Prediction of Photolysis of PCDD/Fs Adsorbed to Spruce [Picea abies (L.) Karst.] Needle Surfaces Under Sunlight Irradiation Based on Projection Pursuit Regression

2008 ◽  
Vol 28 (2) ◽  
pp. 155-162 ◽  
Author(s):  
Yongna Yuan ◽  
Ruisheng Zhang ◽  
Rongjing Hu
Keyword(s):  
Picea Abies ◽  
Projection Pursuit ◽  
Sunlight Irradiation ◽  
Projection Pursuit Regression

scholarly journals Impact of measurement errors on the inferred stellar asteroseismic ages

2018 ◽  
Vol 620 ◽  
pp. A168 ◽  
Author(s):  
G. Valle ◽  
M. Dell’Omodarme ◽  
P. G. Prada Moroni ◽  
S. Degl’Innocenti
Keyword(s):  
Effective Temperature ◽  
Measurement Errors ◽  
Regression Models ◽  
Linear Models ◽  
Projection Pursuit ◽  
Observational Constraints ◽  
Error Source ◽  
Projection Pursuit Regression ◽  
The Impact ◽  
Age Estimates

Aims. We aim to perform a theoretical investigation on the direct impact of measurement errors in the observational constraints on the recovered age for stars in main sequence (MS) and red giant branch (RGB) phases. We assumed that a mix of classical (effective temperature Teff and metallicity [Fe/H]) and asteroseismic (Δν and νmax) constraints were available for the objects. Methods. Artificial stars were sampled from a reference isochrone and subjected to random Gaussian perturbation in their observational constraints to simulate observational errors. The ages of these synthetic objects were then recovered by means of a Monte Carlo Markov chains approach over a grid of pre-computed stellar models. To account for observational uncertainties the grid covers different values of initial helium abundance and mixing-length parameter, that act as nuisance parameters in the age estimation. Results. The obtained differences between the recovered and true ages were modelled against the errors in the observables. This procedure was performed by means of linear models and projection pursuit regression models. The first class of statistical models provides an easily generalizable result, whose robustness is checked with the second method. From linear models we find that no age error source dominates in all the evolutionary phases. Assuming typical observational uncertainties, for MS the most important error source in the reconstructed age is the effective temperature of the star. An offset of 75 K accounts for an underestimation of the stellar age from 0.4 to 0.6 Gyr for initial and terminal MS. An error of 2.5% in νmax resulted the second most important source of uncertainty accounting for about −0.3 Gyr. The 0.1 dex error in [Fe/H] resulted particularly important only at the end of the MS, producing an age error of −0.4 Gyr. For the RGB phase the dominant source of uncertainty is νmax, causing an underestimation of about 0.6 Gyr; the offset in the effective temperature and Δν caused respectively an underestimation and overestimation of 0.3 Gyr. We find that the inference from the linear model is a good proxy for that from projection pursuit regression models. Therefore, inference from linear models can be safely used thanks to its broader generalizability. Finally, we explored the impact on age estimates of adding the luminosity to the previously discussed observational constraints. To this purpose, we assumed – for computational reasons – a 2.5% error in luminosity, much lower than the average error in the Gaia DR2 catalogue. However, even in this optimistic case, the addition of the luminosity does not increase precision of age estimates. Moreover, the luminosity resulted as a major contributor to the variability in the estimated ages, accounting for an error of about −0.3 Gyr in the explored evolutionary phases.

scholarly journals Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression

2016 ◽  
Vol 20 (12) ◽  
pp. 4717-4729 ◽  
Author(s):  
Martin Durocher ◽  
Fateh Chebana ◽  
Taha B. M. J. Ouarda
Keyword(s):  
Target Location ◽  
Projection Pursuit ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Projection Pursuit Regression ◽  
Index Flood ◽  
Regional Flood Frequency Analysis ◽  
Quantile Estimates ◽  
Target Locations ◽  
Hydrological Variables

Abstract. This study investigates the utilization of hydrological information in regional flood frequency analysis (RFFA) to enforce desired properties for a group of gauged stations. Neighbourhoods are particular types of regions that are centred on target locations. A challenge for using neighbourhoods in RFFA is that hydrological information is not available at target locations and cannot be completely replaced by the available physiographical information. Instead of using the available physiographic characteristics to define the centre of a target location, this study proposes to introduce estimates of reference hydrological variables to ensure a better homogeneity. These reference variables represent nonlinear relations with the site characteristics obtained by projection pursuit regression, a nonparametric regression method. The resulting neighbourhoods are investigated in combination with commonly used regional models: the index-flood model and regression-based models. The complete approach is illustrated in a real-world case study with gauged sites from the southern part of the province of Québec, Canada, and is compared with the traditional approaches such as region of influence and canonical correlation analysis. The evaluation focuses on the neighbourhood properties as well as prediction performances, with special attention devoted to problematic stations. Results show clear improvements in neighbourhood definitions and quantile estimates.

scholarly journals Establishing grassland yield models using Projection Pursuit Regression Method

2005 ◽  
Vol 48 (1) ◽  
pp. 47-55
Author(s):  
Li Jianlong ◽  
QI Jiaguo ◽  
Zhao Dehua ◽  
Jiang Ping ◽  
Xu Sheng
Keyword(s):  
Projection Pursuit ◽  
Regression Method ◽  
Projection Pursuit Regression

Assessment of phytotoxicity grade during composting based on EEM/PARAFAC combined with projection pursuit regression

2017 ◽  
Vol 326 ◽  
pp. 10-17 ◽  
Author(s):  
Hong-Yang Cui ◽  
Yue Zhao ◽  
Yan-Ni Chen ◽  
Xu Zhang ◽  
Xue-Qin Wang ◽  
...  
Keyword(s):  
Projection Pursuit ◽  
Projection Pursuit Regression
Sign in / Sign up

Export Citation Format

Share Document