For example, economic and financial data are often time series. Relationships between predictors and response are often nonlinear.
Real data can have quite substantial outliers from a variety of causes.
Standard techniques assume the variables in the model is known exactly when doing inference on the parameters. Frequently some or all of these important features are ignored when tackling a regression or regression-like model. Techniques for dealing with each of these problems individually have been around for some time - in some cases for many decades. This paper is an attempt to give a relatively non-technical outline of some of the ideas, with some examples.