In this rubric I attempt to give an overview on the research for handling missing values. If you are interested, I recommend visiting the website Rmisstastic, which provides references to many different approaches for handling missing data in a variety of research areas and applications.

*### Explicit Single Imputation

### Likelihood Based Inference with Incomplete Data (Nonignorable)

### Multiple Imputation by Chained Equations

### Augmented Inverse Probability Weighting

### Implicit Single Imputation

### Selection Models

### Pattern Mixture Models

### Bayesian Iterative Simulation Methods

Explicit Single imputation denotes a method based on an explicit model which replaces a missing datum with a single value. In this method the sample size is retrieved. However, the imputed values are assumed to be the real values that would have been observed when the data would have been complete

Specific methods are required to make inference under nonignorable nonresponse assumptions, that is when the value of the variable that is missing is related to some values which are not observed by the analyst (e.g. the missing values themselves)

Multiple Imputation by Chained Equations (MICE) allows most models to be fit to a dataset with missing values on the independent and/or dependent variables, and provides rigorous standard errors for the fitted parameters. The basic idea is to treat each variable with missing values as the dependent variable in a regression, with some or all of the remaining variables as its predictors

Augmented Inverse Probability Weighting (AIPW) is a IPW technique that derives estimators using a combination of the propensity score and the regression model. This approach has the attractive doubly robust property that estimators are consistent as long as either the propensity score or the outcome regression model is correctly specified

Implicit Single imputation denotes a method not based on an explicit model which replaces a missing datum with a single value. In this method the sample size is retrieved. However, the imputed values are assumed to be the real values that would have been observed when the data would have been complete

Selection Models (SM) are typically used to handle nonignorable missingness. They factorise the joint likelihood of measurement process and missingness process into a marginal density of the measurement process and the density of the missingness process conditional on the outcomes, which describes the missing data selection based on the complete data.

Pattern Mixture Models (PMM) are typically used to handle nonignorable missingness. They factorise the joint likelihood of measurement process and missingness process into a marginal density of the missingness process and the density of the measurement process conditional on the missing data patterns, where the model of interest is fitted for each pattern.

The most popular class of Bayesian iterative methods is called Markov chain Monte Carlo (MCMC), which comprises different algorithms for sampling from a probability distribution. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution