1
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1answer
58 views

How to determine whether a set of data are qualitative or quantitative?

Assume a data set with multiple columns, where the categorical data are coded. What is the best rule(s) or rule of thumb to determine whether each column contains qualitative data or quantitative ...
0
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0answers
27 views

how to handle the imbalanced data in regression analysis

The problem here is very similar to the problem asked by someben in 2012 (link:Sampling for Imbalanced Data in Regression). It involves the linear regression analysis using an unbalanced dataset. Say, ...
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0answers
44 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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0answers
35 views

How to handle uninterested missing (unobserved) data?

Suppose I have $N$ items $\{x_n\}_{n=1}^N$ (also denote the feature vector as $x_n$) and I have observed $M$ pairwise preferences $\{x_{i_m} \succ x_{j_m}\}_{m=1}^M$ from a user. Now I want to predict ...
0
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0answers
12 views

rebuild model based on what columns a record has

I've built a model in R using glm, and in the new dataset that I need to predict, there exist some new levels for the columns that are non-numeric. I know there are so many approaches to deal with ...
3
votes
1answer
241 views

Determining probability distribution for datasets with missing values

As a part of my exploratory data analysis (EDA) prior to further analysis, I'm trying to determine a probability distribution of my pilot dataset's variables. A particular feature of this dataset is a ...