Robust inference for parsimonious model-based clustering

We introduce a robust clustering procedure for parsimonious model-based clustering. The classical mclust framework is robustified through impartial trimming and eigenvalue-ratio constraints (the tclust framework, which is robust but not affine invariant). An advantage of our resulting mtclust approach is that eigenvalue-ratio constraints are not needed for certain model formulations, leading to affine invariant robust parsimonious clustering. We illustrate the approach via simulations and a benchmark real data example. R code for the proposed method is available at https://github.com/afarcome/mtclust.     

Estimation of factor-augmented panel regressions with weakly influential factors

The use of factor-augmented panel regressions has become very popular in recent years. Existing methods for such regressions require that the common factors are strong, an assumption that is likely to be mistaken in practice. Motivated by this, the current article offers an analysis of the effect of weak, semi-weak, and semi-strong factors on two of the most popular estimators for factor-augmented regressions, namely, principal components (PC) and common correlated effects (CCE).     

Exploration of Soft Power Path of Enterprises in Ethnic Minority Areas from the Perspective of Libich's Minimum Factor Theory

For the construction of enterprises’ soft power in minority areas, the biggest resource is the local original ecological culture, which transforms into the soft power of enterprises in minority areas, and cultivates the core competitiveness of enterprises, so as to promote the rapid growth of enterprises in these areas, to go out of these regions, and to provide new impetus for local development. Minimum factor theory tells us that in minority areas, the key to the success of soft power construction is to find the gene of enterprise culture from the original ecological culture, so as to build a unique soft power system, realize the development of enterprise clusters, and then drive the rapid progress of regional evolution. Since the implementation of market-oriented reform, the economic history in these areas shows that those enterprises not only achieve rapid development, but contribute to local economic development as the leaders in specific fields, with the transformation of original ecological culture into their own soft power.     

Bayesian compartmental model for an infectious disease with dynamic states of infection

Population-level proportions of individuals that fall at different points in the spectrum [of disease severity], from asymptomatic infection to severe disease, are often difficult to observe, but estimating these quantities can provide information about the nature and severity of the disease in a particular population. Logistic and multinomial regression techniques are often applied to infectious disease modeling of large populations and are suited to identifying variables associated with a particular disease or disease state. However, they are less appropriate for estimating infection state prevalence over time because they do not naturally accommodate known disease dynamics like duration of time an individual is infectious, heterogeneity in the risk of acquiring infection, and patterns of seasonality. We propose a Bayesian compartmental model to estimate latent infection state prevalence over time that easily incorporates known disease dynamics. We demonstrate how and why a stochastic compartmental model is a better approach for determining infection state proportions than multinomial regression is by using a novel method for estimating Bayes factors for models with high-dimensional parameter spaces. We provide an example using visceral leishmaniasis in Brazil and present an empirically-adjusted reproductive number for the infection.     

旅游者对景观生态美的识别研究

生态美反映了人类与自然界，即人的内在自然与外在自然的和谐统一关系。旅游者在旅游活动中随处可以感受到这种生态美景。为使主要审美群体——旅游者能更好地鉴赏和识别景观生态美，本文分析了生态美内涵、生态与美感关联度、景观生态美内涵，得出景观生态美识别因子为适应、健康和创造，以期能协助旅游者更好地欣赏生态美景，同时也启发景观规划实践者，营造出更优质的生态美景观。     

A pooled Bayes test of independence for sparse contingency tables from small areas

We study the association between bone mineral density (BMD) and body mass index (BMI) when contingency tables are constructed from the several U.S. counties, where BMD has three levels (normal, osteopenia and osteoporosis) and BMI has four levels (underweight, normal, overweight and obese). We use the Bayes factor (posterior odds divided by prior odds or equivalently the ratio of the marginal likelihoods) to construct the new test. Like the chi-squared test and Fisher's exact test, we have a direct Bayes test which is a standard test using data from each county. In our main contribution, for each county techniques of small area estimation are used to borrow strength across counties and a pooled test of independence of BMD and BMI is obtained using a hierarchical Bayesian model. Our pooled Bayes test is computed by performing a Monte Carlo integration using random samples rather than Gibbs samples. We have seen important differences among the pooled Bayes test, direct Bayes test and the Cressie-Read test that allows for some degree of sparseness, when the degree of evidence against independence is studied. As expected, we also found that the direct Bayes test is sensitive to the prior specifications but the pooled Bayes test is not so sensitive. Moreover, the pooled Bayes test has competitive power properties, and it is superior when the cell counts are small to moderate.     

Formulas for the second-order analysis of marked point processes

Following Ripley, for the second moment measure of stationary isotropic marked point processes in R^destimators are given. Two examples are considered.     

How to build nomogram for type 2 diabetes using a naïve Bayesian classifier technique

In this study, we introduced a method for building a Bayesian nomogram and proposed an appropriate nomogram for type 2 diabetes (T2D) using data from 13,474 subjects collected from the 2013–2015 Korean National Health and Nutrition Examination Survey (KNHANES) data. We identified risk factors related to T2D, proposed a visual nomogram for T2D from a naïve Bayesian classifier model, and predicted incidence rates. Additionally, we computed confidence intervals for the influence of risk factors (attributes) and verified the proposed Bayesian nomogram using a receiver operating characteristic curve. Finally, we compared logistic regression and the Bayesian nomogram for T2D. The results of the analysis of the T2D data showed that the most influential factor among all attributes in the Bayesian nomogram was age group, and the highest risk factor for T2D incidence was cardiovascular disease. Dyslipidemia and hypertension also had significant impacts on T2D incidence while the effects of sex, smoking status, and employment status were relatively small compared to those of other variables. Using the proposed Bayesian nomogram, we can easily predict the incidence rate of T2D in an individual, and treatment plans can be established based on this information.     

民营企业高层管理者胜任力实证研究——基于因子分析法的评价

为揭示中国民营企业高层管理者的胜任特征,基于因子分析法对胜任力特征进行了实证分析。分析认为：民营企业高层管理者胜任力建设所面临的具体问题是品德素养、基本能力、个人才能、组织协调、领导艺术;提出了针对民营企业高层管理者胜任力建设的模型,并从选拔、培训及绩效考评3个方面建设的策略。     

Research Summary of Chinese Ecological Anthropology in 2013

The year 2013 was a fruitful year for the study of Ecological Anthropology in China . More than one hundred related papers were been published in 2013 . They can be roughly divided into three categorie...