nonparametric inference
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Author(s):  
Min Dai ◽  
Jinqiao Duan ◽  
jianyu Hu ◽  
Xiangjun Wang

The information detection of complex systems from data is currently undergoing a revolution,driven by the emergence of big data and machine learning methodology. Discovering governingequations and quantifying dynamical properties of complex systems are among central challenges. Inthis work, we devise a nonparametric approach to learn the relative entropy rate from observationsof stochastic differential equations with different drift functions. The estimator corresponding tothe relative entropy rate then is presented via the Gaussian process kernel theory. Meanwhile, thisapproach enables to extract the governing equations. We illustrate our approach in several examples.Numerical experiments show the proposed approach performs well for rational drift functions, notonly polynomial drift functions.


2021 ◽  
Vol 7 (1) ◽  
pp. 17
Author(s):  
Wende Clarence Safari ◽  
Ignacio López-de-Ullibarri ◽  
María Amalia Jácome

We introduce nonparametric estimators to estimate the conditional survival function, cure probability and latency function in the setting of a mixture cure model when the cure status is partially known. For the sake of illustration, we present an application concerning patients hospitalized with COVID-19 in Galicia (Spain) during the first outbreak of the epidemic.


2021 ◽  
Vol 919 (1) ◽  
pp. 11
Author(s):  
Ming-Zhe Han ◽  
Jin-Liang Jiang ◽  
Shao-Peng Tang ◽  
Yi-Zhong Fan

2021 ◽  
Vol 5 (4) ◽  
pp. 182-190
Author(s):  
Antônio Pelli- Neto ◽  
Carmino Hayashi ◽  
Giovana Barbosa de Oliveira ◽  
Paloma Cristina Pimenta ◽  
Afonso Pelli

The least squares method has been largely used in several areas, mainly because of its simplicity. It is a widely used knowledge tool. However, the current advances in Information Technology have contributed to the development of decision support systems, in a search for greater reliability of predictions from samples. The use of Information Technology in Limnology is still limited. The main objective of this study is to show the possibility of using Artificial Neural Network in the process of inference of the total number of the rate of biological communities from samples. Our data show that the use of nonparametric inference, along with nonlinear data mapping, may lead to more consistent and efficient results, as the Artificial Neural Networks.


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