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

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

Fully Nonparametric Regression Estimation Based on Empirical Mode Decomposition

2012 ◽  
Vol 271-272 ◽  
pp. 932-935
Author(s):  
Hong Ying Hu ◽  
Wen Long Li ◽  
Feng Qiang Zhao
Keyword(s):  
Empirical Mode Decomposition ◽  
Wavelet Decomposition ◽  
Estimation Method ◽  
Regression Function ◽  
Regression Estimation ◽  
Function Estimation ◽  
Wavelet Regression ◽  
Nonparametric Regression Estimation ◽  
Mode Decomposition ◽  
Non Stationary Signal

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.

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

PLoS ONE ◽  
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.

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

2019 ◽  
Vol 12 (3) ◽  
pp. 146 ◽  
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.

scholarly journals PENDEKATAN REGRESI SPLINE UNTUK MEMODELKAN POLA PERTUMBUHAN BERAT BADAN BALITA

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.

scholarly journals ANALISIS REGRESI NONPARAMETRIK SPLINE MULTIVARIAT UNTUK PEMODELAN INDIKATOR KEMISKINAN DI INDONESIA

2016 ◽  
Vol 5 (3) ◽  
pp. 111 ◽  
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.

The biochemistry of drugs and doping methods used to enhance aerobic sport performance

2008 ◽  
Vol 44 ◽  
pp. 63-84 ◽  
Author(s):  
Chris E. Cooper
Keyword(s):  
Oxygen Uptake ◽  
Oxygen Content ◽  
Oxygen Delivery ◽  
Sport Performance ◽  
Blood Substitutes ◽  
Altitude Training ◽  
Blood Oxygen ◽  
Oxygen Carriers ◽  
Sports Performance ◽  
Blood Doping

Optimum performance in aerobic sports performance requires an efficient delivery to, and consumption of, oxygen by the exercising muscle. It is probable that maximal oxygen uptake in the athlete is multifactorial, being shared between cardiac output, blood oxygen content, muscle blood flow, oxygen diffusion from the blood to the cell and mitochondrial content. Of these, raising the blood oxygen content by raising the haematocrit is the simplest acute method to increase oxygen delivery and improve sport performance. Legal means of raising haematocrit include altitude training and hypoxic tents. Illegal means include blood doping and the administration of EPO (erythropoietin). The ability to make EPO by genetic means has resulted in an increase in its availability and use, although it is probable that recent testing methods may have had some impact. Less widely used illegal methods include the use of artificial blood oxygen carriers (the so-called ‘blood substitutes’). In principle these molecules could enhance aerobic sports performance; however, they would be readily detectable in urine and blood tests. An alternative to increasing the blood oxygen content is to increase the amount of oxygen that haemoglobin can deliver. It is possible to do this by using compounds that right-shift the haemoglobin dissociation curve (e.g. RSR13). There is a compromise between improving oxygen delivery at the muscle and losing oxygen uptake at the lung and it is unclear whether these reagents would enhance the performance of elite athletes. However, given the proven success of blood doping and EPO, attempts to manipulate these pathways are likely to lead to an ongoing battle between the athlete and the drug testers.

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