Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis

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Interval-wise testing for functional data (2017, background): “The former approach commonly relies on distributional assumptions on functional data and/or on asymptotic results (Cuevas et al. 2004; Abramovich and Angelini 2006; Antoniadis and Sapatinas 2007; Horv´ath and Kokoszka 2012; Staicu et al. 2014; Zhang and Liang 2014).”
A general framework for functional regression modelling (2017, background): “This also holds—at least to some extent—for inference in such models (e.g., Greven et al., 2008; Scheipl et al., 2008; Wood, 2013) and its transfer to functional regression (e.g., Staicu et al., 2014; Swihart et al., 2014; McLean et al., 2015).”
A Two Sample Distribution-Free Test for Functional Data with Application to a Diffusion Tensor Imaging Study of Multiple Sclerosis (2016, background): “…of two independent samples of curves Ramsay and Silverman (2005) introduced a pointwise t-test, Zhang et al. (2010) presented an L2-norm-based test, Horváth et al. (2013) proposed a test based on the sample means of the curves and Staicu et al. (2014) developed a pseudolikelihood ratio test.”