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Appropriate model selection methods for nonstationary generalized extreme value models
Kim, Hanbeen,Kim, Sooyoung,Shin, Hongjoon,Heo, Jun-Haeng Elsevier 2017 Journal of hydrology Vol.547 No.-
<P><B>Abstract</B></P> <P>Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike’s information criterion (AIC), corrected Akaike’s information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We compared the AIC, AICc, BIC, and LRT for nonstationary GEV models. </LI> <LI> Monte Carlo simulation was conducted for evaluating the performances of all tests. </LI> <LI> Under stationary conditions, the BIC shows the best performance (N>40). </LI> <LI> Under nonstationary conditions, regression lines for model selection were proposed. </LI> <LI> The results of simulations were verified through the application of observed data. </LI> </UL> </P>
( Hanbeen Kim ),( Hyo Gun Lee ),( Youl-chang Baek ),( Seyoung Lee ),( Jakyeom Seo ) 한국축산학회 2020 한국축산학회지 Vol.62 No.1
The aim of this study was to investigate the effects of 3-nitrooxypropanol (NOP) on gas production, rumen fermentation, and animal performances depending on animal type using a meta-analysis approach. A database consisted of data from 14 studies, 18 experiments and 55 treatments. The supplementation of NOP linearly decreased methane (CH<sub>4</sub>) emissions [g/kg dry matter intake (DMI)] regardless of animal type and length of experimental period (beef, p < 0.0001, R<sup>2</sup> = 0.797; dairy, p = 0.0003, R<sup>2</sup> = 0.916; and long term, p < 0.0001, R<sup>2</sup> = 0.910). The total volatile fatty acids (VFA) concentration and the proportion of acetate, based on beef cattle database, were significantly decreased with increasing NOP supplementation (p = 0.0015, R<sup>2</sup> = 0.804 and p = 0.0003, R<sup>2</sup> = 0.918), whereas other individual VFAs was increased. Based on the dairy database, increasing levels of NOP supplementation linearly decreased proportion of acetate (p = 0.0284, R<sup>2</sup> = 0.769) and increased that of valerate (p = 0.0340, R<sup>2</sup> = 0.522), regardless of significant change on other individual VFAs. In animal performances, the DMI, from beef cattle database, tended to decrease when the levels of NOP supplementation increased (p = 0.0574, R<sup>2</sup> = 0.170), whereas there was no significant change on DMI from dairy cattle database. The NOP supplementation tended to decrease milk yield (p = 0.0606, R<sup>2</sup> = 0.381) and increase milk fat and milk protein (p = 0.0861, R<sup>2</sup> = 0.321, p = 0.0838, R<sup>2</sup> = 0.322). NOP is a viable candidate as a feed additive because of its CH<sub>4</sub> mitigation effects, regardless of animal type and experiment period, without adverse effects on animal performances.