Simulation for Data Science with R

Chapter 10. Simulation with Complex Data

Is an estimator biased in finite samples? Is an estimator consistent under departures from assumptions? Is the sampling variance under/overestimated under different assumptions? Does method A provide better properties than method B in terms of bias, precision, and so on? Is the size of a test correct (achieving nominal level of coverage under the null hypothesis)? Is the power of a test larger than for other tests?

All these questions can be answered by statistical simulation. Some of these questions have already been answered in Chapter 6, Probability Theory Shown by Simulation where the concept of bias, large numbers, and the central limit theorem was shown by simulation. We also saw Monte Carlo-based ...