城市交通中车辆择路行为实证研究

随机、时变的交通流分布,偶发的交通事故等因素导致了路径随机的车辆旅行时间,也决定了现实城市交通网络中只存在随机最短路.已有的路径选择研究大都假设人们力求选择最短路,而通过实际调查发现:人们的择路行为依赖出行情景,随出行目的、约束时间、对路径的熟悉程度以及路径的不确定程度而变化.验证了城市交通中人们在不确定环境下的择路行为也符合展望理论.     

High value manufacturing (HVM) in the UK: case studies and focus group insights

This paper studies the internal mechanisms that allow organisations to become high value manufacturing (HVM). Using a qualitative methodology, three UK manufacturing companies formed in-depth case studies with semi-structured interviews, observations and historical data. The HVM value matrix of Martinez and co-workers is used to categorise each organisation’s value proposition. Wider benchmarking of the three organisations was carried out against a focus group with an additional seven manufacturing organisations. Thus, data from 10 manufacturing organisations are included in this research. The cases follow the ‘customer intimacy’ HVM discipline. The business processes supporting these value propositions were identified. Interestingly, each organisation’s desired value proposition differs from their current one. ‘Technological integrators’ predominantly rely on new product development (NPD) and Strategy processes, whereas ‘Socialisors’ rely predominantly on Strategy and Customer Relationship processes. Companies can use the findings to better understand their current HVM value proposition and, where necessary, plan their transition to a future desired HVM value proposition.     

基于ART及多风险规避的交通网络均衡模型

随着对交通系统不确定性认识的深入,以绝对理性为基础的“期望效用理论”在风险环境下的路径选择分析中显示出局限性,而“预期后悔理论”则为之提供了新的分析思路.将预期后悔理论应用到风险环境下的路径选择分析中,将出行者一致风险规避的假设扩展到多风险规避,建立了基于后悔理论及多风险规避出行特征的交通网络随机用户均衡变分不等式模型,并给出了求解算法.通过算例分析发现,后悔心理对出行者的路径选择并不总是显著的.在非风险环境及极端风险环境中,后悔心理对出行者的路径选择影响是微弱的,但是当环境处于极端风险与非风险之间时,后悔心理对出行者路径选择有着较为显著的影响.     

Tractable Consideration Set Structures for Assortment Optimization and Network Revenue Management

Discrete‐choice models are widely used to model consumer purchase behavior in assortment optimization and revenue management. In many applications, each customer segment is associated with a consideration set that represents the set of products that customers in this segment consider for purchase. The firm has to make a decision on what assortment to offer at each point in time without the ability to identify the customer's segment. A linear program called the Choice‐based Deterministic Linear Program (CDLP) has been proposed to determine these offer sets. Unfortunately, its size grows exponentially in the number of products and it is NP‐hard to solve when the consideration sets of the segments overlap. The Segment‐based Deterministic Concave Program with some additional consistency equalities (SDCP+) is an approximation of CDLP that provides an upper bound on CDLP's optimal objective value. SDCP+ can be solved in a fraction of the time required to solve CDLP and often achieves the same optimal objective value. This raises the question under what conditions can one guarantee equivalence of CDLP and SDCP+. In this study, we obtain a structural result to this end, namely that if the segment consideration sets overlap with a certain tree structure or if they are fully nested, CDLP can be equivalently replaced with SDCP+. We give a number of examples from the literature where this tree structure arises naturally in modeling customer behavior.     

软预算约束与国企改革：从要素投入的视角分析

论文运用动态模型对软预算约束的形成路径进行了理论研究,并对中国国有企业改革的路径选择进行实证研究。研究结果表明软预算约束起源于企业要素投入与生产模式的匹配模式,而中国国有企业改革路径选择的结果是使要素投入和生产模式满足弱匹配条件。     

Bayesian model comparison via parallel model output

Bayesian estimation via MCMC methods opens up new possibilities in estimating complex models. However, there is still considerable debate about how selection among a set of candidate models, or averaging over closely competing models, might be undertaken. This article considers simple approaches for model averaging and choice using predictive and likelihood criteria and associated model weights on the basis of output for models that run in parallel. The operation of such procedures is illustrated with real data sets and a linear regression with simulated data where the true model is known.     

Books on probability ideas are reviewed

In this paper, semiparametric methods are applied to estimate multivariate volatility functions, using a residual approach as in [J. Fan and Q. Yao, Efficient estimation of conditional variance functions in stochastic regression, Biometrika 85 (1998), pp. 645–660; F.A. Ziegelmann, Nonparametric estimation of volatility functions: The local exponential estimator, Econometric Theory 18 (2002), pp. 985–991; F.A. Ziegelmann, A local linear least-absolute-deviations estimator of volatility, Comm. Statist. Simulation Comput. 37 (2008), pp. 1543–1564], among others. Our main goal here is two-fold: (1) describe and implement a number of semiparametric models, such as additive, single-index and (adaptive) functional-coefficient, in volatility estimation, all motivated as alternatives to deal with the curse of dimensionality present in fully nonparametric models; and (2) propose the use of a variation of the traditional cross-validation method to deal with model choice in the class of adaptive functional-coefficient models, choosing simultaneously the bandwidth, the number of covariates in the model and also the single-index smoothing variable. The modified cross-validation algorithm is able to tackle the computational burden caused by the model complexity, providing an important tool in semiparametric volatility estimation. We briefly discuss model identifiability when estimating volatility as well as nonnegativity of the resulting estimators. Furthermore, Monte Carlo simulations for several underlying generating models are implemented and applications to real data are provided.     

Kernel adjusted nonparametric regression

In this paper we propose and study a new kernel regression estimator in which the kernel is taken from a properly adapted location-scale family of the design distribution. We show that, while the original smoothing may be performed with sub-optimal bandwidths, adaptation of proper scale parameters yields overall optimal estimators. Unlike traditional smoothing methodology, our approach does not aim at estimating pivotal higher order derivatives.     

Merging information for semiparametric density estimation

Summary.  The density ratio model specifies that the likelihood ratio of m −1 probability density functions with respect to the m th is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well-established techniques to choose the smoothing parameter for the density estimators proposed.