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Showing 3 results for Missing Data
Sayedeh Fatemeh Miri, Ehsan Bahrami Samani,
Volume 6, Issue 1 (8-2012)
Abstract
In this paper a general model is proposed for the joint distribution of nominal, ordinal and continuous variables with and without missing data. Closed forms are presented for likelihood functions of general location models. Also the Joe approximation is used for the parameters of general location models with mixed continuous, ordinal and nominal data with non-ignorable missing responses. To explain the ability of proposed models some simulation studies are performed and some real data are analyzed from a foreign language achievement study.
Ehsan Bahrami Samani, Nafeseh Khojasteh Bakht,
Volume 14, Issue 1 (8-2020)
Abstract
In this paper, the analysis of count response with many zeros, named as zero-inflated data, is considered. Assumes that responses follow a zero-inflated power series distribution. Because of there is missing of the type of random in the covariate, some of the data application, various methods for estimating of parameters by using the score function with and without missing data for the proposed regression model are presented. On the other hand, known or unknown selection probability in the missing covariates results in presenting a semi-parametric method for estimating of parameters in the zero-inflated power series regression model. To illustrate the proposed method, simulation studies and a real example are applied. Finally, the performance of the semi-parametric method is compared with maximum likelihood, complete-case and inverse probability weighted method.
Mehrdad Ghaderi, Zahra Rezaei Ghahroodi, Mina Gandomi,
Volume 19, Issue 1 (9-2025)
Abstract
Researchers often face the problem of how to address missing data. Multiple imputation by chained equations is one of the most common methods for imputation. In theory, any imputation model can be used to predict the missing values. However, if the predictive models are incorrect, it can lead to biased estimates and invalid inferences. One of the latest solutions for dealing with missing data is machine learning methods and the SuperMICE method. In this paper, We present a set of simulations indicating that this approach produces final parameter estimates with lower bias and better coverage than other commonly used imputation methods. Also, implementing some machine learning methods and an ensemble algorithm, SuperMICE, on the data of the Industrial establishment survey is discussed, in which the imputation of different variables in the data co-occurs. Also, the evaluation of various methods is discussed, and the method that has better performance than the other methods is introduced.
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