Nonparametric/nonlinear regression I am looking for a survey/book on some state-of-the-art non-parametric (or nonlinear regression) methods, preferably with an inclination towards sequential data. Untill now I have used gaussian process regression and some others such as knn regression, Random Forest regression, etc. I am looking for other possible regressors for example particle filtering based regression, etc. As mentioned earlier, a recent survey would be great.
 A: I think this textbook is a good place to start: https://www.amazon.com/High-Dimensional-Statistics-Non-Asymptotic-Statistical-Probabilistic-ebook/dp/B07N46XF8B
This is a fairly sophisticated text, I consider it a research-level introduction to many nonparametric methods and you can think of this as a more mathematically intense Elements of Statistical Learning.
I'd also encourage you to take a look at sieve estimators which are quite a popular method in the econometrics literature but I think unfortunately overlooked in some other fields. These estimators are very useful as they nest many types of nonparametric estimation procedures from series estimators to neural networks and even plain old linear regression. There is actually a nice and easy youtube video explaining them: https://www.youtube.com/watch?v=cqecz-DL-jI
For a more sophisticated approach including inference, results see: https://www.sciencedirect.com/science/article/pii/S157344120706076X
A: I found that the following link gives a comprehensive summary of the different methods. 
http://stat.columbia.edu/~porbanz/npb-tutorial.html
posting this as an answer so that this may come of use to someone else
