scholarly journals Non-parametric Regression Model for Continuous-time Day Ahead Load Forecasting with Bernstein Polynomial

2019 ◽  
Author(s):  
Roya Nikjoo ◽  
Abouzar Estebsari ◽  
Mohammad Nazari
Keyword(s):  
Regression Model ◽  
Continuous Time ◽  
Bernstein Polynomial ◽  
Load Forecasting ◽  
Parametric Regression ◽  
Non Parametric

COMPLETE MOMENT CONVERGENCE FOR ARRAYS OF ROWWISE NEGATIVELY ASSOCIATED RANDOM VARIABLES AND ITS APPLICATION IN NON-PARAMETRIC REGRESSION MODEL

2017 ◽  
Vol 32 (1) ◽  
pp. 37-57 ◽  
Author(s):  
Yi Wu ◽  
Xuejun Wang ◽  
Soo Hak Sung
Keyword(s):  
Numerical Simulation ◽  
Regression Model ◽  
Random Variables ◽  
Complete Moment Convergence ◽  
Parametric Regression ◽  
Associated Random Variables ◽  
Negatively Associated ◽  
Complete Consistency ◽  
Moment Convergence ◽  
Non Parametric

In this paper, some results on the complete moment convergence for arrays of rowwise negatively associated (NA, for short) random variables are established. The results obtained in this paper correct the corresponding one obtained in Ko [13] and also improve and generalize the corresponding ones of Kuczmaszewska [14] and Ko [13]. As an application of the main results, we present a result on complete consistency for the estimator in a non-parametric regression model based on NA errors. Finally, we provide a numerical simulation to verify the validity of our result.

Prediction Model for Dissolved Gas in Transformer Oil Based on Non-Parametric Regression

2014 ◽  
Vol 986-987 ◽  
pp. 1410-1413
Author(s):  
Jin Qiang Chen
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Prediction Accuracy ◽  
Power Transformer ◽  
Transformer Oil ◽  
Dissolved Gas ◽  
Data Sequence ◽  
Parametric Regression ◽  
Historical Samples ◽  
Non Parametric

The non-parametric regression prediction model for dissolved gases in power transformer and its application are studied. As the intervals between two analytic experiments of transformer dissolved gas are unfixed,the data sequence sampled with unequal intervals is converted into the data sequences with equal intervals,which is smoothed to form a new sequence. And then use the historical samples data to establish non-parametric regression model for prediction. Compared with the grey model,the non-parametric regression model has better prediction accuracy. The case verifies the correctness and feasibility of the method.

THE CONSISTENCY FOR THE WEIGHTED ESTIMATOR OF NON-PARAMETRIC REGRESSION MODEL BASED ON WIDELY ORTHANT-DEPENDENT ERRORS

2017 ◽  
Vol 32 (3) ◽  
pp. 469-481 ◽  
Author(s):  
Hao Xia ◽  
Yi Wu ◽  
Xinran Tao ◽  
Xuejun Wang
Keyword(s):  
Convergence Rate ◽  
Regression Model ◽  
Moment Condition ◽  
Parametric Regression ◽  
Weighted Estimator ◽  
Complete Consistency ◽  
Model Based ◽  
Widely Orthant Dependent ◽  
Non Parametric

In this paper, the complete consistency for the weighted estimator of non-parametric regression model based on widely orthant-dependent errors is established, where the restriction imposed on the dominating coefficient g(n) is very general. Moreover, under some stronger moment condition, we further obtain the convergence rate of the complete consistency, where the assumption on the dominating coefficient g(n) is also very general.

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