Nonparametric Bayes inference for concave distribution functions

Bayesian inference for concave distribution functions is investigated. This is made by transforming a mixture of Dirichlet processes on the space of distribution functions to the space of concave distribution functions. We give a method for sampling from the posterior distribution using a Pólya urn scheme in combination with a Markov chain Monte Carlo algorithm. The methods are extended to estimation of concave distribution functions for incompletely observed data.     

Nonparametric estimation of variance, skewness and kurtosis of the distribution of a statistic by jackknife and bootstrap techniques

While jackknife and bootstrap estimates of the variance of a statistic are well–known, the author extends these nonparametric maximum likelihood techniques to the estimation of skewness and kurtosis. In addition to the usual negative jackknife also a positive jackknife as proposed by BERAN (1984) receives interest in this work. The performance of the methods is investigated by a Monte Carlo study for Kendall's tau in various situations likely to occur in practice. Possible applications of these developments are discussed.     

Interview with Professor Adrian FM Smith

Adrian Smith joined The Alan Turing Institute as Institute Director and Chief Executive in September 2018. In May 2020, he was confirmed as President Elect of the Royal Society. He is also a member of the government's AI Council, which helps boost AI growth in the UK and promote its adoption and ethical use in businesses and organisations across the country. Professor Smith's previous role was Vice-Chancellor of the University of London where he was in post from 2012. He is a past President of the Royal Statistical Society and was elected a Fellow of the Royal Society in 2001 in recognition of his contribution to statistics. In 2003-04 Professor Smith undertook an inquiry into Post-14 Mathematics Education for the UK Secretary of State for Education and Skills and in 2017, on behalf of Her Majesty's Treasury and the Department for Education, published a 16-18 Maths Review. In 2006 he completed a report for the UK Home Secretary on the issue of public trust in Crime Statistics. He received a knighthood in the 2011 New Year Honours list. The following conversation took place at the Alan Turing Institute in London, on July 19 2019.     

非参数统计视角下的异方差检验设计及其应用

以数据生成过程为导向,探讨了异方差来源的基本类型;依据非参数统计的基本思想,设计了切实可行的Mood方差检验方法与平方秩检验方法,并针对异方差来源类型,分析了相应的检验思路。蒙特卡罗模拟表明,Mood方差检验方法在异方差检验方面具有很高的检验效力;平方秩检验方法在异常经济现象情形下检验效力较低,而在其他情形下检验效力很高。同时,进一步阐释了纠正异方差的基本逻辑。     

A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models

It is well known that the usual procedures for estimating panel data models are inconsistent in the dynamic setting. A large number of consistent estimators however, have been proposed in the literature. This paper provides a survey of the majority of mainstream estimators, which tend to consist of IV and GMM ones. It also considers a newly proposed extension to the promising Wansbeek–Bekker estimator (Harris & Mátyás, 2000). To provide guidance to the applied researcher working on micro-datasets, the small sample performance of these estimators is evaluated using a set of Monte Carlo experiments.     

蒙特卡洛模拟方法在矿业投资风险分析中的应用

蒙特卡洛模拟方法是一种非常重要的矿业投资风险分析方法。文中介绍了蒙特卡洛模拟方法的思想和具体步骤,以及常见随机数产生方式,同时讲述了该方法的成功例子,最后简单分析了该方法的优点以及目前在使用中存在的问题。     

A Geo-Additive Bayesian Discrete-Time Survival Model and its Application to Spatial Analysis of Childhood Mortality in Malawi

We describe a flexible geo-additive Bayesian survival model that controls, simultaneously, for spatial dependence and possible nonlinear or time-varying effects of other variables. Inference is fully Bayesian and is based on recently developed Markov Chain Monte Carlo techniques. In illustrating the model we introduce a spatial dimension in modelling under-five mortality among Malawian children using data from Malawi Demographic and Health Survey of 2000. The results show that district-level socioeconomic characteristics are important determinants of childhood mortality. More importantly, a separate spatial process produces district clustering of childhood mortality indicating the importance of spatial effects. The visual nature of the maps presented in this paper highlights relationships that would, otherwise, be overlooked in standard methods.     

CKLS-JUMP过程驱动的利率动态模型:理论估计与实证模拟

利率期限结构动态模式研究已经成为现代金融领域的一个研究热点,而跳跃扩散过程已经成为模拟存贷款利率最为有效的动态模型。本文主要以商业银行存贷款利率为对象,研究分析利率期限结构的动态变化过程。首先基于存贷款利率的变化特征,建立利率的CKLS-JUMP跳跃扩散模型;其次,运用马尔科夫链蒙特卡罗模拟方法(MCMC)对其参数进行理论估计;最后,以我国商业银行五年期存贷款利率为例进行实证模拟。研究结论认为:CKLS-JUMP模型更加符合我国存贷款利率动态行为;同时MCMC方法比传统估计方法更加准确。     

Nonparametric Efficiency Analysis under Price Uncertainty: A First-Order Stochastic Dominance Approach

This paper extends the nonparametric approach to efficiency analysis to deal with uncertainty of input-output prices. We generalize the notion of economic efficiency to derive necessary and sufficient first-order stochastic dominance (FSD) efficiency conditions. Interestingly, the FSD conditions include as limiting cases the traditional conditions for economic efficiency and technical efficiency. Furthermore, we propose empirical tests for these FSD conditions, which require minimal assumptions concerning the preferences of the decision-maker and the statistical distribution of the prices. From operational point of view, the FSD conditions can be tested empirically using standard mathematical programming techniques. An empirical application to the Dutch electricity distribution sector illustrates the approach.     

Small sample properties of maximum likelihood versus generalized method of moments based tests for spatially autocorrelated errors

Many applied researchers have to deal with spatially autocorrelated residuals (SAR). Available tests that identify spatial spillovers as captured by a significant SAR parameter, are either based on maximum likelihood (MLE) or generalized method of moments (GMM) estimates. This paper illustrates the properties of various tests for the null hypothesis of a zero SAR parameter in a comprehensive Monte Carlo study. The main finding is that Wald tests generally perform well regarding both size and power even in small samples. The GMM-based Wald test is correctly sized even for non-normally distributed disturbances and small samples, and it exhibits a similar power as its MLE-based counterpart. Hence, for the applied researcher the GMM Wald test can be recommended, because it is easy to implement.     

中国企业资本结构动态调整的估计方法与蒙特卡罗模拟

本文首先从计量经济学以及资本结构动态调整的视角逐一评述了资本结构部分调整模型的六种主要估计方法,即混合OLS估计法、Fama-MacBeth估计法、固定效应估计法、GMM估计法、长差分LD估计法以及双边截取Tobit估计法。然后,针对我国上市公司的实际样本使用这六种估计方法进行了实证检验。在此基础上,依据我国上市公司资本结构的分布形态,采用蒙特卡罗模拟技术来识别和判断这六种方法在估计我国上市公司资本结构调整速度中的有效性。本文发现,传统的混合OLS估计法及Fama-MacBeth估计法反而是检验我国上市公司资本结构动态调整的有效方法,其估计所得的市值杠杆平均调整速度为16.3%－20.1%。     

A Markov Chain Monte Carlo procedure to generate revealed preference consistent datasets

This paper presents Markov-Chain-Monte-Carlo (MCMC) procedures to sample uniformly from the collection of datasets that satisfy some revealed preference test. The MCMC for the GARP test combines a Gibbs-sampler with a simple hit and run step. It is shown that the MCMC has the uniform distribution as its unique invariant distribution and that it converges to this distribution at an exponential rate.     

蒙特卡罗模拟在项目财务评价中的应用

把蒙特卡罗方法运用于项目财务评价,可以通过构造数学模型与计算机仿真得到相对精确的评价值概率分布,并对该评价结果的有效性进行一定程度的讨论。     

基于实验方法的经济行为特征研究--当代经济学发展新特点

行为经济学基于心理学原理和实验方法,开辟了当代经济学发展的新途径,是活跃在前沿领域、备受关注的一个新的理论分支。行为经济学的形成发展,对理性人假设的检验、研究方法和研究对象的扩展等对现代经济学构成了挑战,以及近年来在宏观经济、劳动经济、金融和消费行为选择等领域取得的应用成就,启发我们重新审视中国环境中经济行为的基本特征,促进经济理论建设和解决改革深层次问题。     

A Skew‐normal copula‐driven GLMM

This paper presents a method for fitting a copula‐driven generalized linear mixed models. For added flexibility, the skew‐normal copula is adopted for fitting. The correlation matrix of the skew‐normal copula is used to capture the dependence structure within units, while the fixed and random effects coefficients are estimated through the mean of the copula. For estimation, a Monte Carlo expectation–maximization algorithm is developed. Simulations are shown alongside a real data example from the Framingham Heart Study.     

A Comparative Simulation Study of Wavelet Shrinkage Estimators for Poisson Counts

Using computer simulations, the finite sample performance of a number of classical and Bayesian wavelet shrinkage estimators for Poisson counts is examined. For the purpose of comparison, a variety of intensity functions, background intensity levels, sample sizes, primary resolution levels, wavelet filters and performance criteria are employed. A demonstration is given of the use of some of the estimators to analyse a data set arising in high-energy astrophysics. Following the philosophy of reproducible research, the M atlab programs and real-life data example used in this study are made freely available.     

A Factor Analytical Approach to Price Discovery

Existing econometric approaches for studying price discovery presume that the number of markets are small, and their properties become suspect when this restriction is not met. They also require making identifying restrictions and are in many cases not suitable for statistical inference. The current paper takes these shortcomings as a starting point to develop a factor analytical approach that makes use of the cross‐sectional variation of the data, yet is very user‐friendly in that it does not involve any identifying restrictions or obstacles to inference.     

基于模糊综合评价的施工进度计划风险模拟

针对风险因素给施工进度计划带来的不确定影响,对项目活动的风险因素进行了模糊综合评价,确定了主导风险因素,明确了活动时间的概率分布,改进了传统PERT网络活动时间的估计方法。在此基础上,对PERT网络工期分布及进度风险进行了蒙特卡罗仿真模拟。通过某岩石溶注浆工程的实例验证结果表明,经典PERT网络工期的正态分布假设基本符合工程实际,进度风险估计与考虑了主导风险因素的估计存在一定偏差。     

Why I am a Psychology Major: An Empirical Analysis of Student Motivations

The underlying “architecture of the decision to pursue a degree in psychology was quantified using the Method of Sorting technique to identifying the critical issues necessary to make this choice. Multidimentsional scaling procedures were employed to construct a three-dimensional map representing the relationships between reasons for selecting psychology as a major. Freshman and senior psychology majors (N = 165) from a regional university and a large research-based institution rated the relative importance of items in their decision-making process. Hierarchical clustering procedures revealed seven different groups of students. Although significant differences associated with class standing were not found, institutional affiliation did influence cluster composition. Reflecting local emphases, students at the regional institution had a greater interest in Counseling Psychology, whereas those at the research-based school focused on Clinical Psychology. This semantic map and the associated item clusters arising from psychology student data provides an empirical basis for, amongst other things, course selection, faculty-initiated program design or revision, strategic niche marketing, and student retention.