Non parametric mixture of strictly monotone regression models

2017 ◽  
Vol 47 (2) ◽  
pp. 415-426
Author(s):  
Yi Zhang ◽  
Qingle Zheng
Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 299
Author(s):  
Jaime Pinilla ◽  
Miguel Negrín

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.


2020 ◽  
Vol 69 (5) ◽  
pp. 1033-1060 ◽  
Author(s):  
Ajaya Kumar Panda ◽  
Swagatika Nanda

PurposeThe purpose of this paper is to empirically analyze the determinants of capital structure and their long-run equilibrium relationships with firm-specific and macroeconomic indicators for Indian manufacturing firms.Design/methodology/approachThe study is conducted using the panel semi-parametric and non-parametric regression models to identify the key determinants of capital structure. Panel cointegration models are also employed for analyzing the long-run equilibrium association of capital structure with its determinants.FindingsThe study finds that each manufacturing sector has unique determinants of capital structure. The debt level is significantly affected by asset tangibility, growth opportunity, effective tax rate, non-debt tax shield, cash flow, profitability, firm size, foreign investment, government borrowing, economic growth, and interest rate. All these firm-specific and macroeconomic variables have strong long-run equilibrium relationship with capital structure as a whole.Practical Implication of the StudyThe study analyzes the determinants of capital structure for eight manufacturing sectors of India, which helps firm managers and policy-makers to identify appropriate factors that maximize firm value. The sector-specific features of firms may lead to a new path with regard to corporate governance and ownership structure to enhance stakeholder's satisfaction.Originality/valueThe use of semi-parametric and non-parametric panel regression models to analyze the determinants of capital structure, and the use of panel cointegration approach to explore the long-run equilibrium relationship between the determinants and its factors are the unique contributions of the present research.


2016 ◽  
Vol 10 (8) ◽  
pp. 1825-1832 ◽  
Author(s):  
Edson Ortiz de Matos ◽  
Allan Rodrigo Arrifano Manito ◽  
Ubiratan Holanda Bezerra ◽  
Benjamim Cordeiro Costa ◽  
Thiago Mota Soares ◽  
...  

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