Simple wavelet decompositions can be extended through lifting schemes to have better smoothness properties. This can lead to simpler algorithms for nonparametric regression problems with uneven or random designs.
The asymptotic behavior of density estimation and nonparametric regression problems is linked to the problem of estimating a smooth function in a continuous white noise experiment. Bibliography of work on deficiency distance between experiments
Standard asymptotic results no longer apply in mixture models because under the null hypothesis there is lack of identifiability.
Sandwich estimators have been used to estimate the variance in models with cluster observations (repeated measures, longitudinal data). These are approximately normal only if the Effective Number of Groups is large.
Nonparametric Regression of Presidential Approval Rating with Correlated Observations