# Online resources (R routines, case studies, etc)

### This webpage proposes downloadable materials related to methods dealing with high dimensional data. Note that the "plain text" files are very useful for practitionners wishing implementing themselves these methods.

#### NESTED-KERNEL ESTIMATOR

• Ferraty F., Zullo A., Fauvel, M. (2019). Nonparametric regression on contaminated functional predictor with application to hyperspectral data. Econometrics and Statistics, 9, 95-107.

The files given just below gathers all what you need for implementing this method:
• R routines R routines source code containing all R routines dealing with nested-kernel estimator (plain text)
• Implementation R commandlines allowing to implement the NKE methodology with different simulated datasets covering discrimination (i.e. responses = labels) as well as regression (scalar responses) setting (plain text)

#### NONPARAMETRIC VARIABLE SELECTION (NOVAS)

• Ferraty F., Hall P. (2015). An Algorithm for Nonlinear, Nonparametric Model Choice and Prediction. J. Comput. Graph. Stat., 24, 695-714 (arXiv:1401.8097).

The files given just below gathers all what you need for implementing this method:
• R routines R routines source code containing all R routines dealing with NOVAS (NOnparametric VAriable Selection) (plain text)
• Case studies Useful file giving the R commandlines explaining how implementing in a very easy way this nonparametric variable selection. In particular, it allows to implement this method on different datasets dealing with food industry, petroleum or genomics problems (plain text)
• Simulations R commandlines allowing to reproduce the simulation study leading to the results given in Table 4 of the above-mentioned work (plain text)

#### NONPARAMETRIC REGRESSION WHEN BOTH RESPONSE AND PREDICTOR ARE RANDOM FUNCTIONS

• Ferraty F., Van Keilegom I., Vieu, P. (2012). Regression when both Response and Predictor are Functions. J. Multivariate Anal., 109, 10-28.

The files given just below gathers all what you need for implementing this method:
• R routines R routines source code containing all R routines dealing with NPFDA (NonParametric Functional Data Analysis) ; it includes routines necessary for implementing the nonparametric regression when both response and predictor are functions (plain text)
• Case studies Useful file giving the R commandlines explaining how implementing in a very easy way this functional nonparametric regression. In particular, it allows to reproduce all methodologies presented in the above-mentioned paper: estimations, predictions, simulations, asymptotic distributions, bootstrapped errors, pseudo-confidence area (plain text)

#### MOST PREDICTIVE DESIGN POINTS (mpdp) FOR FUNCTIONAL DATA PREDICTORS

• Ferraty F., Hall P., Vieu, P. (2010). Most predictive design points for functional data predictors. Biometrika, 97, 807-824.

The files given just below gathers all what you need for implementing this method: