Handbook of Partial Least Squares, Concepts, Methods and Applications. Edited by V. Esposito Vinzi, W.W. Chin, J Hensley and H. Wang. Published by Springer.
This Handbook is a book of 33 papers selected from three rounds of peer review process. There is a lot of very good material in this book. I wish that I had delved into it earlier
Chapter 28 which is a paper on How to Write Up and Report PLS Partial Least Squares Analyses, discusses Sample size and goes into detail about how and why you can use a smaller sample size with PLS. That alone is enough reason to read this book. Add to it the tables and examples there is enough material to keep me really busy reading the next time I need to do a questionnaire.
Smoothing Spline ANOVA Models by Chong Gu, published by Springer
I am intrigued by this book. Splines were my favorite thing in graduate school. I have made a lot of ANOVA models. It is fun to see what I tried to do finally achieved.
Smoothing Splines ANOVA Models uses R as the programming language. Great to see in a book are in depth proofs and R code.
Chapter 3.3 shows how to draw Bayesian confidence intervals in R.
Chapter 3.10.1 discusses the difference between natural splines and B splines. That B splines have different boundary conditions.
There is code for doing cubic splines with a jump. Something that you run into with real data.
In Chapter 8.63 about hazard functions and the Weibull family has code for cubic spline Weibull regression with censored and truncated data.
I am enjoying reading this book. The code works and the examples are easy to understand.