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:
- Readme
file (plain text)
- 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:
- Readme
file (plain text)
- 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:
- Readme
file (plain text)
- 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:
- Readme
file (plain text)
- R
"mpdp" routines R routines source code for
implementing the stepwise algorithm (forward selection + backward
deletion) allowing to select the most predictive design points
(plain text)
- Case studies
Useful file giving the R commandlines
explaining how implementing in a very easy way this stepwise
algorithm on simulations and real datasets (plain text)
- R
"flr" routines Contains R routines of the
alternative functional linear regression in order to make comparison
with our stepwise algorithm (plain text)
NONPARAMETRIC FUNCTIONAL DATA ANALYSIS (NPFDA)
Copyright: All these available online materials are achieved, provided and maintained by Frederic Ferraty; they may be downloaded freely for your own personal study. Use for any commercial purpose is forbidden.