scholarly journals Bayesian quantile regression using random B-spline series prior

2017 ◽  
Vol 109 ◽  
pp. 121-143 ◽  
Author(s):  
Priyam Das ◽  
Subhashis Ghosal
Keyword(s):  
Quantile Regression ◽  
B Spline ◽  
Bayesian Quantile Regression

scholarly journals Projection of Long-Term Care Costs in China, 2020–2050: Based on the Bayesian Quantile Regression Method

2019 ◽  
Vol 11 (13) ◽  
pp. 3530 ◽  
Author(s):  
Xiaocang Xu ◽  
Linhong Chen
Keyword(s):  
Quantile Regression ◽  
Long Term Care ◽  
Financial Burden ◽  
Severe Disabilities ◽  
Term Care ◽  
Bayesian Quantile Regression ◽  
The Future ◽  
The Government ◽  
Quantile Regression Method

The aging population in China highlights the significance of elderly long-term care (LTC) services. The number of people aged 65 and above increased from 96 million in 2003 to 150 million in 2016, some of whom were disabled due to chronic diseases or the natural effects of aging on bodily functions. Therefore, the measurement of future LTC costs is of crucial value. Following the basic framework but using different empirical methods from those presented in previous literature, this paper attempts to use the Bayesian quantile regression (BQR) method, which has many advantages over traditional linear regression. Another innovation consists of setting and measuring the high, middle, and low levels of LTC cost prediction for each disability state among the elderly in 2020–2050. Our projections suggest that by 2020, LTC costs will increase to median values of 39.46, 8.98, and 20.25 billion dollars for mild, moderate, and severe disabilities, respectively; these numbers will reach 141.7, 32.28, and 72.78 billion dollars by 2050. The median level of daily life care for mild, moderate, and severe disabilities will increase to 26.23, 6.36, and 27 billion dollars. Our results showed that future LTC cost increases will be enormous, and therefore, the establishment of a reasonable individual-social-government payment mechanism is necessary for the LTC system. The future design of an LTCI system must take into account a variety of factors, including the future elderly population, different care conditions, the financial burden of the government, etc., in order to maintain the sustainable development of the LTC system.

scholarly journals Non-Stationary Flood Frequency Analysis Using Cubic B-Spline-Based GAMLSS Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1867
Author(s):  
Chunlai Qu ◽  
Jing Li ◽  
Lei Yan ◽  
Pengtao Yan ◽  
Fang Cheng ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Design Flood ◽  
Statistical Parameters ◽  
B Spline ◽  
Quantile Regression Model ◽  
Design Values ◽  
The Relationship

Under changing environments, the most widely used non-stationary flood frequency analysis (NFFA) method is the generalized additive models for location, scale and shape (GAMLSS) model. However, the model structure of the GAMLSS model is relatively complex due to the large number of statistical parameters, and the relationship between statistical parameters and covariates is assumed to be unchanged in future, which may be unreasonable. In recent years, nonparametric methods have received increasing attention in the field of NFFA. Among them, the linear quantile regression (QR-L) model and the non-linear quantile regression model of cubic B-spline (QR-CB) have been introduced into NFFA studies because they do not need to determine statistical parameters and consider the relationship between statistical parameters and covariates. However, these two quantile regression models have difficulties in estimating non-stationary design flood, since the trend of the established model must be extrapolated infinitely to estimate design flood. Besides, the number of available observations becomes scarcer when estimating design values corresponding to higher return periods, leading to unreasonable and inaccurate design values. In this study, we attempt to propose a cubic B-spline-based GAMLSS model (GAMLSS-CB) for NFFA. In the GAMLSS-CB model, the relationship between statistical parameters and covariates is fitted by the cubic B-spline under the GAMLSS model framework. We also compare the performance of different non-stationary models, namely the QR-L, QR-CB, and GAMLSS-CB models. Finally, based on the optimal non-stationary model, the non-stationary design flood values are estimated using the average design life level method (ADLL). The annual maximum flood series of four stations in the Weihe River basin and the Pearl River basin are taken as examples. The results show that the GAMLSS-CB model displays the best model performance compared with the QR-L and QR-CB models. Moreover, it is feasible to estimate design flood values based on the GAMLSS-CB model using the ADLL method, while the estimation of design flood based on the quantile regression model requires further studies.

scholarly journals Is Carbon Dioxide (CO2) Emission an Important Factor Affecting Healthcare Expenditure? Evidence from China, 2005–2016

2019 ◽  
Vol 16 (20) ◽  
pp. 3995 ◽  
Author(s):  
Chen ◽  
Zhuo ◽  
Xu ◽  
Xu ◽  
Gao
Keyword(s):  
Carbon Dioxide ◽  
Air Pollution ◽  
Quantile Regression ◽  
Co2 Emission ◽  
Healthcare Expenditure ◽  
Carbon Dioxide Emission ◽  
Ordinary Least Squares ◽  
Bayesian Quantile Regression ◽  
Carbon Dioxide Co2 ◽  
The Relationship

As a result of China’s economic growth, air pollution, including carbon dioxide (CO2) emission, has caused serious health problems and accompanying heavy economic burdens on healthcare. Therefore, the effect of carbon dioxide emission on healthcare expenditure (HCE) has attracted the interest of many researchers, most of which have adopted traditional empirical methods, such as ordinary least squares (OLS) or quantile regression (QR), to analyze the issue. This paper, however, attempts to introduce Bayesian quantile regression (BQR) to discuss the relationship between carbon dioxide emission and HCE, based on the longitudinal data of 30 provinces in China (2005–2016). It was found that carbon dioxide emission is, indeed, an important factor affecting healthcare expenditure in China, although its influence is not as great as the income variable. It was also revealed that the effect of carbon dioxide emission on HCE at a higher quantile was much smaller, which indicates that most people are not paying sufficient attention to the correlation between air pollution and healthcare. This study also proves the applicability of Bayesian quantile regression and its ability to offer more valuable information, as compared to traditional empirical tools, thus expanding and deepening research capabilities on the topic.

scholarly journals Friction Characteristics of Post-Tensioned Tendons of Full-Scale Structures Based on Site Tests

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haoyun Yuan ◽  
Yuan Li ◽  
Bin Zhou ◽  
Shuanhai He ◽  
Peizhi Wang
Keyword(s):  
Friction Coefficient ◽  
Quantile Regression ◽  
Least Square Method ◽  
Regression Method ◽  
Full Scale ◽  
Least Square ◽  
Friction Characteristics ◽  
Bayesian Quantile Regression ◽  
Prestressing Force ◽  
Quantile Regression Method

In the design of prestressing concrete structures, the friction characteristics between strands and channels have an important influence on the distribution of prestressing force, which can be considered comprehensively by curvature and swing friction coefficients. However, the proposed friction coefficient varies widely and may lead to an inaccurate prestress estimation. In this study, four full-scale field specimens were established to measure the friction loss of prestressing tendons with electromagnetic sensors and anchor cable dynamometers to evaluate the friction coefficient. The least square method and Bayesian quantile regression method were adopted to calculate the friction coefficient, and the results were compared with that in the specifications. Field test results showed that Bayesian quantile regression method was more effective and significant in the estimation of the friction coefficient.

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