地理距离、方言文化与劳动力空间流动

本文基于百度迁徙大数据研究中国劳动力的空间流动，系统考察地理和文化对劳动力流动的影响。通过构造流动机会比率，基于引力模型和普通最小二乘法的研究表明地理距离和方言距离阻碍劳动力流动。在空间距离上，劳动力偏好邻近城市，地理距离每增加1%，劳动力的流动机会比率降低约0.6%。在空间位置上，劳动力倾向于在方言文化相近地域范围流动，方言距离每增加1%，劳动力的流动机会比率下降2%左右。通过构造两地年均降水量差距和小麦种植适宜度差距作为方言距离的工具变量，以两阶段最小二乘法估计缓解内生性问题，估计显示结论稳健。考虑普通话因素后方言距离的抑制影响依然稳健，但目的地的高普通话普及率显著发挥促进劳动力流动的引力作用。最后，本文得出持续推广普通话、加强交通建设和深化中等教育的政策建议。     

《明伦大典》的编纂始末

文章详细介绍了嘉靖《明伦大典》的编纂过程，该书由《大礼奏议》到《明伦大典》最后成书，历时四年，几易其稿，经数人之手，是嘉靖时期的一部重要的史料，对大礼议及嘉靖政治研究有重大意义。笔者通过分析该书的编纂原因、编纂过程以及内容上前后数稿的异同，深入发掘了该书的重要意义与史料价值。     

A response to M. Stone: 'How not to measure the efficiency of public services (and how one might)'

Summary.  This is a response to Stone's criticisms of the Spottiswoode report to the UK Treasury which was responding to the Treasury's request for improved methods to evaluate the efficiency and productivity of the 43 police districts in England and Wales. The Spottiswoode report recommended uses of data envelopment analysis (DEA) and stochastic frontier analysis (SFA), which Stone critiqued en route to proposing an alternative approach. Here we note some of the most serious errors in his criticism and inaccurate portrayals of DEA and SFA. Most of our attention is devoted to DEA, and to Stone's recommended alternative approach without much attention to SFA, partly because of his abbreviated discussion of the latter. In our response we attempt to be constructive as well as critical by showing how Stone's proposed approach can be joined to DEA to expand his proposal beyond limitations in his formulations.     

Profit-oriented productivity change

This study develops an applicable profit-oriented productivity indicator when producers pursue profit maximization and can recognize input and output prices. We define the indicator, inspired by the Luenberger indicator and the Nerlovian efficiency measurement, in terms of both quantity distance functions and profit. Hence, the study׳s first stage decomposes the profit-oriented productivity change into two terms: profit efficiency change and profit technology change. Second, we decompose profit efficiency change into the changes in technical efficiency and allocative efficiency. Finally, profit technology change is separated into two components for capturing the shifts of technology and relative output/input prices. These decompositions provide a more complete picture of the sources of productivity change. We illustrate them with a sample of Taiwanese banks and compute the results using the models of directional distance functions.     

When Costs Are Unequal and Unknown: A Subtree Grafting Approach for Unbalanced Data Classification*

In binary classifications, a decision tree learned from unbalanced data typically creates an important challenge related to the high misclassification rate of the minority class. Assigning different misclassification costs can address this problem, though usually at the cost of accuracy for the majority class. This effect can be particularly hazardous if the costs cannot be specified precisely. When the costs are unknown or difficult to determine, decision makers may prefer a classifier with more balanced accuracy for both classes rather than a standard or cost‐sensitively learned one. In the context of learning trees, this research therefore proposes a new tree induction approach called subtree grafting (STG). On the basis of a real bank data set and several other data sets, we test the proposed STG method and find that our proposed approach provides a successful compromise between standard and cost‐sensitive trees.     

一个动态学生收费系统的设计

本文采用UCDOS希望汉字操作系统所提供的SDK开发工具在Fozpro数据库开发系统下，采用动态数据库的生成方法设计了一个学生收费管理系统，避免了传统静态数据库的缺陷。     

图像化时代网络小说的空间化分析

网络媒介既为网络小说提供了传播的平台,其特点也影响了小说的创作与接受模式,造成网络小说中情节高速发展,时间随意切断和压缩,包含于漫画化的小说空间中。网络小说创作趋向图像化与空间化,并在一个想象的异度空间中对当代的现状加以选择性地呈现,并反过来刺激了这一现状中的当代逻辑,使受众在看似游戏轻松的接受状态中,潜在地接受了这些价值常常显得暧昧不清的意识形态。     

Detecting homogeneous segments in DNA sequences by using hidden Markov models

In recent years there has been a rapid growth in the amount of DNA being sequenced and in its availability through genetic databases. Statistical techniques which identify structure within these sequences can be of considerable assistance to molecular biologists particularly when they incorporate the discrete nature of changes caused by evolutionary processes. This paper focuses on the detection of homogeneous segments within heterogeneous DNA sequences. In particular, we study an intron from the chimpanzee α-fetoprotein gene; this protein plays an important role in the embryonic development of mammals. We present a Bayesian solution to this segmentation problem using a hidden Markov model implemented by Markov chain Monte Carlo methods. We consider the important practical problem of specifying informative prior knowledge about sequences of this type. Two Gibbs sampling algorithms are contrasted and the sensitivity of the analysis to the prior specification is investigated. Model selection and possible ways to overcome the label switching problem are also addressed. Our analysis of intron 7 identifies three distinct homogeneous segment types, two of which occur in more than one region, and one of which is reversible.     

Partial least squares estimator for single-index models

The partial least squares (PLS) approach first constructs new explanatory variables, known as factors (or components), which are linear combinations of available predictor variables. A small subset of these factors is then chosen and retained for prediction. We study the performance of PLS in estimating single-index models, especially when the predictor variables exhibit high collinearity. We show that PLS estimates are consistent up to a constant of proportionality. We present three simulation studies that compare the performance of PLS in estimating single-index models with that of sliced inverse regression (SIR). In the first two studies, we find that PLS performs better than SIR when collinearity exists. In the third study, we learn that PLS performs well even when there are multiple dependent variables, the link function is non-linear and the shape of the functional form is not known.