  Length-biased data are a particular case of weighted data, which arise in
many situations: biomedicine, quality control or epidemiology among others. In
this paper we study the theoretical properties of kernel density estimation in
the context of length-biased data, proposing two consistent bootstrap methods
that we use for bandwidth selection. Apart from the bootstrap bandwidth
selectors we suggest a rule-of-thumb. These bandwidth selection proposals are
compared with a least-squares cross-validation method. A simulation study is
accomplished to understand the behaviour of the procedures in finite samples.
