Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

高校廉洁管理路径探析

廉洁文化是社会主义先进文化的重要组成部分。在高校推行廉洁化管理不仅是和谐高校构建的需要,还是我国反腐败战略的重要组成部分。当前,高校推行廉洁化管理,必须构建全员育廉、全程育廉、全方位育廉三种范式,并在实践中通过健全制度、利用载体和形成合力来完善这三种范式。     

任务型教学法在教学中的应用及影响

文章介绍了任务型教学法的基本概况,阐述了如何将任务型教学法有效引入大学英语教学,探讨了任务型教学法对大学英语教学的影响。     

Gay tourism in a sun and beach destination

Gay tourism is seen as an attractive business opportunity for many destinations. However, there is a lack of research at identifying the resources necessary for success in this type of tourism. This work is aimed at filling this gap by using the premises of the resource based view and transfers them to the analysis of territories to identify the valuable resources that are required for a sun and beach destination to attract gay tourists. To this end, this study has focused on a gay tourism sub niche, tourists lodged in gay-exclusive resorts in Gran Canaria. In order to confirm the validity of this approach, the relationship between the satisfaction of gay tourists and the condition of the valuable resources was studied by means of a robust statistical new method, namely Bayesian model averaging. That method permits the inclusion of uncertainty in the theoretical models that determine destination competitiveness, thus reducing many of the problems that arise in the application of the more conventional statistical methods in this type of analysis.     

Detecting log-periodicity in a regime-switching model of stock returns

Log-periodic precursors have been identified before most and perhaps all financial crashes of the Twentieth Century, but efforts to statistically validate the leading model of log-periodicity, the Johansen–Ledoit–Sornette (JLS) model, have generally failed. The main feature of this model is that log-harmonic fluctuations in financial prices are driven by similar fluctuations in expected daily returns. Here we search more broadly for evidence of any log-periodic variation in expected daily returns by estimating a regime-switching model of stock returns in which the mean return fluctuates between a high and a low value. We find such evidence prior to the two largest drawdowns in the S&P 500 since 1950. However, if we estimate a log-harmonic specification for the stock index for the same time periods, fixing the frequency and critical time according to the results of the regime-switching model, the parameters do not satisfy restrictions imposed by the JLS model.     

A New Typology for Economic Sectors with a view to Policy Implications

This paper is an attempt to tease out a typology of economic sectors based on a systems approach to innovation and economic growth that may be useful for policy analysis. The typology explored here revolves around novel products rather than ethereal knowledge-producing entities. This insight goes back to Allyn Young (1928) and Joseph Schumpeter (1934) who argued that the introduction of new goods was the engine of economic growth. More precisely, our typology of sectors focuses on novel products which are efficiency-enhancing within and between sectors through the market mechanism. The scheme revolves around the relationship between 'Enabling' and 'Recipient' sectors (which gives the typology its name: ER), and offers a lens for viewing and interpreting a substantive part of the mechanics of modern economic growth. The last part of the paper briefly discusses a few immediate policy implications, although it has the potential for greater use and value in this regard.     

Bounds for Ratios of Posterior Expectations: Applications in the Collective Risk Model

This paper considers the collective risk model for the insurance claims process. We will adopt a Bayesian point of view, where uncertainty concerning the specification of the prior distribution is a common question. The robust Bayesian approach uses a class of prior distributions which model uncertainty about the prior, instead of a single distribution. Relatively little research has dealt with robustness with respect to ratios of posterior expectations as occurs with the Esscher and Variance premium principles. Appropriate techniques are developed in this paper to solve this problem using the k -contamination class in the collective risk model.     

Stochastic model specification search for Gaussian and partial non-Gaussian state space models

Model specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time-varying. To this aim a new model space MCMC method is developed in this paper. It is based on extending the Bayesian variable selection approach which is usually applied to variable selection in regression models to state space models. For non-Gaussian state space models stochastic model search MCMC makes use of auxiliary mixture sampling. We focus on structural time series models including seasonal components, trend or intervention. The method is applied to various well-known time series.     

Bayesian and Frequentist Inference for Ecological Inference: The R×C Case

In this paper we propose Bayesian and frequentist approaches to ecological inference, based on R × C contingency tables, including a covariate. The proposed Bayesian model extends the binomial-beta hierarchical model developed by K ing , R osen and T anner (1999) from the 2×2 case to the R × C case. As in the 2×2 case, the inferential procedure employs Markov chain Monte Carlo (MCMC) methods. As such, the resulting MCMC analysis is rich but computationally intensive. The frequentist approach, based on first moments rather than on the entire likelihood, provides quick inference via nonlinear least-squares, while retaining good frequentist properties. The two approaches are illustrated with simulated data, as well as with real data on voting patterns in Weimar Germany. In the final section of the paper we provide an overview of a range of alternative inferential approaches which trade-off computational intensity for statistical efficiency.     

The Macroeconomic Consequences of Mixing Sunnis and Shias: A Bayesian Errors-in-Variables Approach

How damaging is the Sunni/Shia split to the economies of Islamic countries? Is it better to be one or the other? Or is it better to have an even balance between the two? Answering these questions is complicated by the fact that the data are often missing or imprecisely measured. We employ the technique of Bayesian data augmentation to circumvent these two problems, and find that properly controlling for these features in the data leads to drastically different conclusions than what is found using ordinary least squares. We find that there is nothing in the differential nature of Sunni or Shia Islam to make one more economically prosperous than the other. Nor do we find any support for the popular hypothesis that Sunnis and Shias cannot mix.