集聚效应、集聚效率与城市规模分布变化

摘　　要:本文从城市集聚效应的视角,分析我国城市规模分布的结构性变化的机制,在此基础上构建了包含集聚效应的城市总量生产函数的计量经济学模型,然后应用OLS方法估算我国不同规模等级城市的平均集聚效应指数和平均集聚效率指数．并对比分析其对城市规模分布变化的影响。本文的实证结果表明,平均集聚效率指数的高低与城市规模分布比重的增减相一致。     

我国生产用能源消费变动的分解分析

摘　　要:能源消费分解是探讨能源消费变动影响因素的一种常用方法。在本文中介绍了目前研究中较为合理的一种分解方法——对数平均D氏指数法,并借助这一方法来对我国“六五”时期以来的生产用能源消费情况进行分解分析,探讨产业结构变动和产业内效率提高对能源消费和总体单位能耗的影响。根据测算结果,笔者认为产业内能源效率的提高是我国能源节约的主要因素;进一步建议政府构建“能源分解指数体系”作为制定能源政策的依据。     

高等教育投资回报率估算：基于西部三个城市的微观数据

本文基于中国西部三个城市,7949个住户的微观数据,利用内生的处理效应模型测算了城市家庭20-35岁青年的高等教育投资回报率。经验研究的结果显示：1. 现阶段青年人的高等教育投资内部报酬率大致在7%左右;按照30年工作时间计算的收入增加现值大约在8万元上下,高等教育使得月收入期望值平均增加80%。高等教育还能够使青年就业的可能性增加8%左右;2. 从性别的视角观察,高等教育对于女性的回报率高于男性,女青年内部报酬率大约在8.3%（男青年7.6%）;就业概率增加15.9%（男青年4%）,每月工资期望值增加122%（男青年67.5%）;3. 如果高等教育的全部花费增加到平均6万元,则从单纯的现金流意义上说,高等教育没有增加收入的价值了。     

New imperfect rankings models for ranked set sampling

Ranked set sampling is a sampling approach that leads to improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. This ranking may be done either by subjective judgment or according to an auxiliary variable, and it need not be completely accurate. In fact, results in the literature have shown that no matter how poor the quality of the ranking, procedures based on ranked set sampling tend to be at least as efficient as procedures based on simple random sampling. However, efforts to quantify the gains in efficiency for ranked set sampling procedures have been hampered by a shortage of available models for imperfect rankings. In this paper, we introduce a new class of models for imperfect rankings, and we provide a rigorous proof that essentially any reasonable model for imperfect rankings is a limit of models in this class. We then describe a specific, easily applied method for selecting an appropriate imperfect rankings model from the class.     

Inferences on correlation coefficients: One-sample,independent and correlated cases

This article concerns inference on the correlation coefficients of a multivariate normal distribution. Inferential procedures based on the concepts of generalized variables (GVs) and generalized p$p$-values are proposed for elements of a correlation matrix. For the simple correlation coefficient, the merits of the generalized confidence limits and other approximate methods are evaluated using a numerical study. The study indicates that the proposed generalized confidence limits are uniformly most accurate even for samples as small as three. The results are extended for comparing two independent correlations, overlapping and non-overlapping dependent correlations. For each problem, the properties of the GV approach and other asymptotic methods are evaluated using Monte Carlo simulation. The GV approach produces satisfactory results for all the problems considered. The methods are illustrated using a few practical examples.     

Wavelet goodness-of-fit test for dependent data

We introduce a new goodness-of-fit test which can be applied to hypothesis testing about the marginal distribution of dependent data. We derive a new test for the equivalent hypothesis in the space of wavelet coefficients. Such properties of the wavelet transform as orthogonality, localisation and sparsity make the hypothesis testing in wavelet domain easier than in the domain of distribution functions. We propose to test the null hypothesis separately at each wavelet decomposition level to overcome the problem of bi-dimensionality of wavelet indices and to be able to find the frequency where the empirical distribution function differs from the null in case the null hypothesis is rejected. We suggest a test statistic and state its asymptotic distribution under the null and under some of the alternative hypotheses.     

北宋书序作者所选择的传播因素分析

书序具有重要的传播作用,以北宋诗文集序为对象考察,书序作者在选择推动传播的因素时,对作品艺术性、知识性等文本因素的关注不如对作家身份、生平轶事等非文本因素普遍。其中原因,从书序作者的操作层面来看,是因为在书序中进行文艺批评是很难把握的;从面对读者的传播效果层面来分析,是因为非文本因素较之文本相关的评论因素可以产生显著的传播效果。于是,非文本因素在文学传播中变成了一位无形的操纵者,虽然它不是长远的传播之道,但却常常起着奇妙的传播作用,从而给书序这一文体增加活力。     

A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Estimated associations between an outcome variable and misclassified covariates tend to be biased when the methods of estimation that ignore the classification error are applied. Available methods to account for misclassification often require the use of a validation sample (i.e. a gold standard). In practice, however, such a gold standard may be unavailable or impractical. We propose a Bayesian approach to adjust for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov Chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. A simulated numerical example and a real clinical example are given to illustrate the proposed approach. Our results suggest that the estimated log odds of inpatient care and the corresponding standard deviation are much larger in our proposed method compared with the models ignoring misclassification. Ignoring misclassification produces downwardly biased estimates and underestimate uncertainty.     

INSIGHTS ON SERVICE SYSTEM DESIGN FROM A NORMAL APPROXIMATION TO ERLANG'S DELAY FORMULA

We show how a simple normal approximation to Erlang's delay formula can be used to analyze capacity and staffing problems in service systems that can be modeled as M/M/s queues. The numbers of servers, s, needed in an M/M/s queueing system to assure a probability of delay of, at most, p can be well approximated by s ≅ p + z_***I-p+, where z_1-p, is the (1 - p)th percentile of the standard normal distribution and ρ, the presented load on the system, is the ratio of Λ, the customer arrival rate, to μ, the service rate. We examine the accuracy of this approximation over a set of parameters typical of service operations ranging from police patrol, through telemarketing to automatic teller machines, and we demonstrate that it tends to slightly underestimate the number of servers actually needed to hit the delay probability target—adding one server to the number suggested by the above formula typically gives the exact result. More importantly, the structure of the approximation promotes operational insight by explicitly linking the number of servers with server utilization and the customer service level. Using a scenario based on an actual teleservicing operation, we show how operations managers and designers can quickly obtain insights about the trade-offs between system size, system utilization and customer service. We argue that this little used approach deserves a prominent role in the operations analyst's and operations manager's toolbags.