scholarly journals Limit properties of the monotone rearrangement for density and regression function estimation

Bernoulli ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 549-583 ◽  
Author(s):  
Dragi Anevski ◽  
Anne-Laure Fougères
Keyword(s):  
Regression Function ◽  
Function Estimation ◽  
Monotone Rearrangement

Regression Function Estimation Using Spline Wavelets

2005 ◽  
Vol 34 (4) ◽  
pp. 823-832
Author(s):  
Md. KAMAL UDDIN ◽  
U. V. NAIK-NIMBALKAR
Keyword(s):  
Regression Function ◽  
Function Estimation ◽  
Spline Wavelets

Fully Nonparametric Regression Estimation Based on Empirical Mode Decomposition

2012 ◽  
Vol 271-272 ◽  
pp. 932-935
Author(s):  
Hong Ying Hu ◽  
Wen Long Li ◽  
Feng Qiang Zhao
Keyword(s):  
Empirical Mode Decomposition ◽  
Wavelet Decomposition ◽  
Estimation Method ◽  
Regression Function ◽  
Regression Estimation ◽  
Function Estimation ◽  
Wavelet Regression ◽  
Nonparametric Regression Estimation ◽  
Mode Decomposition ◽  
Non Stationary Signal

Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently. It has been applied in many engineering fields. EMD has many similarities with wavelet decomposition. But EMD Decomposition has its own characteristics, especially in accurate trend extracting. Therefore the paper firstly proposes an algorithm of extracting slow-varying trend based on EMD. Then, according to wavelet regression estimation method, a new regression function estimation method based on EMD is presented. The simulation proves the advantages of the approach with easy computation and more accurate result.

scholarly journals Regression Function and Noise Variance Tracking Methods for Data Streams with Concept Drift

2018 ◽  
Vol 28 (3) ◽  
pp. 559-567 ◽  
Author(s):  
Maciej Jaworski
Keyword(s):  
Data Streams ◽  
Goodness Of Fit ◽  
Concept Drift ◽  
Sliding Window ◽  
Regression Function ◽  
Function Estimation ◽  
Noise Variance ◽  
Forgetting Factor ◽  
Variance Estimators ◽  
Variance Change

Abstract Two types of heuristic estimators based on Parzen kernels are presented. They are able to estimate the regression function in an incremental manner. The estimators apply two techniques commonly used in concept-drifting data streams, i.e., the forgetting factor and the sliding window. The methods are applicable for models in which both the function and the noise variance change over time. Although nonparametric methods based on Parzen kernels were previously successfully applied in the literature to online regression function estimation, the problem of estimating the variance of noise was generally neglected. It is sometimes of profound interest to know the variance of the signal considered, e.g., in economics, but it can also be used for determining confidence intervals in the estimation of the regression function, as well as while evaluating the goodness of fit and in controlling the amount of smoothing. The present paper addresses this issue. Specifically, variance estimators are proposed which are able to deal with concept drifting data by applying a sliding window and a forgetting factor, respectively. A number of conducted numerical experiments proved that the proposed methods perform satisfactorily well in estimating both the regression function and the variance of the noise.

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