The progress of electronic devices like sensors allow us to collect data over finer and finer grids. These high-tech data introduce some continuum that leads the statisticians to consider statistical units as functions, surfaces, or more generally as a mathematical object. The generic terminology for this new kind of data is Functional Data (FD) and the statistical methodologies using FD are usually called Functional Data Analysis (FDA). FD appear in numerous fields of sciences like biology, chemometrics, econometrics, geophysics, medicine, etc. The usefulness of FDA as a decision-making tool in major questions like health, environment, etc., makes it really attractive for the statistician community and this is certainly the main motor of its fast development. In that sense, FDA is really an interdisciplinary and modern statistical topic. Several books promoted the practical impact of FDA in various fields of sciences ((Ramsay & Silverman 2002 [266]), (Ramsay & Silverman 2005 [267]), (Ferraty & Vieu 2006 [126]), and (Ferraty & Romain 2011 [124])).