SPLINE ESTIMATOR IN HOMOSCEDASTIC MULTI-RESPONSE NONPARAMETRIC REGRESSION MODEL IN CASE OF UNBALANCED NUMBER OF OBSERVATIONS

2016 ◽  
Vol 100 (9) ◽  
pp. 1433-1453 ◽  
Author(s):  
Nur Chamidah ◽  
Budi Lestari
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Nonparametric Regression Model

Estimation of nonstationary nonparametric regression model with multiplicative structure

2021 ◽  
Author(s):  
Likai Chen ◽  
Ekaterina Smetanina ◽  
Wei Biao Wu
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Risk Premium ◽  
Convergence Rates ◽  
Broad Class ◽  
Estimation Procedure ◽  
Small Samples ◽  
Nonparametric Regression Model ◽  
Conditional Mean ◽  
Mean Function

Abstract This paper presents a multiplicative nonstationary nonparametric regression model which allows for a broad class of nonstationary processes. We propose a three-step estimation procedure to uncover the conditional mean function and establish uniform convergence rates and asymptotic normality of our estimators. The new model can also be seen as a dimension-reduction technique for a general two-dimensional time-varying nonparametric regression model, which is especially useful in small samples and for estimating explicitly multiplicative structural models. We consider two applications: estimating a pricing equation for the US aggregate economy to model consumption growth, and estimating the shape of the monthly risk premium for S&P 500 Index data.

scholarly journals Estimasi Fungsi Regresi Dalam Model Regresi Nonparametrik Birespon Menggunakan Estimator Smoothing Spline dan Estimator Kernel

2018 ◽  
Vol 15 (2) ◽  
pp. 20 ◽  
Author(s):  
Budi Lestari
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Kernel Estimator ◽  
Regression Function ◽  
Predictor Variables ◽  
Smoothing Spline ◽  
Estimating Functions ◽  
Nonparametric Regression Model ◽  
Estimation Techniques ◽  
Connection Pattern

Abstract Regression model of bi-respond nonparametric is a regression model which is illustrating of the connection pattern between respond variable and one or more predictor variables, where between first respond and second respond have correlation each other. In this paper, we discuss the estimating functions of regression in regression model of bi-respond nonparametric by using different two estimation techniques, namely, smoothing spline and kernel. This study showed that for using smoothing spline and kernel, the estimator function of regression which has been obtained in observation is a regression linier. In addition, both estimators that are obtained from those two techniques are systematically only different on smoothing matrices. Keywords: kernel estimator, smoothing spline estimator, regression function, bi-respond nonparametric regression model. AbstrakModel regresi nonparametrik birespon adalah suatu model regresi yang menggambarkan pola hubungan antara dua variabel respon dan satu atau beberapa variabel prediktor dimana antara respon pertama dan respon kedua berkorelasi. Dalam makalah ini dibahas estimasi fungsi regresi dalam  model regresi nonparametrik birespon menggunakan dua teknik estimasi yang berbeda, yaitu smoothing spline dan kernel. Hasil studi ini menunjukkan bahwa, baik menggunakan smoothing spline maupun menggunakan kernel, estimator fungsi regresi yang didapatkan merupakan fungsi linier dalam observasi. Selain itu, kedua estimator fungsi regresi yang didapatkan dari kedua teknik estimasi tersebut secara matematis hanya dibedakan oleh matriks penghalusnya.Kata Kunci : Estimator Kernel, Estimator Smoothing Spline, Fungsi Regresi, Model Regresi Nonparametrik Birespon.

scholarly journals Kernel-Spline Estimation of Additive Nonparametric Regression Model

2019 ◽  
Vol 546 ◽  
pp. 052028
Author(s):  
Rahmat Hidayat ◽  
I Nyoman Budiantara ◽  
Bambang Widjanarko Otok ◽  
Vita Ratnasari
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Nonparametric Regression Model ◽  
Additive Nonparametric Regression ◽  
Spline Estimation

Bi-responses nonparametric regression model using MARS and its properties

2015 ◽  
Vol 9 ◽  
pp. 1417-1427 ◽  
Author(s):  
Ayub Parlin Ampulembang ◽  
Bambang Widjanarko Otok ◽  
Agnes Tuti Rumiati ◽  
Budiasih
Keyword(s):  
Regression Model ◽  
Nonparametric Regression ◽  
Nonparametric Regression Model
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