Detection of fruit traits by using near-infrared(NIR)spectroscopy may encounter out-of-distribution samples that exceed the generalization ability of a constructed calibration model.Therefore,confidence analysis for a...Detection of fruit traits by using near-infrared(NIR)spectroscopy may encounter out-of-distribution samples that exceed the generalization ability of a constructed calibration model.Therefore,confidence analysis for a given prediction is required,but this cannot be done using common calibration models of NIR spectroscopy.To address this issue,this paper studied the Gaussian process regression(GPR)for fruit traits detection using NIR spectroscopy.The mean and variance of the GPR were used as the predicted value and confidence,respectively.To show this,a real NIR data set related to dry matter content measurements in mango was used.Compared to partial least squares regression(PLSR),GPR showed approximately 14%lower root mean squared error(RMSE)for the in-distribution test set.Compared with no confidence analysis,using the variance of GPR to remove abnormal samples made GPR and PLSR showed approximately 58%and 10%lower RMSE on the mixed distribution test set,respectively(when the type 1 error rate was set to 0.1).Compared with traditional one-class classification methods,the variance of the GPR can be used to effectively eliminate poorly predicted samples.展开更多
An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only smal...An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.展开更多
In cancer survival analysis, it is very frequently to estimate the confidence intervals for survival probabilities.But this calculation is not commonly involve in most popular computer packages, or only one methods of...In cancer survival analysis, it is very frequently to estimate the confidence intervals for survival probabilities.But this calculation is not commonly involve in most popular computer packages, or only one methods of estimation in the packages. In the present Paper, we will describe a microcomputer Program for estimating the confidence intervals of survival probabilities, when the survival functions are estimated using Kaplan-Meier product-limit or life-table method. There are five methods of estimation in the program (SPCI), which are the classical(based on Greenwood's formula of variance of S(ti), Rothman-Wilson, arcsin transformation, log(-Iog) transformation, Iogit transformation methods. Two example analysis are given for testing the performances of the program running.展开更多
To better understand the current situation of China textile industry and the enterprisesdemand,China Textile Entrepreneur Association joined hands with China Textile StatisticsCenter to make a questionaire survey on t...To better understand the current situation of China textile industry and the enterprisesdemand,China Textile Entrepreneur Association joined hands with China Textile StatisticsCenter to make a questionaire survey on the executives of textile mills and manufacturers atthe end of Dec.2008.More than 100 questionaries were returned by the end of Jan.2009.123 of them were reviewed and analyzed.Here below are the key points:展开更多
基金the National Natural Science Foundation of China(62105245)the Zhejiang Natural Science Foundation of China(LQ20F030059,and LY21C200001)the Wenzhou Science and Technology Bureau General Project(S2020011),China.
文摘Detection of fruit traits by using near-infrared(NIR)spectroscopy may encounter out-of-distribution samples that exceed the generalization ability of a constructed calibration model.Therefore,confidence analysis for a given prediction is required,but this cannot be done using common calibration models of NIR spectroscopy.To address this issue,this paper studied the Gaussian process regression(GPR)for fruit traits detection using NIR spectroscopy.The mean and variance of the GPR were used as the predicted value and confidence,respectively.To show this,a real NIR data set related to dry matter content measurements in mango was used.Compared to partial least squares regression(PLSR),GPR showed approximately 14%lower root mean squared error(RMSE)for the in-distribution test set.Compared with no confidence analysis,using the variance of GPR to remove abnormal samples made GPR and PLSR showed approximately 58%and 10%lower RMSE on the mixed distribution test set,respectively(when the type 1 error rate was set to 0.1).Compared with traditional one-class classification methods,the variance of the GPR can be used to effectively eliminate poorly predicted samples.
基金Funding of Jiangsu Innovation Program for Graduate Education (CXZZ11_0193)NUAA Research Funding (NJ2010009)
文摘An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.
文摘In cancer survival analysis, it is very frequently to estimate the confidence intervals for survival probabilities.But this calculation is not commonly involve in most popular computer packages, or only one methods of estimation in the packages. In the present Paper, we will describe a microcomputer Program for estimating the confidence intervals of survival probabilities, when the survival functions are estimated using Kaplan-Meier product-limit or life-table method. There are five methods of estimation in the program (SPCI), which are the classical(based on Greenwood's formula of variance of S(ti), Rothman-Wilson, arcsin transformation, log(-Iog) transformation, Iogit transformation methods. Two example analysis are given for testing the performances of the program running.
文摘To better understand the current situation of China textile industry and the enterprisesdemand,China Textile Entrepreneur Association joined hands with China Textile StatisticsCenter to make a questionaire survey on the executives of textile mills and manufacturers atthe end of Dec.2008.More than 100 questionaries were returned by the end of Jan.2009.123 of them were reviewed and analyzed.Here below are the key points:
