## What is MCMC imputation?

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The PROC MI MCMC full data imputation method uses an iterative Markov chain Monte Carlo method to simulate draws from the posterior, p(μ,Σ|Yobs). 2 STEPS IN MCMC. Here provides a detailed description of the MCMC algorithm. We will describe the algorithm in general terms.

## What is Markov chain sampling?

In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain.

**Why do we use Markov chain Monte Carlo?**

Markov Chain Monte Carlo provides an alternate approach to random sampling a high-dimensional probability distribution where the next sample is dependent upon the current sample.

### What is Rubins rule?

Rubin´s Rules (RR) are designed to pool parameter estimates, such as mean differences, regression coefficients, standard errors and to derive confidence intervals and p-values. We illustrate RR with a t-test example in 3 generated multiple imputed datasets in SPSS.

### What is monotone missing pattern?

A missing data pattern is said to be monotone if the variables Yj can be ordered such that if Yj is missing then all variables Yk with k>j are also missing. This occurs, for example, in longitudinal studies with drop-out. If the pattern is not monotone, it is called non-monotone or general.

**How do you simulate a Markov chain?**

Simulating from a Markov Chain One can simulate from a Markov chain by noting that the collection of moves from any given state (the corresponding row in the probability matrix) form a multinomial distribution. One can thus simulate from a Markov Chain by simulating from a multinomial distribution.

#### What is a trace plot?

Trace plots are similar to perturbation plots for non-mixture designs. They are used compare the effects of all the components in the design space. The factors tool is used to set the reference blend through which the traces are plotted.

#### How do you enter pool data in SPSS?

Click Data -> Split File. In the dialog box select “Imputation_” and move it to “Groups Based On:” box. Check “Compare Groups”. Now any analysis that you run should get run on Original Data, Iteration No 1 data, Iteration No 2 data… and the pooled data.

**How many imputations are needed?**

An old answer is that 2–10 imputations usually suffice, but this recommendation only addresses the efficiency of point estimates. You may need more imputations if, in addition to efficient point estimates, you also want standard error (SE) estimates that would not change (much) if you imputed the data again.