Parametric modeling of quantile regression coefficient functions

Biometrics ◽  
2015 ◽  
Vol 72 (1) ◽  
pp. 74-84 ◽  
Author(s):  
Paolo Frumento ◽  
Matteo Bottai
Keyword(s):  
Quantile Regression ◽  
Regression Coefficient ◽  
Parametric Modeling

scholarly journals The Role of Socio-Demographic Factors in the Coverage of Breast Cancer Screening: Insights From a Quantile Regression Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Lilu Ding ◽  
Svetlana Jidkova ◽  
Marcel J. W. Greuter ◽  
Koen Van Herck ◽  
Mathieu Goossens ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Quantile Regression ◽  
Breast Cancer Screening ◽  
Regression Coefficient ◽  
Demographic Characteristics ◽  
Annual Income ◽  
Opportunistic Screening ◽  
Coverage Rates

Background: In Flanders, breast cancer (BC) screening is performed in a population-based breast cancer screening program (BCSP), as well as in an opportunistic setting. Women with different socio-demographic characteristics are not equally covered by BC screening.Objective: To evaluate the role of socio-demographic characteristics on the lowest 10th and highest 90th quantile levels of BC screening coverage.Methods: The 2017 neighborhood-level coverage rates of 8,690 neighborhoods with women aged 50–69 and eligible for BCSP and opportunistic screening were linked to socio-demographic data. The association between socio-demographic characteristics and the coverage rates of BCSP and opportunistic screening was evaluated per quantile of coverage using multivariable quantile regression models, with specific attention to the lowest 10th and highest 90th quantiles.Results: The median coverage in the BCSP was 50%, 33.5% in the 10th quantile, and 64.5% in the 90th quantile. The median coverage of the opportunistic screening was 12, 4.2, and 24.8% in the 10th and 90th quantile, respectively. A lower coverage of BCSP was found in neighborhoods with more foreign residents and larger average household size, which were considered indicators for a lower socioeconomic status (SES). However, a higher average personal annual income, which was considered an indicator for a higher SES, was also found in neighborhoods with lower coverage of BCSP. For these neighborhoods, that have a relatively low and high SES, the negative association between the percentage of foreign residents, average household size, and average personal annual income and the coverage in the BCSP had the smallest regression coefficient and 95% confidence interval (CI) values were −0.75 (95% CI: −0.85, −0.65), −13.59 (95% CI: −15.81, −11.37), and −1.05 (95% CI: −1.18, −0.92), respectively, for the 10th quantile. The neighborhoods with higher coverage of opportunistic screening had a relatively higher average personal annual income, with the largest regression coefficient of 1.72 (95% CI: 1.59, 1.85) for the 90th quantile.Conclusions: Women from relatively low and high SES neighborhoods tend to participate less in the BCSP, whereas women with a relatively high SES tend to participate more in opportunistic screening. For women from low SES neighborhoods, tailored interventions are needed to improve the coverage of BCSP.

A penalized approach to covariate selection through quantile regression coefficient models

2019 ◽  
Vol 20 (4) ◽  
pp. 369-385
Author(s):  
Gianluca Sottile ◽  
Paolo Frumento ◽  
Marcello Chiodi ◽  
Matteo Bottai
Keyword(s):  
Model Selection ◽  
Quantile Regression ◽  
Regression Model ◽  
Regression Coefficient ◽  
Regression Coefficients ◽  
Modelling Framework ◽  
Quantile Regression Model ◽  
Regression Estimators ◽  
Simulation Results ◽  
Selection Of

The coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selection is quantile-specific, our approach permits using information on all quantiles simultaneously. We describe the estimator, provide simulation results and analyse the data that motivated the present article. The proposed approach is implemented in the qrcmNP package in R.

scholarly journals Parametric modeling of quantile regression coefficient functions with count data

2021 ◽  
Author(s):  
Paolo Frumento ◽  
Nicola Salvati
Keyword(s):  
Quantile Regression ◽  
Count Data ◽  
Model Building ◽  
R Package ◽  
Parametric Modeling ◽  
Medical Expenditure ◽  
National Medical ◽  
Discrete Response ◽  
The Us ◽  
Parametric Functions

AbstractApplying quantile regression to count data presents logical and practical complications which are usually solved by artificially smoothing the discrete response variable through jittering. In this paper, we present an alternative approach in which the quantile regression coefficients are modeled by means of (flexible) parametric functions. The proposed method avoids jittering and presents numerous advantages over standard quantile regression in terms of computation, smoothness, efficiency, and ease of interpretation. Estimation is carried out by minimizing a “simultaneous” version of the loss function of ordinary quantile regression. Simulation results show that the described estimators are similar to those obtained with jittering, but are often preferable in terms of bias and efficiency. To exemplify our approach and provide guidelines for model building, we analyze data from the US National Medical Expenditure Survey. All the necessary software is implemented in the existing R package .

Pengaruh konsep diri, self efficacy dan motivasi terhadap Learning outcome mata pelajaran Aqidah Akhlak di kelas XI MA Pacet Mojokerto

2017 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Happy Ikmal
Keyword(s):  
Learning Outcomes ◽  
Significant Relationship ◽  
Self Efficacy ◽  
Teaching And Learning ◽  
Regression Coefficient ◽  
T Test ◽  
Self Concept ◽  
Working Hypothesis ◽  
Calculation Results ◽  
The Right

Teaching and learning activities is a conscious activity and aims. Therefore, for these activities can be run well and achieve the expected goals, it must be done with the strategy or the right learning approach .. The purpose of this study were: 1) to describe the influence of Self-Concept on the results of studying chemistry at Class XI MA Pacet Mojokerto. 2) Describe the effect of self-efficacy on Learning outcomes chemistry in Class XI MA Pacet Mojokerto 3) Describe the effect of motivation on Learning outcomes chemistry in Class XI MA Pacet Mojokerto 4) Describe the relationship Self-concept, self-efficacy and motivation to Results studied chemistry at Class XI MA Pacet Mojokerto. From the results of the analysis can be summarized as follows: 1) There is a significant relationship between self-concept of the Learning outcomes chemistry inquiry model. T test against self-concept variables (X1) obtained regression coefficient (B) 0.440 (44.0%), coefficient (Beta) 0.091, tcount of 0.378 with significance 0.006 t. Because of the significance of t less than 5% (0.007 <0.05), the Nil Hypothesis (H0) is rejected and working hypothesis (Hi) is received. 2) There is a significant relationship between self-efficacy toward chemistry Learning outcomes inquiry model. T test for Self-efficacy variable (X2) obtained regression coefficient (B) 0.329 (32.9%), coefficient (Beta) 0.124, tcount of 0.436 with a significance of 0.009 t. Because of the significance of t less than 5% (0.008 <0.05), the Nil Hypothesis (H0) is rejected and working hypothesis (Hi) is received. 3) There is a significant relationship between motivation to learn chemistry results inquiry model. T test for motivation variable (X3) obtained regression coefficient (B) 0.130 (13.0%), coefficient (Beta) 0.065, tcount of 0.230 with a significance of 0.001 t. Because of the significance of t less than 5% (0.001 <0.05), the Nil Hypothesis (H0) is rejected and working hypothesis (Hi) received 4) From the calculation results obtained Fhitung value 2,249 (significance F = 0.001). So Fhitung> F table (2,249> 2:03) or Sig F <5% (0.001 <0.05). It means that together independent variables consisting of variable self-concept (X1), Self-efficacy (X2), motivation (X3) simultaneously to variable results of studying chemistry (Y).

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