scholarly journals Regresi Spline Polynomial Truncated Biprediktor untuk Identifikasi Perubahan Jumlah Trombosit Pasien Demam Berdarah Dengue

2019 ◽  
Vol 7 (2) ◽  
pp. 97-112
Author(s):  
Anna Islamiyati
Keyword(s):  
Platelet Count ◽  
Longitudinal Study ◽  
Regression Model ◽  
Dengue Fever ◽  
Nonparametric Regression ◽  
Treatment Time ◽  
Model Estimation ◽  
Nonparametric Regression Model ◽  
Simultaneous Model ◽  
Spline Polynomial

Abstract:This paper is a longitudinal study using a nonparametric regression model to identify changes in platelet count from dengue fever. Changes in platelet counts were analyzed based on treatment time and hematocrit count factors. The estimator method proposed is spline polynomial truncated bipredictor. Based on the results of the simultaneous model estimation, we obtained GCV = 714.72 and R2 = 95.9%, it means the model is feasible to explain and identify changes in platelet count based on the time of treatment and the number of hematocrit from DBD patients. Based on the data, there are four patterns of platelet change based on time of treatment and three patterns of platelet change based on hematocrit that are different from each other.Abstrak:Paper ini merupakan studi longitudinal dengan menggunakan model regresi nonparametrik untuk mengidentifikasi perubahan jumlah trombosit demam berdarah. Perubahan jumlah trombosit dianalisis berdasarkan faktor waktu perawatan dan jumlah hematokrit. Metode estimator yang diusulkan adalah spline polynomial truncated bi prediktor. Berdasarkan hasil taksiran model simultan diperoleh GCV = 714,72 dan R2 = 95,9%, artinya model layak untuk menjelaskan dan mengidentifikasi perubahan jumlah trombosit berdasarkan waktu perawatan dan jumlah hematokrit pasien DBD. Berdasarkan data, terdapat empat pola perubahan trombosit berdasarkan waktu perawatan dan tiga pola perubahan trombosit berdasarkan hematokrit yang berbeda satu sama lain.

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.

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