In this paper we report an analysis of sampling error uncertainties in mean maximum and minimum temperatures (Tmax and Tmin) carried out on monthly,seasonal and annual scales,including an examination of homogenized ...In this paper we report an analysis of sampling error uncertainties in mean maximum and minimum temperatures (Tmax and Tmin) carried out on monthly,seasonal and annual scales,including an examination of homogenized and original data collected at 731 meteorological stations across China for the period 1951-2004.Uncertainties of the gridded data and national average,linear trends and their uncertainties,as well as the homogenization effect on uncertainties are assessed.It is shown that the sampling error variances of homogenized Tmax and Tmin,which are larger in winter than in summer,have a marked northwest-southeast gradient distribution,while the sampling error variances of the original data are found to be larger and irregular.Tmax and Tmin increase in all months of the year in the study period 1951-2004,with the largest warming and uncertainties being 0.400℃ (10 yr)-1 + 0.269℃ (10 yr)-1 and 0.578℃ (10 yr)-1 + 0.211℃ (10 yr)-1 in February,and the least being 0.022℃ (10 yr)-1 + 0.085℃ (10 yr)-1 and 0.104℃ (10 yr)-1 +0.070℃ (10 yr)-1 in August.Homogenization can remove large uncertainties in the original records resulting from various non-natural changes in China.展开更多
Non-sampling errors can generally be divided into three types:sampling frame errors,non-response errors and measurement errors.Missing target units in the sam-pling frame,improper handling of non-responses,and misrepo...Non-sampling errors can generally be divided into three types:sampling frame errors,non-response errors and measurement errors.Missing target units in the sam-pling frame,improper handling of non-responses,and misreporting or underreport-ing of key variables in the questionnaire can all cause deviations in a survey’s results.The widespread application of Computer-Assisted Personal Interviewing(CAPI)systems and the inclusion of administrative records from government sources in sur-veys has strengthened the ability to control non-sampling errors.Taking a national fertility sampling survey as an example,this study summarizes the sources of var-ious non-sampling errors and explains how to harness big data resources such as administrative records to control non-sampling errors throughout the survey.The study analyzes the impact of three types of non-sampling errors on the results of the fertility survey and examines the strategies used to address the problems caused by these non-sampling errors.The findings indicate that non-sampling errors were the main source of total error in the survey,and that the errors found came mainly from sampling frame errors;non-response errors and measurement errors were controlled and had little impact on the survey results.展开更多
The principle of planc-to-plane perpendicularity measuring with coordinate measuring machine (CMM) is described and the main factors that influence the measuring precision are analyzed. The minimum condition method ...The principle of planc-to-plane perpendicularity measuring with coordinate measuring machine (CMM) is described and the main factors that influence the measuring precision are analyzed. The minimum condition method is adopted to eliminate the fitting error of the datum plane. In order to diminish the length error of the object plane, the tactics of measuring some part of the plane and then scale to the whole plane is employed. With large quantity of measuring experiments on fiat plates, the most appropriate number of points in measuring a plane is determined to reduce the sampling error.展开更多
Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling desi...Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems: i) different population vaxiograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.展开更多
One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the...One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the speed and scalability of the association rulemining is to do the algorithm on a random sample instead of the entire database. But how toeffectively define and efficiently estimate the degree of error with respect to the outcome of thealgorithm, and how to determine the sample size needed are entangling researches until now. In thispaper, an effective and efficient algorithm is given based on the PAC (Probably Approximate Correct)learning theory to measure and estimate sample error. Then, a new adaptive, on-line, fast samplingstrategy - multi-scaling sampling - is presented inspired by MRA (Multi-Resolution Analysis) andShannon sampling theorem, for quickly obtaining acceptably approximate association rules atappropriate sample size. Both theoretical analysis and empirical study have showed that the Samplingstrategy can achieve a very good speed-accuracy trade-off.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 41130103)the 973 Program (Grant Nos. 2009CB421406 and 2012CB955401)+1 种基金the US National Oceanographic and Atmospheric Administration (Grant No. EL133E09SE4048)the US National Science Foundation (Grant Nos. AGS-1015926 and AGS-1015957)
文摘In this paper we report an analysis of sampling error uncertainties in mean maximum and minimum temperatures (Tmax and Tmin) carried out on monthly,seasonal and annual scales,including an examination of homogenized and original data collected at 731 meteorological stations across China for the period 1951-2004.Uncertainties of the gridded data and national average,linear trends and their uncertainties,as well as the homogenization effect on uncertainties are assessed.It is shown that the sampling error variances of homogenized Tmax and Tmin,which are larger in winter than in summer,have a marked northwest-southeast gradient distribution,while the sampling error variances of the original data are found to be larger and irregular.Tmax and Tmin increase in all months of the year in the study period 1951-2004,with the largest warming and uncertainties being 0.400℃ (10 yr)-1 + 0.269℃ (10 yr)-1 and 0.578℃ (10 yr)-1 + 0.211℃ (10 yr)-1 in February,and the least being 0.022℃ (10 yr)-1 + 0.085℃ (10 yr)-1 and 0.104℃ (10 yr)-1 +0.070℃ (10 yr)-1 in August.Homogenization can remove large uncertainties in the original records resulting from various non-natural changes in China.
基金sponsored by the Follow-up Research on Fertility Level and Fertility Intentions with the Help of Big Data(No.21BRK001)a research project funded by the National Social Science Fund of China.
文摘Non-sampling errors can generally be divided into three types:sampling frame errors,non-response errors and measurement errors.Missing target units in the sam-pling frame,improper handling of non-responses,and misreporting or underreport-ing of key variables in the questionnaire can all cause deviations in a survey’s results.The widespread application of Computer-Assisted Personal Interviewing(CAPI)systems and the inclusion of administrative records from government sources in sur-veys has strengthened the ability to control non-sampling errors.Taking a national fertility sampling survey as an example,this study summarizes the sources of var-ious non-sampling errors and explains how to harness big data resources such as administrative records to control non-sampling errors throughout the survey.The study analyzes the impact of three types of non-sampling errors on the results of the fertility survey and examines the strategies used to address the problems caused by these non-sampling errors.The findings indicate that non-sampling errors were the main source of total error in the survey,and that the errors found came mainly from sampling frame errors;non-response errors and measurement errors were controlled and had little impact on the survey results.
基金sponsored by the Special Research Fund for Young Teachers of Universities in Shanghai under Grant No.gjd-07048
文摘The principle of planc-to-plane perpendicularity measuring with coordinate measuring machine (CMM) is described and the main factors that influence the measuring precision are analyzed. The minimum condition method is adopted to eliminate the fitting error of the datum plane. In order to diminish the length error of the object plane, the tactics of measuring some part of the plane and then scale to the whole plane is employed. With large quantity of measuring experiments on fiat plates, the most appropriate number of points in measuring a plane is determined to reduce the sampling error.
基金?nancially supported by the National Natural Science Foundation of China (Nos. 41541006 and 41771246)co-funded by Enterprise Ireland and the European Regional Development Fund (ERDF) under the National Strategic Reference Framework (NSRF) 2007–2013
文摘Sampling plays an important role in acquiring precise soil information required in modern agricultural production worldwide, which determines both the cost and quality of final soil mapping products. For sampling design, it has been proposed possibile to transfer the relationships between kriging variance and sampling grid spacing from an area with existing information to other areas with similar soil-forming environments. However, this approach is challenged in practice because of two problems: i) different population vaxiograms among similar areas and ii) sampling errors in estimated variograms. This study evaluated the effects of these two problems on the transferability of the relationships between kriging variance and sampling grid spacing, by using spatial data simulated with three variograms and soil samples collected from four grasslands in Ireland with similar soil-forming environments. Results showed that the variograms suggested by different samples collected with the same grid spacing in the same or similar areas were different, leading to a range of mean kriging variance (MKV) for each grid spacing. With increasing grid spacing, the variation of MKV for a specific grid spacing increased and deviated more from the MKV generated using the population variograms. As a result, the spatial transferability of the relationships between kriging variance and grid spacing for sampling design was limited.
基金CAS Project of Brain and Mind Science,国家高技术研究发展计划(863计划),国家重点基础研究发展计划(973计划),国家自然科学基金,湖南省自然科学基金
文摘One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the speed and scalability of the association rulemining is to do the algorithm on a random sample instead of the entire database. But how toeffectively define and efficiently estimate the degree of error with respect to the outcome of thealgorithm, and how to determine the sample size needed are entangling researches until now. In thispaper, an effective and efficient algorithm is given based on the PAC (Probably Approximate Correct)learning theory to measure and estimate sample error. Then, a new adaptive, on-line, fast samplingstrategy - multi-scaling sampling - is presented inspired by MRA (Multi-Resolution Analysis) andShannon sampling theorem, for quickly obtaining acceptably approximate association rules atappropriate sample size. Both theoretical analysis and empirical study have showed that the Samplingstrategy can achieve a very good speed-accuracy trade-off.