Literature on nonparametric density estimation I am about to write my bachelor thesis about non-parametric density estimation, especially kernel density estimators and their application in classification. As I am quite new to looking for academic literature, I am having a hard time finding the most important and modern papers, or other resources, and would be glad if someone could give me a hint. Right now I am mainly working with older works (especially by Silverman and Devroye).
 A: Kernel Smoothing by Wand & Jones was my main book reference, you can follow the literature from their bibliography. It's very well written, thin and technical just enough
A: Adding to Aksakal, there are two more classical books that are worth recommending:

Silverman, B. W. (1986). Density estimation for statistics and data
  analysis. CRC press.
Scott, D. W. (2015). Multivariate density estimation: theory,
  practice, and visualization. John Wiley & Sons.

A: Density estimation is obviously a vastly studied topic. If you care for results in density estimation with provable guarantees on sample complexity and time of computation, while having approximation up to arbitrarily low error, I strongly recommend you peruse literature from the Theoretical Computer Science community.
A starting point could be this presentation by Diakonikolas, and then, consequently this survey again by Diakonikolas. You will find folklore and more recent results on non-parametric density estimation, for discrete and continuous distributions. The references in the presentation and survey all contain pointers to a wealth of literature (with clear discussion on applications) on density estimation, studied by various communities.
I also recommend the books by Devroye-Lugosi and Devroye-Gvorfi, as well as the terse book by Tsybakov, again, if you want theoretically flavoured discussions about density estimation.
