A REGRESSION ESTIMATION METHOD FOR COLLINEAR PREDICTORS

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.

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Oxygen uptake plateau diagnosis using a new developed segmented regression estimation method for autocorrelated data

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2021.3069458 ◽

2021 ◽

pp. 1-1

Author(s):

Silvio Cabral Patricio ◽

Alessandro Jose Queiroz Sarnaglia ◽

Fabio A. Fajardo Molinares ◽

Paulo Henrique Silva Marques Azevedo

Keyword(s):

Oxygen Uptake ◽

Estimation Method ◽

Regression Estimation ◽

Segmented Regression ◽

Autocorrelated Data

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DGQR estimation for interval censored quantile regression with varying-coefficient models

PLoS ONE ◽

10.1371/journal.pone.0240046 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0240046

Author(s):

ChunJing Li ◽

Yun Li ◽

Xue Ding ◽

XiaoGang Dong

Keyword(s):

Quantile Regression ◽

Estimation Method ◽

Real Data ◽

Regression Estimation ◽

Varying Coefficient Models ◽

Simulation Studies ◽

Varying Coefficient ◽

Censored Quantile Regression ◽

Direct Generalization ◽

Interval Censored

This paper propose a direct generalization quantile regression estimation method (DGQR estimation) for quantile regression with varying-coefficient models with interval censored data, which is a direct generalization for complete observed data. The consistency and asymptotic normality properties of the estimators are obtained. The proposed method has the advantage that does not require the censoring vectors to be identically distributed. The effectiveness of the method is verified by some simulation studies and a real data example.

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The Impact of Urbanization on Income Inequality: A Study in Vietnam

Journal of Risk and Financial Management ◽

10.3390/jrfm12030146 ◽

2019 ◽

Vol 12 (3) ◽

pp. 146 ◽

Cited By ~ 2

Author(s):

Ha ◽

Le ◽

Trung-Kien

Keyword(s):

Income Inequality ◽

Estimation Method ◽

School Enrollment ◽

Regression Estimation ◽

Short Term ◽

Enrollment Rate ◽

Pooled Mean Group ◽

Impact Of Urbanization ◽

The Impact

This paper explores the impact of urbanization on income inequality in Vietnam, using the regression estimation method with panel data including Driscoll and Kraay, and Pooled Mean Group. The research data cover 63 provinces in Vietnam from 2006 to 2016. The results show that in the long term, urbanization has an impact on reducing income inequality. In the short term, urbanization has a negligible impact on income inequality. The hypothesis of an inverted-U-shaped relationship between urbanization and income inequality is confirmed. The high school enrollment rate and the proportion of agriculture have an effect on reducing income inequality.

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PENDEKATAN REGRESI SPLINE UNTUK MEMODELKAN POLA PERTUMBUHAN BERAT BADAN BALITA

E-Jurnal Matematika ◽

10.24843/mtk.2018.v07.i03.p212 ◽

2018 ◽

Vol 7 (3) ◽

pp. 259

Author(s):

NI LUH SUKERNI ◽

I KOMANG GDE SUKARSA ◽

NI LUH PUTU SUCIPTAWATI

Keyword(s):

Regression Model ◽

Cross Validation ◽

Estimation Method ◽

Growth Patterns ◽

Regression Estimation ◽

Spline Regression ◽

Quadratic Spline ◽

Nonparametric Regression Estimation ◽

Weight Growth ◽

High Flexibility

The study is aimed to estimate the best spline regression model for toddler’s weight growth patterns. Spline is one of the nonparametric regression estimation method which has a high flexibility and is able to handle data that change in particular subintervals so thus resulting in model which fitted the data. This study uses data of toddler’s weight growth at Posyandu Mekar Sari, Desa Suwug, Kabupaten Buleleng. The best spline regression model is chosen based on the minimum Generalized Cross Validation (GCV) value. The study shows that the best spline regression model for the data is quadratic spline regression model with six optimal knot points. The minimum GCV value is 0,900683471925 with the determination coefficient equals to 0,954609.

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ANALISIS REGRESI NONPARAMETRIK SPLINE MULTIVARIAT UNTUK PEMODELAN INDIKATOR KEMISKINAN DI INDONESIA

E-Jurnal Matematika ◽

10.24843/mtk.2016.v05.i03.p129 ◽

2016 ◽

Vol 5 (3) ◽

pp. 111 ◽

Cited By ~ 1

Author(s):

DESAK AYU WIRI ASTITI ◽

I WAYAN SUMARJAYA ◽

MADE SUSILAWATI

Keyword(s):

Nonparametric Regression ◽

Cross Validation ◽

Social Economy ◽

Estimation Method ◽

Regression Method ◽

Regression Estimation ◽

Multivariate Spline ◽

Nonparametric Regression Estimation ◽

Poverty Indicators ◽

The Relationship

The aim of this study is to obtain statistics models which explain the relationship between variables that influence the poverty indicators in Indonesia using multivariate spline nonparametric regression method. Spline is a nonparametric regression estimation method that is automatically search for its estimation wherever the data pattern move and thus resulting in model which fitted the data. This study, uses data from survey of Social Economy National (Susenas) and survey of Employment National (Sakernas) of 2013 from the publication of the Central Bureau of Statistics (BPS). This study yields two models which are the best model from two used response variables. The criterion uses to select the best model is the minimum Generalized Cross Validation (GCV). The best spline model obtained is cubic spline model with five optimal knots.

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