@ARTICLE{publication1, title = "A weighted least-squares cross-validation bandwidth selector for kernel density estimation", author = "{TENREIRO, Carlos}", year = "2017", abstract = "Since the late 1980s, several methods have been considered in the literature to reduce the sample variability of the least-squares cross-validation bandwidth selector for kernel density estimation. In this article, a weighted version of this classical method is proposed and its asymptotic and finite-sample behavior is studied. The simulation results attest that the weighted cross-validation bandwidth performs quite well, presenting a better finite-sample performance than the standard cross-validation method for \“easy-to-estimate\” densities, and retaining the good finite-sample performance of the standard cross-validation method for \“hard-to-estimate\” ones. ", journal = "Communications in Statistics - Theory and Methods", volume = "46", number = "7", pages = "3438-3458", url = "http://www.tandfonline.com/doi/full/10.1080/03610926.2015.1062108" }