scholarly journals Distribution free prediction intervals for multiple functional regression

2022 ◽  
Vol 15 (2) ◽  
pp. 161-170
Author(s):  
Kehui Chen ◽  
Ryan Kelly
Keyword(s):  
Prediction Intervals ◽  
Functional Regression ◽  
Distribution Free

Distribution-Free and other Prediction Intervals

1987 ◽  
Vol 41 (1) ◽  
pp. 11-15
Author(s):  
Hendrik S. Konijn
Keyword(s):  
Prediction Intervals ◽  
Distribution Free

Distribution-Free and Other Prediction Intervals

1987 ◽  
Vol 41 (1) ◽  
pp. 11 ◽  
Author(s):  
Hendrik S. Konijn
Keyword(s):  
Prediction Intervals ◽  
Distribution Free

Adaptive Distribution-Free Tests

1973 ◽  
Vol 2 (4) ◽  
pp. 337-356
Author(s):  
Ronald Randles ◽  
Robert Hogg
Keyword(s):  
Distribution Free

Statistics Distribution-Free

1957 ◽  
Vol 2 (4) ◽  
pp. 102-103
Author(s):  
ALLEN L. EDWARDS
Keyword(s):  
Distribution Free

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

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.

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