While reading a highly cited paper I found completely new terminology. Google and student-friends who are studying mathematics do not know it. Can you help please?
It appears in the discussion of $l_p$-norm penalty terms in regression.
[...] A value of $p = 2$ leads to the ridge estimate, while $p = 0$ corresponds to traditional model selection. It is well known that the estimates have a parsimonious property (with some components being exactly zero) for $p \leq 1 $ only, while the optimization problem in (3) is only convex for $p \geq 1$ [...]
What does "parsimonious" mean in this context?