Literature on robustness of regression assumptions In my OLS regression not all assumptions are perfectly met, but I read that due to a large sample size there is a certain robustness to assumptions (my sample is 2500 people). 
E.g. the DV isn't perfectly normal distributed, with significant, but small skewness and kurtosis (if you test for it, e.g. using the 'gvlma' package in R). 
I am now looking for literature to cite when I talk about my regression, e.g. "Inspection of fitted values vs mean plots revealed minimal deviation from homoscedasticity, but within range of robustness of regression (xxx, 1990)." 
 A: Answered in comments:
One thing to note here, is that it doesn't matter if the DV is normally distributed, only if the residuals are (see here: What if residuals are normally distributed, but y is not?), & even then, with a large sample size the central limit theorem will cover you. You are likely to be fine. – gung 
Go to Google Scholar and search with the terms: robustness of regression assumptions. Many of the references are in peer reviewed journals with full text available in JSTOR. – R. Schumacher
Gung, I absolutely believe you, but there are a lot of websites out there listing normal distribution of Y as assumption in linear regression. I hope the reviewers will know that this is not the case. Do you have a citation that I can use in combination with the "central limit theorem"? – Torvon  
Torvon, any competent textbook will be clear about this. For example, Draper & Smith (Applied Regression Analysis, 2nd Ed.) develop the regression equations at the beginning of section 2.6, then discuss what can be done in a subsection "Without Distributional Assumptions," and only then discuss what can further be done (mainly with the F tests) in a subsection "With Distributional Assumptions." Ultimately, "robustness" is going to be relative to the conclusions you are trying to draw: some of them will be largely insensitive to homoscedasticity but others might be more sensitive. – whuber 
