南京中山植物园游憩功能满意度评价研究

随着植物园"旅游经济"的发展,植物园在承担植物科学研究及科普功能的同时,也成为公众日常休闲的重要绿地。本文选取南京中山植物园作为研究对象,建立评价体系并运用模糊综合评价法对游客满意度进行研究,探寻影响植物园游憩功能的显著因子,为未来植物园规划设计提供参考。结果表明:游客对南京中山植物园的游憩功能达到基本满意水平。其中,植物物种多样性、景区分布、建筑景观、声景观的满意度较高,滨水景观的满意度评价值较低。通过Pearson相关系数发现植物色彩丰富度、科普场所、科普内容是影响游客满意度的显著因子。运用IPA图对16项评价因子进行重要度-满意度分析,并对各象限内的因子提出改进方向。最后基于游憩功能满意度评价结果,提出改进策略:加大对植物园休息设施、卫生设施、娱乐设施和商业服务设施的投入;更新科普方式,增添特色游憩主题园区;完善现有道路体系,增强对北部专类园的可达性;增加色叶植物的种类、提高色彩丰富度等。以期为中山植物园游憩功能的优化及其他植物园的规划设计提供借鉴。     

针对生物识别信息的刑法保护:现实境遇与完善路径——以四川“人脸识别案”为切入点

当前生物识别信息在我国社会中的运用呈现逐年递增的趋势.生物识别信息具备本体特殊性和社会特殊性,这决定了其具备与普通公民个人信息不同的重要性,应受到刑法的特殊保护,但我国既定刑事立法对生物识别信息并未进行任何形式的特殊保护.可运用实质解释的方法,在不违反罪刑法定原则的前提下,充分利用两高《解释》第5条中第1款第10项和第2款第4项这两个兜底条款,将"侵犯生物识别信息5条及以上"认定为"情节严重",将"侵犯生物识别信息50条及以上"认定为"情节特别严重",由此降低针对生物识别信息原本的入罪和法定刑升格的数量,最终实现对生物识别信息的特殊刑法保护.     

货币政策反应函数渠道对金融周期影响时变机制的实证研究

在关于货币政策影响经济主体风险承担水平,进而影响金融周期波动机制的研究中,基于风险承担渠道的相关研究较为成熟.区别于以往相关研究多关注货币政策实际采取的立场,文章基于货币政策反应函数渠道探讨了数量型与价格型货币政策反应函数对金融周期波动影响的时变机制.滚动回归的实证结果显示:无论数量型货币政策规则还是价格型货币政策规则,货币政策对信贷波动反应的敏感性主要影响金融周期的波动,但在价格型货币政策规则下,基于信贷视角观察金融周期波动时,货币政策信贷敏感性与货币政策资产价格敏感性对金融周期影响差异较小;较之于价格型货币政策规则,货币政策对信贷波动反应的敏感性在数量型货币政策规则下,对金融周期波动的影响更显著,并在一定程度上表现出随时间扩大的趋势.文章的创新之处在于:强调了货币政策通过政策反应函数渠道而非以往研究中较多关注的狭义风险承担渠道影响金融周期波动的事实,并构建计量模型对货币政策反应函数渠道影响金融周期波动的时变机制进行了详细刻画.     

走出离合词现象的迷局——从准定语与准名词现象入手

词的离合现象是语法研究中的老大难问题。文章独辟蹊径,从与词的离析使用密切相关的准定语和准名词入手,根据句子的信息结构原则,解释了词的离合现象的成因。文章指出,话语的信息结构安排是促成句法结构重组的语用推力:话题语的指称化处置诱发了准定语结构的操作,基本不涉及词的离析;评述语受单一新信息原则制约而出现的事件化处置,迫使及物性低的双音词强行拆分以容纳事件化所需要素,是诱发词的离析并催生准定语和准名词的直接动因。文章的观点是,用朴素的眼光看汉语,汉语中并无什么离合词,词的离合只是非形态语中双音词的用法变异,是语用型语言句子信息结构进行整编与重组的副产品。     

人口流动导引的家庭代价及发展省思—基于陇东南 Q 村的个案考察

以陇东南 Q 村为个案,在分析农村人口流动动因的基础上,系统梳理了人口流动导引的一系列家庭代价。 家庭功能遭致消解性代价方面,表现为儿童青少年的教育抚养和老年人养老照料功能弱化;家庭风险多发频发性代价方面,表现为婚姻和家庭稳定性下降、青少年越轨行为突出和家庭成员安全风险增大;家庭分化或衰落性代价方面,表现为空巢家庭、隔代家庭和独身家庭大量出现,家庭的空壳化和复杂分化成为部分家庭走向衰落的前兆。 代价视角的分析表明,需要将家庭置于相关制度设置和公共政策讨论的核心,明确家庭建设在中国农村社会具有的特殊的文化价值和现实意义。 发展和守护家庭,调动家庭自我保障、自我调节和服务的功能及积极性,是政府相关政策制定中必须予以重视的关键议题。     

Accurate computation of single and product moments of order statistics under progressive censoring

An accurate procedure is proposed to calculate approximate moments of progressive order statistics in the context of statistical inference for lifetime models. The study analyses the performance of power series expansion to approximate the moments for location and scale distributions with high precision and smaller deviations with respect to the exact values. A comparative analysis between exact and approximate methods is shown using some tables and figures. The different approximations are applied in two situations. First, we consider the problem of computing the large sample variance–covariance matrix of maximum likelihood estimators. We also use the approximations to obtain progressively censored sampling plans for log-normal distributed data. These problems illustrate that the presented procedure is highly useful to compute the moments with precision for numerous censoring patterns and, in many cases, is the only valid method because the exact calculation may not be applicable.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

On the estimation of non linear functions in stochastic volatility models

Abstract

This paper focuses on the inference of suitable generally non linear functions in stochastic volatility models. In this context, in order to estimate the variance of the proposed estimators, a moving block bootstrap (MBB) approach is suggested and discussed. Under mild assumptions, we show that the MBB procedure is weakly consistent. Moreover, a methodology to choose the optimal length block in the MBB is proposed. Some examples and simulations on the model are also made to show the performance of the proposed procedure.     

Kernel naive Bayes discrimination for high‐dimensional pattern recognition

Kernel discriminant analysis translates the original classification problem into feature space and solves the problem with dimension and sample size interchanged. In high‐dimension low sample size (HDLSS) settings, this reduces the ‘dimension’ to that of the sample size. For HDLSS two‐class problems we modify Mika's kernel Fisher discriminant function which – in general – remains ill‐posed even in a kernel setting; see Mika et al. (1999). We propose a kernel naive Bayes discriminant function and its smoothed version, using first‐ and second‐degree polynomial kernels. For fixed sample size and increasing dimension, we present asymptotic expressions for the kernel discriminant functions, discriminant directions and for the error probability of our kernel discriminant functions. The theoretical calculations are complemented by simulations which show the convergence of the estimators to the population quantities as the dimension grows. We illustrate the performance of the new discriminant rules, which are easy to implement, on real HDLSS data. For such data, our results clearly demonstrate the superior performance of the new discriminant rules, and especially their smoothed versions, over Mika's kernel Fisher version, and typically also over the commonly used naive Bayes discriminant rule.     

医疗信息共享对患者转移数量和服务质量水平影响研究

当前我国医院间存在严重的"信息孤岛"，医院参与医疗信息共享意愿不高，患者的诊疗信息被静态碎片化储存而无法充分有效地利用。考虑到医院进行医疗信息共享将降低患者转移成本，本文构建一个多阶段双寡头动态博弈模型研究医疗信息共享对医院竞争过程中患者转移数量和服务质量水平决策的影响。首先，根据是否存在转移成本，将患者分为新患者和经验性患者，借助Hotelling模型刻画患者的效用函数，分析患者就诊决策。然后，在政府价格规制和不考虑医院利他性情景下，构建了医院累积期望收益目标函数，使用动态规划方法，求解实现医院累积期望收益最大化的服务质量水平，获得了实现患者相互转移且医院在市场中共存的马尔可夫完美均衡。最后，根据医院参与医疗信息共享后患者转移成本降为零，分析与比较信息共享前后患者转移数量和服务质量水平变化。研究发现：在不同医院间本身存在患者转移背景下，医院参与信息共享后，患者转移数量增加但存在一个上限，增加的转移量与患者在医院间的转移成本呈正相关，与初始感知效用的差值范围呈负相关；医院参与信息共享后，均衡状态下的医院服务质量水平高于信息共享前的服务质量水平。因此，在不改变当前医保支付方式下，要加快推进医疗信息共享，政府部门可以根据医院的患者数量和服务质量水平变化对其进行定期补贴，以激励医院积极参与医疗信息共享，本文给出了这个补贴的量化表达。