The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of paralle...The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.展开更多
The choice of weights in frequentist model average estimators is an important but difficult problem. Liang et al. (2011) suggested a criterion for the choice of weight under a general parametric framework which is ter...The choice of weights in frequentist model average estimators is an important but difficult problem. Liang et al. (2011) suggested a criterion for the choice of weight under a general parametric framework which is termed as the generalized OPT (GOPT) criterion in the present paper. However, no properties and applications of the criterion have been studied. This paper is devoted to the further investigation of the GOPT criterion. We show that how to use this criterion for comparison of some existing weights such as the smoothed AIC-based and BIC-based weights and for the choice between model averaging and model selection. Its connection to the Mallows and ordinary OPT criteria is built. The asymptotic optimality on the criterion in the case of non-random weights is also obtained. Finite sample performance of the GOPT criterion is assessed by simulations. Application to the analysis of two real data sets is presented as well.展开更多
Richardson et al. （Sci Bull, 2015. doi：10.1007/ sl1434-015-0806-z） suggest that the irreducibly simple climate model described in Monckton of Brenchley et al. （Sci Bull 60：122-135, 2015. doi：10.1007/s11434-014- ...Richardson et al. （Sci Bull, 2015. doi：10.1007/ sl1434-015-0806-z） suggest that the irreducibly simple climate model described in Monckton of Brenchley et al. （Sci Bull 60：122-135, 2015. doi：10.1007/s11434-014- 0699-2） was not validated against observations, relying instead on synthetic test data based on underestimated global warming, illogical parameter choice and near-in- stantaneous response at odds with ocean warming and other observations. However, the simple model, informed by its authors＇ choice of parameters, usually hindcasts observed temperature change more closely than the general-circu- lation models, and finds high climate sensitivity implausi- ble. With IPCC＇s choice of parameters, the model is further validated in that it duly replicates IPCC＇s sensitivity interval. Also, fast climate system response is consistent with near-zero or net-negative temperature feedback. Given the large uncertainties in the initial conditions and evolutionary processes determinative of climate sensitivity, subject to obvious caveats a simple sensitivity-focused model need not, and the present model does not, exhibit significantly less predictive skill than the general-circula- tion models.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60873235&60473099)the Science-Technology Development Key Project of Jilin Province of China (No. 20080318)the Program of New Century Excellent Talents in University of China (No. NCET-06-0300).
文摘The theory of nu-support vector regression (Nu-SVR) is employed in modeling time series variationfor prediction. In order to avoid prediction performance degradation caused by improper parameters, themethod of parallel multidimensional step search (PMSS) is proposed for users to select best parameters intraining support vector machine to get a prediction model. A series of tests are performed to evaluate themodeling mechanism and prediction results indicate that Nu-SVR models can reflect the variation tendencyof time series with low prediction error on both familiar and unfamiliar data. Statistical analysis is alsoemployed to verify the optimization performance of PMSS algorithm and comparative results indicate thattraining error can take the minimum over the interval around planar data point corresponding to selectedparameters. Moreover, the introduction of parallelization can remarkably speed up the optimizing procedure.
基金supported by National Natural Science Foundation of China (Grant Nos.71101141, 70933003, 11228103, and 11271355)the Hundred Talents Program of the Chinese Academy of SciencesNational Science Foundation of United States (Grant No. DMS-1007167)
文摘The choice of weights in frequentist model average estimators is an important but difficult problem. Liang et al. (2011) suggested a criterion for the choice of weight under a general parametric framework which is termed as the generalized OPT (GOPT) criterion in the present paper. However, no properties and applications of the criterion have been studied. This paper is devoted to the further investigation of the GOPT criterion. We show that how to use this criterion for comparison of some existing weights such as the smoothed AIC-based and BIC-based weights and for the choice between model averaging and model selection. Its connection to the Mallows and ordinary OPT criteria is built. The asymptotic optimality on the criterion in the case of non-random weights is also obtained. Finite sample performance of the GOPT criterion is assessed by simulations. Application to the analysis of two real data sets is presented as well.
文摘Richardson et al. （Sci Bull, 2015. doi：10.1007/ sl1434-015-0806-z） suggest that the irreducibly simple climate model described in Monckton of Brenchley et al. （Sci Bull 60：122-135, 2015. doi：10.1007/s11434-014- 0699-2） was not validated against observations, relying instead on synthetic test data based on underestimated global warming, illogical parameter choice and near-in- stantaneous response at odds with ocean warming and other observations. However, the simple model, informed by its authors＇ choice of parameters, usually hindcasts observed temperature change more closely than the general-circu- lation models, and finds high climate sensitivity implausi- ble. With IPCC＇s choice of parameters, the model is further validated in that it duly replicates IPCC＇s sensitivity interval. Also, fast climate system response is consistent with near-zero or net-negative temperature feedback. Given the large uncertainties in the initial conditions and evolutionary processes determinative of climate sensitivity, subject to obvious caveats a simple sensitivity-focused model need not, and the present model does not, exhibit significantly less predictive skill than the general-circula- tion models.
