functional regression
General Framework for Nonlinear Functional Regression with Reproducing Kernel Hilbert Spaces
... general functional regression ...between functional responses and predictors. In this case, functional regression model model is extension of the multivariate linear linear ...
18
Stream Temperature Modeling Using Functional Regression Models.
... three functional models were compared to two classical approaches to model the stream ...The functional models were able to model a complete season of stream temperature as a continuous curve compared to ...
32
A new look at weather-related health impacts through functional regression.
... The first strength of the functional regression models proposed in the present paper is the natural addition of a time-of-day or time-of-year aspect. Indeed, the SFLM and FFLM acknowledge that temperature ...
9
Nonlinear functional regression: a functional RKHS approach
... RKHS functional regression method, we illustrate in Figure 4 the prediction of our RKHS estimate and LFR estimate for four weather ...linear functional re- gression estimate, our method deals with ...
8
A k-nearest neighbor approach for functional regression
... In the present paper, we consider the functional regression setting, where the goal is to predict a scalar response Y from some infinite-dimensional observa- tions X. More precisely, we will denote by (X, Y ...
10
Input Output Kernel Regression
... Kernel Regression that present two additional properties compared to OK3-based methods: namely, models are able to take into account structure in input data and can be learned within the framework of penalized ...
40
Real-time estimation for functional stochastic regression models
... heteroscedastic functional regression model with martingale difference errors is ...the regression as well as the conditional vari- ance operators when the response is a real-valued random vari- able ...
29
Interpretable sparse sliced inverse regression for functional data
... inverse regression for functional data ...the functional regression model in which a real random variable has to be predicted from functional ...in functional SIR in order to ...
2
Student Sliced Inverse Regression
... 6. Conclusion and future work We proposed a new approach referred to as Student SIR to robustify SIR. In contrast to most existing approaches which aim at replacing the standard SIR estimators by robust versions, we ...
37
Collaborative Sliced Inverse Regression
... 2 Sliced Inverse Regression (SIR) 2.1 Method Back in 1991, SIR is introduced in [19] as a data-analytic tool : even if the per- formance of computers and the capability to explore huge dataset increased ...
29
Scale checks in censored regression
... the regression function with cen- sored data, few results are obtained for the conditional variance and especially for a scale function which is different from the usual standard deviation ...
23
Bayesian regression and Bitcoin
... Bayesian regression approach as outlined above allows us to utilize the existence of patterns for the purpose of better prediction without explicitly finding ...
7
Estimation non paramétrique de la regression
... C’est la raison pour laquelle on introduit la recherche de l’estimateur de la fonction de régression « r» et la convergence presque complète.Pour cela, la méthode du noyau intro- duite p[r] ...
33
Penalized wavelet monotone regression
... four regression functions, based on samples of size n = ...tone regression, namely the fuel consumption data that relate fuel efficiency (in miles per gallon) to engine ...
18
Kernel Selection in Nonparametric Regression
... a regression model has no reason to be bounded, there were significant additional difficulties, bypassed in [4], to establish an oracle inequality for the numera- tor’s adaptive ...
24
Polychotomous regression : application to landcover prediction
... 1.4 Penalized log-likelihood estimation 7 chosen a time dependence of order 1 since it allows to produce a model not too complex with a reasonable number of parameters to estimate (with respect to the number of ...
14
Nonparametric regression and spatially inhomogeneous information
... 2. Discussion 2.1. Motivation. In most cases, the models considered in curve estimation do not allow situations where the data is inhomogeneous, in so far as the amount of data is implicitly assumed constant over space ...
151
Rank-R Multiway Logistic Regression
... ` 2 penalties in order to try to catch smoother effects in time and space. 5 Conclusion Rank-R Multiway Logistic Regression is presented in this paper and shows promising results in EEG application. While R-MLR is ...
7
Extreme Partial Least-Squares regression
... ? Corresponding author, stephane.girard@inria.fr Abstract We propose a new approach, called Extreme-PLS, for dimension reduction in regression and adapted to distribution tails. The objective is to find linear ...
40
Nonlinear regression using smooth Bayesian estimation
... Index Terms— Bayesian algorithm, Hamiltonian Monte- Carlo, MCMC, Parameter estimation, Radar altimetry. 1. INTRODUCTION In many applications, the observed data are well described by a nonlinear function of a vector of ...
6