Book Reviews

Books reviewed:
Office International Des Epizooties (World Organization for Animal Health) World Animal Health in 1998, Part 1: Reports on the Animal Health Status and Disease Control Methods and Tables on Incidence of List A Diseases and Part 2: Tables on the Animal Health Status and Disease Control Methods
Peter Sedlmeier. Lawrence Erlbaum Associates, Mahwah Improving Statistical Reasoning: Theoretical Models and Practical Implications
Andrew Ford. Island Press Modeling the Environment. An Introduction to System Dynamics Modeling of Environmental Systems
Jacob I. Bregman. Lewis Publishers, Boca Raton Environmental Impact Statements, Second Edition     

新常态下货币供给对工业经济的非对称冲击——兼论冲击路径的情景设计与分析

货币政策冲击对工业产出和价格的非对称影响,是新常态下把握好货币供给政策的方向、力度和节奏的重要参考依据,有助于提升货币供给政策的针对性、灵活性和前瞻性。局部投影方法在不同区制下,计算工业产出和价格对货币供给冲击的脉冲响应结果表明：货币供给冲击对工业产出的影响具有不确定性,且总体上表现为中性特征;而货币供给冲击对工业价格的影响不仅在不同区制下,而且在新常态前后均表现出显著的差异性和非对称性,总体来说是短期有效,长期中性的。情景设计的分析结果显示新常态下采用增加货币供给的政策来刺激工业经济是不可取的,其作用效果可能出现工业产出停滞不前,工业价格急剧飙升的工业滞胀。因此,需要从工业产业升级,工业技术创新等工业供给侧寻求工业经济新的增长点和动力机制。     

基于有限知识和理性的双寡头战略定产纳什均衡问题研究

经典古诺寡头模型以及目前相关的寡头定产竞争模型最致命的缺陷是对寡头博弈目标的假设、时序假设、有限理性与知识的假设与现实严重不符.本文依据新的博弈条件假设,构建了一种对现实决策情形具有较强的普适性的描述性博弈结构模型.该模型能够对现实中的主导与从属型厂商之间、存在先知先觉博弈者的战略定产决策问题进行很好地描述,且经证明经典古诺博弈模型是该模型的一种特例.在此基础上,本文证明了:一般情况下,斗争策略是基于战略利益的双寡头定产竞争的纳什均衡策略,并找到了双寡头战略调整的聚点均衡;发现了:存在战略扩展阻尼条件的先期决策寡头的阻尼纳什均衡,并给出了该问题的构造性的证明和仿真算例.     

收益率分布主观模型及其实证分析

在累积展望理论基础上,通过累积概率描述主观因素对内在价值分布的影响,建立收益率分布主观模型。建立从经验收益率数据中估计投资群体的风险追求程度、投资情绪和非完全理性程度等行为变量参数的方法。利用沪市A股数据,进行χ2同分布拟合检验,结果表明:0.05水平下,沪市69.56%股票的收益率分布认为符合收益率分布主观模型,接受t分布、双正态混合分布、稳定分布和正态分布假设的股票分别为56.31%、44.80%、46.06%和1.74%。通过比较大、小公司投资群体的行为差异,以及利用收益率主观模型估计的内在价值信息参数和行为参数对截面个股收益率进行解释的实证分析,表明收益率分布主观模型及估计行为参数的方法具有合理性。     

Development and application of a dose–response model for Elizabethkingia spp

Elizabethkingia spp. are common environmental pathogens responsible for infections in more vulnerable populations. Although the exposure routes of concern are not well understood, some hospital-associated outbreaks have indicated possible waterborne transmission. In order to facilitate quantitative microbial risk assessment (QMRA) for Elizabethkingia spp., this study fit dose–response models to frog and mice datasets that evaluated intramuscular and intraperitoneal exposure to Elizabethkingia spp. The frog datasets could be pooled, and the exact beta-Poisson model was the best fitting model with optimized parameters α  = 0.52 and β = 86,351. Using the exact beta-Poisson model, the dose of Elizabethkingia miricola resulting in a 50% morbidity response (LD₅₀) was estimated to be approximately 237,000 CFU. The model developed herein was used to estimate the probability of infection for a hospital patient under a modeled exposure scenario involving a contaminated medical device and reported Elizabethkingia spp. concentrations isolated from hospital sinks after an outbreak. The median exposure dose was approximately 3 CFU/insertion event, and the corresponding median risk of infection was 3.4E-05. The median risk estimated in this case study was lower than the 3% attack rate observed in a previous outbreak, however, there are noted gaps pertaining to the possible concentrations of Elizabethkingia spp. in tap water and the most likely exposure routes. This is the first dose–response model developed for Elizabethkingia spp. thus enabling future risk assessments to help determine levels of risk and potential effective risk management strategies.     

Testing for parameter constancy in the time series direction in panel data models

We propose tests for parameter constancy in the time series direction in panel data models. We construct a locally best invariant test based on Tanaka [Time series analysis: nonstationary and noninvertible distribution theory. New York: Wiley; 1996] and an asymptotically point optimal test based on Elliott and Müller [Efficient tests for general persistent time variation in regression coefficients. Rev Econ Stud. 2006;73:907–940]. We derive the limiting distributions of the test statistics as T→∞ while N is fixed, and calculate the critical values by applying numerical integration and response surface regression. Simulation results show that the proposed tests perform well if we apply them appropriately.     

Sensitivity studies of air scattered neutron dose from particle accelerators

Sensitivity analysis of air scattered neutron dose at several distances from a particle accelerator with respect to shield thickness and neutron yield distributions has been carried out. We illustrate the successful use of Response Surface Methodology in studying the behaviour of the sensitivity coefficients for main effects and interaction terms. A comparison of the full six factor design and the orthogonal Central Composite Design has been made. The overhead shield is found to be the most sensitive parameter followed by the high energy part of the neutron energy distribution.     

Estimation of the Mean of a Sensitive Variable in the Presence of Auxiliary Information

Sousa et al. (2010 Sousa , R. , Shabbir , J. , Real , P. C. , Gupta , S. ( 2010 ). Ratio estimation of the mean of a sensitive variable in the presence of auxiliary information . J. Statist. Theor. Prac. 4 ( 3 ): 495 – 507 .[Taylor & Francis Online] , [Google Scholar]) introduced a ratio estimator for the mean of a sensitive variable and showed that this estimator performs better than the ordinary mean estimator based on a randomized response technique (RRT). In this article, we introduce a regression estimator that performs better than the ratio estimator even for modest correlation between the primary and the auxiliary variables. The underlying assumption is that the primary variable is sensitive in nature but a non sensitive auxiliary variable exists that is positively correlated with the primary variable. Expressions for the Bias and MSE (Mean Square Error) are derived based on the first order of approximation. It is shown that the proposed regression estimator performs better than the ratio estimator and the ordinary RRT mean estimator (that does not utilize the auxiliary information). We also consider a generalized regression-cum-ratio estimator that has even smaller MSE. An extensive simulation study is presented to evaluate the performances of the proposed estimators in relation to other estimators in the study. The procedure is also applied to some financial data: purchase orders (a sensitive variable) and gross turnover (a non sensitive variable) in 2009 for a population of 5,336 companies in Portugal from a survey on Information and Communication Technologies (ICT) usage.     

Robust quasi-likelihood inference in generalized linear mixed models with outliers

It is well known that in a traditional outlier-free situation, the generalized quasi-likelihood (GQL) approach [B.C. Sutradhar, On exact quasilikelihood inference in generalized linear mixed models, Sankhya: Indian J. Statist. 66 (2004), pp. 261–289] performs very well to obtain the consistent as well as the efficient estimates for the parameters involved in the generalized linear mixed models (GLMMs). In this paper, we first examine the effect of the presence of one or more outliers on the GQL estimation for the parameters in such GLMMs, especially in two important models such as count and binary mixed models. The outliers appear to cause serious biases and hence inconsistency in the estimation. As a remedy, we then propose a robust GQL (RGQL) approach in order to obtain the consistent estimates for the parameters in the GLMMs in the presence of one or more outliers. An extensive simulation study is conducted to examine the consistency performance of the proposed RGQL approach.     

Pseudo-Bayes and pseudo-empirical Bayes estimators in randomized response sampling

In this article, new pseudo-Bayes and pseudo-empirical Bayes estimators for estimating the proportion of a potentially sensitive attribute in a survey sampling have been introduced. The proposed estimators are compared with the recent estimator proposed by Odumade and Singh [Efficient use of two decks of cards in randomized response sampling, Comm. Statist. Theory Methods 38 (2009), pp. 439–446] and Warner [Randomized response: A survey technique for eliminating evasive answer bias, J. Amer. Statist. Assoc. 60 (1965), pp. 63–69].