• Corpus ID: 239768639

Imputation of Missing Data Using Linear Gaussian Cluster-Weighted Modeling

@inproceedings{MasmelaCaita2021ImputationOM,
  title={Imputation of Missing Data Using Linear Gaussian Cluster-Weighted Modeling},
  author={Luis Alejandro Masmela-Caita and Thais P. Galletti and Marcos Oliveira Prates},
  year={2021}
}
Missing data theory deals with the statistical methods in the occurrence of missing data. Missing data occurs when some values are not stored or observed for variables of interest. However, most of the statistical theory assumes that data is fully observed. An alternative to deal with incomplete databases is to fill in the spaces corresponding to the missing information based on some criteria, this technique is called imputation. We introduce a new imputation methodology for databases with… 

References

SHOWING 1-10 OF 41 REFERENCES
Imputation through finite Gaussian mixture models
TLDR
The use of finite mixture of multivariate Gaussian distributions for handling missing data and the main reason is that it allows to control the trade-off between parsimony and flexibility.
Multiple Imputation of Missing or Faulty Values Under Linear Constraints
Many statistical agencies, survey organizations, and research centers collect data that suffer from item nonresponse and erroneous or inconsistent values. These data may be required to satisfy linear
Advances in Analysis of Mean and Covariance Structure when Data are Incomplete
TLDR
A review is presented of the methodological advances in fitting data to SEM and, more generally, to mean and covariance structure models when there is missing data, indicating a possible distinction for determining missing data mechanisms.
Bayesian Simultaneous Edit and Imputation for Multivariate Categorical Data
TLDR
A Bayesian hierarchical model is used that couples a stochastic model for the measurement error process with a Dirichlet process mixture of multinomial distributions for the underlying, error-free values and is restricted to have support only on the set of theoretically possible combinations.
Flexible Imputation of Missing Data
TLDR
The problem of missing data concepts of MCAR, MAR and MNAR simple solutions that do not (always) work multiple imputation in a nutshell and some dangers, some do's and some don'ts are covered.
Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys
In many surveys, the data comprise a large number of categorical variables that suffer from item nonresponse. Standard methods for multiple imputation, like log-linear models or sequential regression
Extreme learning machine for missing data using multiple imputations
TLDR
A novel methodology based on Gaussian Mixture Model and Extreme Learning Machine is developed to provide reliable estimates for the regression function (approximation), and final estimation is improved over the mean imputation performed only once to complete the data.
INFERENCE AND MISSING DATA
Two results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the
Mixture regression for observational data, with application to functional regression models
In a regression analysis, suppose we suspect that there are several heterogeneous groups in the population that a sample represents. Mixture regression models have been applied to address such
Missing-Data Adjustments in Large Surveys
Useful properties of a general-purpose imputation method for numerical data are suggested and discussed in the context of several large government surveys. Imputation based on predictive mean
...
1
2
3
4
5
...