Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct ...Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct power functions to select the optimal sample size. We revise this approach when the focus is on testing a single binomial proportion. We consider exact methods and introduce a conservative criterion to account for the typical non-monotonic behavior of the power functions, when dealing with discrete data. The main purpose of this paper is to present a Shiny App providing a user-friendly, interactive tool to apply these criteria. The app also provides specific tools to elicit the analysis and the design prior distributions, which are the core of the two-priors approach.展开更多
Apps are attracting more and more attention from both mobile and web platforms. Due to the self-organized nature of the current app marketplaces, the descriptions of apps are not formally written and contain a lot of ...Apps are attracting more and more attention from both mobile and web platforms. Due to the self-organized nature of the current app marketplaces, the descriptions of apps are not formally written and contain a lot of noisy words and sentences. Thus, for most of the apps, the functions of them are not well documented and thus cannot be captured by app search engines easily. In this paper, we study the problem of inferring the real functions of an app by identifying the most informative words in its description. In order to utilize and integrate the diverse information of the app corpus in a proper way, we propose a probabilistic topic model to discover the latent data structure of the app corpus. The outputs of the topic model are further used to identify the function of an app and its most informative words. We verify the effectiveness of the proposed methods through extensive experiments on two real app datasets crawled from Google Play and Windows Phone Store, respectively.展开更多
目的为了提高校园APP服务质量,提出一种基于质量功能展开(Quality Function Deployment,QFD)的改进研究方法,其将定性定量方法相结合以保证改进方案的可信性。方法首先,基于七维度用户需求列表,使用问卷调查用户对校园APP的使用体验及期...目的为了提高校园APP服务质量,提出一种基于质量功能展开(Quality Function Deployment,QFD)的改进研究方法,其将定性定量方法相结合以保证改进方案的可信性。方法首先,基于七维度用户需求列表,使用问卷调查用户对校园APP的使用体验及期望,并对校园APP用户满意度进行了数据信度检验与验证性因子分析,以确保调查数据及用户需求模型的正确性;其次,结合用户需求改进率和熵值法构建了QFD中质量屋的规划矩阵;再次,采用文献调查法与专家意见集合法确认技术需求,并构建了质量屋的关系矩阵及技术关联矩阵;最后,计算管理需求中的技术需求重要度,完成从“用户需求—技术需求”的转换。结果从质量屋的分析中获得了可提高校园APP服务质量的多个改善建议。结论结合七维度的用户需求模型,采用QFD方法可以指导设计师对校园APP进行更合理的规划设计与改进,以提高校园APP服务质量,同时也为其他类似问题提供了解决思路。展开更多
文摘Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct power functions to select the optimal sample size. We revise this approach when the focus is on testing a single binomial proportion. We consider exact methods and introduce a conservative criterion to account for the typical non-monotonic behavior of the power functions, when dealing with discrete data. The main purpose of this paper is to present a Shiny App providing a user-friendly, interactive tool to apply these criteria. The app also provides specific tools to elicit the analysis and the design prior distributions, which are the core of the two-priors approach.
基金the Hong Kong RGC Project under Grant No. N_HKUST637/13, the National Basic Research 973 Program of China under Grant No. 2014CB340303, the National Natural Science Foundation of China under Grant Nos. 61328202 and 61502021, Microsoft Research Asia Gift Grant, Google Faculty Award 2013, and Microsoft Research Asia Fellowship 2012.
文摘Apps are attracting more and more attention from both mobile and web platforms. Due to the self-organized nature of the current app marketplaces, the descriptions of apps are not formally written and contain a lot of noisy words and sentences. Thus, for most of the apps, the functions of them are not well documented and thus cannot be captured by app search engines easily. In this paper, we study the problem of inferring the real functions of an app by identifying the most informative words in its description. In order to utilize and integrate the diverse information of the app corpus in a proper way, we propose a probabilistic topic model to discover the latent data structure of the app corpus. The outputs of the topic model are further used to identify the function of an app and its most informative words. We verify the effectiveness of the proposed methods through extensive experiments on two real app datasets crawled from Google Play and Windows Phone Store, respectively.
文摘目的为了提高校园APP服务质量,提出一种基于质量功能展开(Quality Function Deployment,QFD)的改进研究方法,其将定性定量方法相结合以保证改进方案的可信性。方法首先,基于七维度用户需求列表,使用问卷调查用户对校园APP的使用体验及期望,并对校园APP用户满意度进行了数据信度检验与验证性因子分析,以确保调查数据及用户需求模型的正确性;其次,结合用户需求改进率和熵值法构建了QFD中质量屋的规划矩阵;再次,采用文献调查法与专家意见集合法确认技术需求,并构建了质量屋的关系矩阵及技术关联矩阵;最后,计算管理需求中的技术需求重要度,完成从“用户需求—技术需求”的转换。结果从质量屋的分析中获得了可提高校园APP服务质量的多个改善建议。结论结合七维度的用户需求模型,采用QFD方法可以指导设计师对校园APP进行更合理的规划设计与改进,以提高校园APP服务质量,同时也为其他类似问题提供了解决思路。
