The NI （non-inferiority） trial design based on the likelihood ratio test eliminates the dependency on the conventional NI margin, and it explicitly uses the MCID （minimum clinical important difference） that links ...The NI （non-inferiority） trial design based on the likelihood ratio test eliminates the dependency on the conventional NI margin, and it explicitly uses the MCID （minimum clinical important difference） that links the statistical analysis to the clinical sense. Different from the conventional trial design, the new methodology is self-adaptive to the change in the sample size and overall cure rate, and it has an asymptotic property. It is shown that MCID is de-composite into constant MCID and statistical MCID. Along with this concept, the concept of the allowed inferiority does not exist, the interpretation of the trial result is more accurate and consistent to the statistical theory as well as the clinical interpretations.展开更多
The International Accounting Standards Board （IASB） and the Financial Accounting Standards Board （FASB） stress the importance of high-quality ＇financial reports. From a scientific point of view, however, major me...The International Accounting Standards Board （IASB） and the Financial Accounting Standards Board （FASB） stress the importance of high-quality ＇financial reports. From a scientific point of view, however, major methodological drawbacks can arise when trying to arrive at a comprehensive assessment and evaluation of the decision usefulness of financial reports. In this conceptually-based exploratory study, the authors construct a 33-item index aimed at operationalizing decision usefulness in terms of the fundamental and enhancing qualitative characteristics laid out in the conceptual framework （CF） of the IASB （2010）. Using a matched-pairs sample design, which includes 70 UK annual reports and 70 US 10-K reports for 2010, the results of test-retest and inter-rater reliability tests show that these multiple items, which were based on items used in previous research, can be measured in a reliable manner. At the same time, the results of an exploratory factor analysis indicate that the IASB qualitative characteristics cannot be measured separately when the 33-item index is applied. At an aggregate level, the results of paired-sample t-tests reveal that UK reports score on average higher than US 10-K reports, which suggests that the overall quality of UK reports is better. The findings of this study add to the existing literature on the empirical evaluation of the effects of international accounting standards, showing that, as compared with 10-K reports, UK annual reports provide more information on topics such as corporate social responsibility （CSR）, corporate governance, and annual bonus schemes. On the other hand, US reports outperform UK reports with respect to the content of fair value information, cash flow statements, off-balance financing, and audit reporting.展开更多
We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from th...We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.展开更多
文摘The NI （non-inferiority） trial design based on the likelihood ratio test eliminates the dependency on the conventional NI margin, and it explicitly uses the MCID （minimum clinical important difference） that links the statistical analysis to the clinical sense. Different from the conventional trial design, the new methodology is self-adaptive to the change in the sample size and overall cure rate, and it has an asymptotic property. It is shown that MCID is de-composite into constant MCID and statistical MCID. Along with this concept, the concept of the allowed inferiority does not exist, the interpretation of the trial result is more accurate and consistent to the statistical theory as well as the clinical interpretations.
文摘The International Accounting Standards Board （IASB） and the Financial Accounting Standards Board （FASB） stress the importance of high-quality ＇financial reports. From a scientific point of view, however, major methodological drawbacks can arise when trying to arrive at a comprehensive assessment and evaluation of the decision usefulness of financial reports. In this conceptually-based exploratory study, the authors construct a 33-item index aimed at operationalizing decision usefulness in terms of the fundamental and enhancing qualitative characteristics laid out in the conceptual framework （CF） of the IASB （2010）. Using a matched-pairs sample design, which includes 70 UK annual reports and 70 US 10-K reports for 2010, the results of test-retest and inter-rater reliability tests show that these multiple items, which were based on items used in previous research, can be measured in a reliable manner. At the same time, the results of an exploratory factor analysis indicate that the IASB qualitative characteristics cannot be measured separately when the 33-item index is applied. At an aggregate level, the results of paired-sample t-tests reveal that UK reports score on average higher than US 10-K reports, which suggests that the overall quality of UK reports is better. The findings of this study add to the existing literature on the empirical evaluation of the effects of international accounting standards, showing that, as compared with 10-K reports, UK annual reports provide more information on topics such as corporate social responsibility （CSR）, corporate governance, and annual bonus schemes. On the other hand, US reports outperform UK reports with respect to the content of fair value information, cash flow statements, off-balance financing, and audit reporting.
基金partially supported by the National High Technology Program(2013AA122804)the Special Fund for Meteorology Scientific Research in the Public Welfare(GYHY201506023)of ChinaOpen Fund of State Key Laboratory of Remote Sensing Science(OFSLRSS201514)
文摘We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.
