Pseudo-metrics as Interesting Tool in Nonparametric Functional Regression

Author(s):  
Laurent Delsol ◽  
Aldo Goia
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.


2021 ◽  
pp. 1-16
Author(s):  
Jérémie Boudreault ◽  
André St-Hilaire ◽  
Fateh Chebana ◽  
Normand E. Bergeron

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1639
Author(s):  
Miguel Lao-Pérez ◽  
Diaa Massoud ◽  
Francisca M. Real ◽  
Alicia Hurtado ◽  
Esperanza Ortega ◽  
...  

Most mammalian species of the temperate zones of the Earth reproduce seasonally, existing a non-breeding period in which the gonads of both sexes undergo functional regression. It is widely accepted that photoperiod is the principal environmental cue controlling these seasonal changes, although several exceptions have been described in other mammalian species in which breeding depends on cues such as food or water availability. We studied the circannual reproductive cycle in males of the Mediterranean pine vole, Microtus duodecimcostatus, in the Southeastern Iberian Peninsula. Morphological, hormonal, functional, molecular and transcriptomic analyses were performed. As reported for populations of other species from the same geographic area, male voles captured in wastelands underwent seasonal testis regression in summer whereas, surprisingly, those living either in close poplar plantations or in our animal house reproduced throughout the year, showing that it is the microenvironment of a particular vole subpopulation what determines its reproductive status and that these animals are pure opportunistic, photoperiod-independent breeders. In addition, we show that several molecular pathways, including MAPK, are deregulated and that the testicular “immune privilege” is lost in the inactive testes, providing novel mechanisms linking seasonal testosterone reduction and testis regression.


1997 ◽  
Vol 134 (1-2) ◽  
pp. 94
Author(s):  
P. Schuff-Werner ◽  
V. Schettler ◽  
H. Rexer ◽  
R. Hilgers ◽  
G.A. Müller

Author(s):  
Frédéric Ferraty ◽  
Philippe Vieu

This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression model, additive functional regression model, smooth nonparametric functional model, and single functional index model. It also considers a number of new models, directly adapted to functional variables from the existing standard multivariate literature.


2019 ◽  
Vol 51 (9) ◽  
pp. 1012-1024
Author(s):  
Devashish Das ◽  
Kalyan S. Pasupathy ◽  
Curtis B. Storlie ◽  
Mustafa Y. Sir

Author(s):  
Frédéric Ferraty ◽  
Philippe Vieu

This article provides an overview of recent nonparametric and semiparametric advances in kernel regression estimation for functional data. In particular, it considers the various statistical techniques based on kernel smoothing ideas that have recently been developed for functional regression estimation problems. The article first examines nonparametric functional regression modelling before discussing three popular functional regression estimates constructed by means of kernel ideas, namely: the Nadaraya-Watson convolution kernel estimate, the kNN functional estimate, and the local linear functional estimate. Uniform asymptotic results are then presented. The article proceeds by reviewing kernel methods in semiparametric functional regression such as single functional index regression and partial linear functional regression. It also looks at the use of kernels for additive functional regression and concludes by assessing the impact of kernel methods on practical real-data analysis involving functional (curves) datasets.


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