Information optimality and Bayesian modelling

The general approach of treating a statistical problem as one of information processing led to the Bayesian method of moments, reference priors, minimal information likelihoods, and stochastic complexity. These techniques rest on quantities that have physical interpretations from information theory. Current work includes: the role of prediction, the emergence of data dependent priors, the role of information measures in model selection, and the use of conditional mutual information to incorporate partial information.     

Nonparametric Bayesian modelling of monotone preferences for discrete choice experiments

Discrete choice experiments are widely used to learn about the distribution of individual preferences for product attributes. Such experiments are often designed and conducted deliberately for the purpose of designing new products. There is a long-standing literature on nonparametric and Bayesian modelling of preferences for the study of consumer choice when there is a market for each product, but this work does not apply when such markets fail to exist as is the case with most product attributes. This paper takes up the common case in which attributes can be quantified and preferences over these attributes are monotone. It shows that monotonicity is the only shape constraint appropriate for a utility function in these circumstances. The paper models components of utility using a Dirichlet prior distribution and demonstrates that all monotone nondecreasing utility functions are supported by the prior. It develops a Markov chain Monte Carlo algorithm for posterior simulation that is reliable and practical given the number of attributes, choices and sample sizes characteristic of discrete choice experiments. The paper uses the algorithm to demonstrate the flexibility of the model in capturing heterogeneous preferences and applies it to a discrete choice experiment that elicits preferences for different auto insurance policies.     

Non‐Gaussian dynamic Bayesian modelling for panel data

A first order autoregressive non‐Gaussian model for analysing panel data is proposed. The main feature is that the model is able to accommodate fat tails and also skewness, thus allowing for outliers and asymmetries. The modelling approach is designed to gain sufficient flexibility, without sacrificing interpretability and computational ease. The model incorporates individual effects and covariates and we pay specific attention to the elicitation of the prior. As the prior structure chosen is not proper, we derive conditions for the existence of the posterior. By considering a model with individual dynamic parameters we are also able to formally test whether the dynamic behaviour is common to all units in the panel. The methodology is illustrated with two applications involving earnings data and one on growth of countries. Copyright © 2009 John Wiley & Sons, Ltd.     

Bayesian robustness modelling using the O‐regularly varying distributions

The theory of robustness modelling is essentially based on heavy‐tailed distributions, because longer tails are more prepared to deal with diverse information (such as outliers) because of the higher probabilities on the tails. There are many classes of distributions that can be regarded as heavy tails; some of them have interesting properties and are not explored in statistics. In the present work, we propose a robustness modelling approach based on the O‐regularly varying class (ORV), which is a generalization of the regular variation family; however, the ORV class allows more flexible tails behaviour, which can improve the way in which the outlying information is discarded by the model. We establish sufficient conditions in the location and in the scale parameter structures, which allow to resolve automatically the conflicts of information. We also provide a procedure for generating new distributions within the ORV class.     

Climbing the drug staircase: a Bayesian analysis of the initiation of hard drug use

Since empirical studies have shown that cannabis users are much more likely to initiate hard drug use, a causal linkage has been suggested (‘gateway hypothesis’). However, individual differences in proneness and accessibility to drugs provide alternative non‐causal explanations for the observed drug use pattern. We propose a Bayesian estimation and predictive framework to analyze the effects and relative importance of previous cannabis use, proneness and accessibility factors on hard drug initiation and to explore possible policy implications. We employ a novel model specification, motivated by four gateway transmission channels, to analyze data from a recent Norwegian survey of young adults. Copyright © 2010 John Wiley & Sons, Ltd.     

An application of the generalized Poisson difference distribution to the Bayesian modelling of football scores

The analysis of sports data, in particular football match outcomes, has always produced an immense interest among the statisticians. In this paper, we adopt the generalized Poisson difference distribution (GPDD) to model the goal difference of football matches. We discuss the advantages of the proposed model over the Poisson difference (PD) model, which was also used for the same purpose. The GPDD model, like the PD model, is based on the goal difference in each game that allows us to account for the correlation without explicitly modelling it. The main advantage of the GPDD model is its flexibility in the tails by considering shorter as well as longer tails than the PD distribution. We carry out the analysis in a Bayesian framework in order to incorporate external information, such as historical knowledge or data, through the prior distributions. We model both the mean and the variance of the goal difference and show that such a model performs considerably better than a model with a fixed variance. Finally, the proposed model is fitted to the 2012–2013 Italian Serie A football data, and various model diagnostics are carried out to evaluate the performance of the model.     

Recent trends in collective bargaining in Nigeria

During the 1970s Nigerian industrial relations moved away from the voluntarist tradition into much greater formalisation of processes and practices. This article describes the present structure and scope of collective bargaining in Nigeria in the light of this development.     

Role of experimental randomization in Bayesian statistics: An asymptotic theory for a single Bayesian

Summary Many experimental situations with controllable, independent design variables have an associated null hypothesis ₀ of zero dependence of observations on the design variables. The necessity of randomization of the design variables for asymptotic discriminability between ₀ and its complement is considered in terms of likelihood ratios. Applications are made to stochastic processes and comparative experiments.

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Zusammenfassung Viele experimentelle Situationen mit kontrollierten, unabhängigen Planungsvariablen haben eine assoziierte Null-Hypothese ₀, die besagt, daß die Beobachtungen nicht von den Planungsvariablen abhängen. Die Notwendigkeit der Randomisierung der Planungsvariablen für asymptotische Diskriminanz zwischen ₀ und ihres Komplementes wird mit Hilfe des Likelihood-Verhältnisses untersucht. Anwendungen auf dem Gebiet stochastischer Prozesse und komparativer Experimente werden diskutiert.

     

在尼日利亚做好项目安全生产管理工作的几点体会

工程项目是施工企业效益的源泉、信誉的窗口。施工企业管理的重心是项目管理,而工程项目管理尤以安全质量管理为重中之重。尼日利亚子公司作为川铁国际公司属地化经营试点单位,主要从事工程承包,目前工程涉及公路、桥梁、房建、市政工程等领域。子公司一直把安全生产管理作为工作的重中之重,把安全生产放在最为突出的位置,确保生产安全和交通安全。尼日利亚子公司自成立以来,安全生产一直处于有序可控状态,至今未发生施工安全和交通安全大的事故,赢得了当地政府和百姓的高度称赞。本文结合子公司安全管理情况,谈谈如何做好企业的安全生产工作。     

Nonlinear dynamics in economic modelling

Decisions in Economics and Finance -     

On model selection in Bayesian regression

We discuss the problem of constructing a suitable regression model from a nonparametric Bayesian viewpoint. For this purpose, we consider the case when the error terms have symmetric and unimodal densities. By the Khintchine and Shepp theorem, the density of response variable can be written as a scale mixture of uniform densities. The mixing distribution is assumed to have a Dirichlet process prior. We further consider appropriate prior distributions for other parameters as the components of the predictive device. Among the possible submodels, we select the one which has the highest posterior probability. An example is given to illustrate the approach.     

尼日利亚拉各斯巴达格瑞高速公路沼泽地路基施工方法

详细介绍尼日利亚拉各斯巴达格瑞高速公路项目沼泽地路基施工方法,内容包括临时围堰、井点降水、淤泥清理及软土开挖、填沙及沙土路基边坡防护等,是在西非热带雨林地区进行沼泽地路基施工的探索。     

The effect of breastfeeding on children's cognitive and noncognitive development

This paper uses propensity score matching methods to investigate the relationship between breastfeeding and children's cognitive and noncognitive development. We find that breastfeeding for four weeks is positively and statistically significantly associated with higher cognitive test scores, by around one tenth of a standard deviation. The association between breastfeeding and noncognitive development is weaker, and is restricted to children of less educated mothers. We conclude that interventions which increase breastfeeding rates would improve not only children's health, but also their cognitive skills, and possibly also their noncognitive development.     

Bayesian estimation of agent-based models

We consider Bayesian inference techniques for agent-based (AB) models, as an alternative to simulated minimum distance (SMD). Three computationally heavy steps are involved: (i) simulating the model, (ii) estimating the likelihood and (iii) sampling from the posterior distribution of the parameters. Computational complexity of AB models implies that efficient techniques have to be used with respect to points (ii) and (iii), possibly involving approximations. We first discuss non-parametric (kernel density) estimation of the likelihood, coupled with Markov chain Monte Carlo sampling schemes. We then turn to parametric approximations of the likelihood, which can be derived by observing the distribution of the simulation outcomes around the statistical equilibria, or by assuming a specific form for the distribution of external deviations in the data. Finally, we introduce Approximate Bayesian Computation techniques for likelihood-free estimation. These allow embedding SMD methods in a Bayesian framework, and are particularly suited when robust estimation is needed. These techniques are first tested in a simple price discovery model with one parameter, and then employed to estimate the behavioural macroeconomic model of De Grauwe (2012), with nine unknown parameters.     

Bayesian assessment of dimensionality in reduced rank regression

We consider Bayesian inference about the dimensionality in the multivariate reduced rank regression framework, which encompasses several models such as MANOVA, factor analysis and cointegration models for multiple time series. The fractional Bayes approach is used to derive a closed form approximation to the posterior distribution of the dimensionality and some asymptotic properties of the approximation are proved. Finite sample properties are studied by simulation and the method is applied to growth curve data and cointegrated multivariate time series.     

Bayesian clustering of distributions in stochastic frontier analysis

In stochastic frontier analysis, firm-specific efficiencies and their distribution are often main variables of interest. If firms fall into several groups, it is natural to allow each group to have its own distribution. This paper considers a method for nonparametrically modelling these distributions using Dirichlet processes. A common problem when applying nonparametric methods to grouped data is small sample sizes for some groups which can lead to poor inference. Methods that allow dependence between each group’s distribution are one set of solutions. The proposed model clusters the groups and assumes that the unknown distribution for each group in a cluster are the same. These clusters are inferred from the data. Markov chain Monte Carlo methods are necessary for model-fitting and efficient methods are described. The model is illustrated on a cost frontier application to US hospitals.     

Bayesian estimation of explained variance in ANOVA designs

We propose to use the squared multiple correlation coefficient as an effect size measure for experimental analysis‐of‐variance designs and to use Bayesian methods to estimate its posterior distribution. We provide the expressions for the squared multiple, semipartial, and partial correlation coefficients corresponding to four commonly used analysis‐of‐variance designs and illustrate our contribution with two worked examples.     

非寿险精算中的贝叶斯信用模型分析

针对传统B櫣hlmann-Straub信用模型不能有效地解决缺失数据信息处理问题,本文利用贝叶斯统计方法,构造了一类新的贝叶斯信用分析模型,引入基于吉布斯抽样的马尔科夫链蒙特卡洛方法进行数值计算,建立了一个索赔后验分层正态模型进行实证分析,证明模型的有效性。研究结果表明,基于MCMC的贝叶斯信用模型能够动态模拟模型参数的后验分布,提高模型估计的精度,对保险公司经验费率厘定方法的改进具有重要的现实意义。