A Mobile Platform's In‐App Advertising Contract Under Agency Pricing for App Sales

Unlike advertising in traditional media, a mobile platform's in‐app advertising market exhibits two unique features—split structure of the mobile platform with a platform owner and an app developer jointly provisioning in‐app advertising, and agency pricing for app sales. We develop a two‐sided market model to analyze the role of these two unique features in determining the platform owner's optimal advertising revenue‐sharing contract. Our results reveal an interesting N‐shaped dynamic regarding the platform owner's optimal choice of her ad revenue share with respect to the overall advertisers’ valuation of in‐app ads. We identify a between‐agent subsidization strategy for the platform owner, where she finds it optimal to subsidize the developer via the advertising channel, leading to greater profits for both of them. We find that the advertising revenue‐sharing contract under agency pricing for app sales leads to a higher app price than would be offered by the integrated platform found in traditional advertising. However, the ad price is coordinated under the platform owner's optimal choice of ad revenue share when she obtains revenue from both the advertising and app sales channels, leading to an alignment of her interest with the app developer's on ad level.     

Non‐parametric Bayesian Intensity Model: Exploring Time‐to‐Event Data on Two Time Scales

Time‐to‐event data have been extensively studied in many areas. Although multiple time scales are often observed, commonly used methods are based on a single time scale. Analysing time‐to‐event data on two time scales can offer a more extensive insight into the phenomenon. We introduce a non‐parametric Bayesian intensity model to analyse two‐dimensional point process on Lexis diagrams. After a simple discretization of the two‐dimensional process, we model the intensity by a one‐dimensional piecewise constant hazard functions parametrized by the change points and corresponding hazard levels. Our prior distribution incorporates a built‐in smoothing feature in two dimensions. We implement posterior simulation using the reversible jump Metropolis–Hastings algorithm and demonstrate the applicability of the method using both simulated and empirical survival data. Our approach outperforms commonly applied models by borrowing strength in two dimensions.     

New asymptotic inferences about the difference,ratio, and linear combination of two independent proportions

In statistics it is customary to realize asymptotic inferences about the difference d, the ratio R or a linear combination L of two independent proportions. In this article the authors evaluate ten inference methods, and conclude that for α = 1% some of the new procedures behave better than the classical. In cases d, R, or L, the optimal method consists in adding 0.5, 0.5 or 1 to all the data, respectively, and then applying a modification of the arc-sine transformation (d or R) or the likelihood ratio test (L). A free program may be obtained at ULR http://www.ugr.es/local/bioest/Z_LINEAR_K.EXE.     

《两地书》中有一个真实的鲁迅

郜元宝认为,今天仍然弥漫着鲁迅与之搏斗的各种文字游戏;只有在文字游戏停止的地方,我们才可能与鲁迅真实相遇,才会发现,原来他是怎样的无法绕过。孙郁认为,浙东文化对鲁迅早期思想和精神底色有多方面的辐射,而鲁迅的现代眼光则颠覆了这一传统并赋予浙东文化另一种隐含。刘绪源认为,《两地书》是仅次于《野草》的、最能反映本真的鲁迅的书,那种把对虚无与黑暗的反抗作为生命本体的实践,和察见渊鱼者不祥的自省,是理解鲁迅的一把重要钥匙。袁盛勇认为,鲁迅思想及其话语实践中的正面和负面因素是难以分割地联系在一起的,应采取一种辩证的态度,回到一个复杂而完整的鲁迅那里。张钊贻则认为,鲁迅早期反民主言论有其历史原因,并非反对民主的理念,他对目前的民主制度能否真正实现确有怀疑,根源在于两种现代性的矛盾理念。     

国家祭祀与地方秩序构建中的互动——以唐宋元伏波神信仰地理为例

伏波信仰是国家祭祀与地方秩序构建互动中的代表。一方面,两伏波将军通过国家册封、地方官员倡建庙宇、士人歌颂其建功立业的英雄主题等形式,形成了神灵在中央的正统性和弘扬的主题。宋元时期,伏波信仰水神的主题不断放大,存在着一定的空间规律。伏波信仰的变化,渗透着国家祭祀的逐渐地方化,渗透着国家在地方秩序构建中,利用英雄等神灵信仰在地方的空间逐步展开和深化。     

"两课"教育教学社会实践运行模式探讨

我国高等学校开设的“两课”是系统的对大学生进行马克思主义理论教育和思想品德教育的主渠道和基本环节,社会实践是提高“两课”教学效果的重要途径和重要环节。当前,各高校存在着诸多制约“两课”社会实践运作的因素,面临着一些亟待解决的问题。针对“两课”社会实践探索中的问题,分析、比较各种不同方式社会实践的可行性,进而进行选择、总结、完善,逐步建立一套更有效可行的“两课”社会实践运作模式,这是解决目前“两课”社会实践存在问题的基本途径。     

论国民政府对交通银行的改组缘起和角色定位

在中国近代金融史上,交通银行是继大清户部银行之后的第二家官办银行,它的创设有着复杂的背景,从最初清政府特许的商业银行,到一个兼有国家银行、商业银行性质的综合性金融机构,早期对实业的发展就做出了一定贡献。当时中国旧式的金融业如票号已经开始走向衰落,钱庄也日渐式微,不能满足中国实业发展的需要,急需一个新式的专业银行来调剂市面。但由于对交通银行的定位不明确,长期以来,交行在业务经营中扮演着多重角色,未能尽全力于发展实业,显然有悖于开办银行的初衷。国民政府觊觎交通银行在金融界的实力和影响,在1928年和1935年对其进行了两次改组,将其定位于发展全国实业的银行,并成为国民政府“四行二局”金融体系的主要成员。交通银行后虽沦为官僚金融资本的垄断工具,但其专业化的形成,有利于近代实业的发展和中国经济的近代化。     

Buckley–James Type Estimator for Censored Data with Covariates Missing by Design

Abstract. The Buckley–James estimator (BJE) is a well‐known estimator for linear regression models with censored data. Ritov has generalized the BJE to a semiparametric setting and demonstrated that his class of Buckley–James type estimators is asymptotically equivalent to the class of rank‐based estimators proposed by Tsiatis. In this article, we revisit such relationship in censored data with covariates missing by design. By exploring a similar relationship between our proposed class of Buckley–James type estimating functions to the class of rank‐based estimating functions recently generalized by Nan, Kalbfleisch and Yu, we establish asymptotic properties of our proposed estimators. We also conduct numerical studies to compare asymptotic efficiencies from various estimators.     

中国融入国际人权两公约的进程与美国的对华政策

中国融入国际人权两公约包含程序性融入和规范性融入两个层次。1996年之前,美国因素虽然阻碍了中国对国际人权两公约的程序性融入,但却引发和推动了中国的规范性融入。1996年之后,随着中美关系的改善,美国因素从程序和规范两个方面都促进了中国融入的进程,推动中国签署了两公约。中国融入国际人权两公约是一个长期的过程,美国因素也将长期发挥作用,但它只是非决定性的外部因素,中国融入国际人权两公约的根本动力是国内的改革开放。美国对华人权政策的主要目的是维护美国的战略利益,中美之间在人权问题上仍然存在着合作的空间。     

Parallel Markov chain Monte Carlo for Bayesian hierarchical models with big data,in two stages

Due to the escalating growth of big data sets in recent years, new Bayesian Markov chain Monte Carlo (MCMC) parallel computing methods have been developed. These methods partition large data sets by observations into subsets. However, for Bayesian nested hierarchical models, typically only a few parameters are common for the full data set, with most parameters being group specific. Thus, parallel Bayesian MCMC methods that take into account the structure of the model and split the full data set by groups rather than by observations are a more natural approach for analysis. Here, we adapt and extend a recently introduced two-stage Bayesian hierarchical modeling approach, and we partition complete data sets by groups. In stage 1, the group-specific parameters are estimated independently in parallel. The stage 1 posteriors are used as proposal distributions in stage 2, where the target distribution is the full model. Using three-level and four-level models, we show in both simulation and real data studies that results of our method agree closely with the full data analysis, with greatly increased MCMC efficiency and greatly reduced computation times. The advantages of our method versus existing parallel MCMC computing methods are also described.