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

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

Probability in the Engineering and Informational Sciences ◽

10.1017/s026996481600053x ◽

2017 ◽

Vol 32 (1) ◽

pp. 37-57 ◽

Cited By ~ 3

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.

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Consistency of the P–C estimator in non parametric regression model based on m-END errors

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2021.1924786 ◽

2021 ◽

pp. 1-12

Author(s):

Shuili Zhang ◽

Tiantian Hou ◽

Cong Qu

Keyword(s):

Regression Model ◽

Parametric Regression ◽

Model Based ◽

Non Parametric

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

2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) ◽

10.1109/eeeic.2019.8783908 ◽

2019 ◽

Author(s):

Roya Nikjoo ◽

Abouzar Estebsari ◽

Mohammad Nazari

Keyword(s):

Regression Model ◽

Continuous Time ◽

Bernstein Polynomial ◽

Load Forecasting ◽

Parametric Regression ◽

Non Parametric

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On Complete Consistency for the Estimator of Nonparametric Regression Model Based on Asymptotically Almost Negatively Associated Errors

Methodology And Computing In Applied Probability ◽

10.1007/s11009-020-09813-x ◽

2020 ◽

Author(s):

Aiting Shen ◽

Siyao Zhang

Keyword(s):

Regression Model ◽

Nonparametric Regression ◽

Nonparametric Regression Model ◽

Negatively Associated ◽

Complete Consistency ◽

Model Based ◽

Asymptotically Almost Negatively Associated

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On Avoidance of the Over-fitting in the B-Spline Non-parametric Regression Model

Ouyou toukeigaku ◽

10.5023/jappstat.33.51 ◽

2004 ◽

Vol 33 (1) ◽

pp. 51-69 ◽

Cited By ~ 1

Author(s):

Hirokazu Yanagihara ◽

Megu Ohtaki

Keyword(s):

Regression Model ◽

Parametric Regression ◽

B Spline ◽

Fitting In ◽

Non Parametric

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A Non-parametric Regression Model for Consumption of Urban Residents and Foreign Exchange Reserve

Modern Applied Science ◽

10.5539/mas.v3n11p59 ◽

2009 ◽

Vol 3 (11) ◽

Author(s):

Yi Ning ◽

Xiangdong Song

Keyword(s):

Regression Model ◽

Foreign Exchange ◽

Foreign Exchange Reserve ◽

Urban Residents ◽

Parametric Regression ◽

Non Parametric

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Complete consistency for the estimator of nonparametric regression model based on m-END errors

Open Mathematics ◽

10.1515/math-2021-0081 ◽

2021 ◽

Vol 19 (1) ◽

pp. 1197-1209

Author(s):

Shui-Li Zhang ◽

Tiantian Hou ◽

Cong Qu

Keyword(s):

Regression Model ◽

Nonparametric Regression ◽

Convergence Rates ◽

Nonparametric Regression Model ◽

Complete Consistency ◽

Model Based ◽

General Conditions

Abstract In this paper, we study the complete consistency for the estimator of nonparametric regression model based on m-END errors and obtain the convergence rates of the complete consistency under more general conditions. Finally, some simulations are illustrated to verify the validity of our results.

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