A non-parametric approach to non-linear causality testing

1996 ◽  
Vol 51 (1) ◽  
pp. 7-18 ◽  
Author(s):  
David Bell ◽  
Jim Kay ◽  
Jim Malley
2019 ◽  
Vol 11 (21) ◽  
pp. 2467
Author(s):  
Ogushi ◽  
Matsuoka ◽  
Defilippi ◽  
Pasquali

Persistent scatterer interferometry (PSI) is commonly applied to monitor surface displacements with millimetric precision. However, this technique still has trouble estimating non-linear displacements because the algorithm is designed for the slow and linear displacements. Additionally, there is a variety of non-linear displacement types, and finding an appropriate displacement model for PSI is still assumed to be a fairly large task. In this paper, the conventional PSI technique is extended using a non-parametric non-linear approach (NN-PSI), and the performance of the extended method is investigated by simulations and actual observation data processing with TerraSAR-X. In the simulation, non-linear displacements are modeled by the magnitudes and periods of the displacement, and the evaluation of NN-PSI is conducted. According to the simulation results, the maximum magnitude of the displacement that can be estimated by NN-PSI is two and a half times the magnitude of the SAR sensor’s wavelength (2.5λ that is roughly equivalent to 8 cm for X-band, 14 cm for C-band, and 60 cm for L-band), and the period of the displacement is about three months. However, this displacement cannot be reconstructed by the conventional PSI due to the limitation, known as the 2π displacement ambiguity. The result of the observation data processing shows that a large displacement with the 2π ambiguity can be estimated by NN-PSI as the simulation results show, but the conventional PSI cannot reconstruct it. In addition, a different approach, Small BAseline Subset (SBAS), is applied to the same data to ensure the accuracy of results, and the correlation between NN-PSI and SBAS is 0.95, while that between the conventional PSI and SBAS is –0.66. It is concluded that NN-PSI enables the reconstruction of non-linear displacements by the non-parametric approach and the expansion of applications to measure surface displacements that could not be measured due to the limitations of the traditional PSI methods.


Technometrics ◽  
1969 ◽  
Vol 11 (1) ◽  
pp. 193-196
Author(s):  
Edward P. C. Kao

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.


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