Block diagrams and splitting criteria for classification trees

Various aspects of the classification tree methodology of Breiman et al., (1984) are discussed. A method of displaying classification trees, called block diagrams, is developed. Block diagrams give a clear presentation of the classification, and are useful both to point out features of the particular data set under consideration and also to highlight deficiencies in the classification method being used. Various splitting criteria are discussed; the usual Gini-Simpson criterion presents difficulties when there is a relatively large number of classes and improved splitting criteria are obtained. One particular improvement is the introduction of adaptive anti-end-cut factors that take advantage of highly asymmetrical splits where appropriate. They use the number and mix of classes in the current node of the tree to identify whether or not it is likely to be advantageous to create a very small offspring node. A number of data sets are used as examples.     

Second‐order bias‐corrected AIC in multivariate normal linear models under non‐normality

This paper deals with a bias correction of Akaike's information criterion (AIC) for selecting variables in multivariate normal linear regression models when the true distribution of observation is an unknown non‐normal distribution. It is well known that the bias of AIC is $O(1)$ , and there are a number of the first‐order bias‐corrected AICs which improve the bias to $O(n^{-1})$ , where $n$ is the sample size. A new information criterion is proposed by slightly adjusting the first‐order bias‐corrected AIC. Although the adjustment is achieved by merely using constant coefficients, the bias of the new criterion is reduced to $O(n^{-2})$ . Then, a variance of the new criterion is also improved. Through numerical experiments, we verify that our criterion is superior to others. The Canadian Journal of Statistics 39: 126–146; 2011 © 2011 Statistical Society of Canada     

从绩效评估标准看澳大利亚地方政府管理理念及特点

政府绩效评估标准是政府管理理念和特点的反映。以澳大利亚新南威尔士州绩效评估标准为研究对象,分析其在政府部门管理的宏观把握途径、以服务为结果的工作方法、对财政经费的内外监控手段和借鉴商业部门管理经验等方面的特点,以期为我国地方政府管理体系的改革提供借鉴。     

Maximum expected entropy transformed Latin hypercube designs

Existing projection designs (e.g. maximum projection designs) attempt to achieve good space-filling properties in all projections. However, when using a Gaussian process (GP), model-based design criteria such as the entropy criterion is more appropriate. We employ the entropy criterion averaged over a set of projections, called expected entropy criterion (EEC), to generate projection designs. We show that maximum EEC designs are invariant to monotonic transformations of the response, i.e. they are optimal for a wide class of stochastic process models. We also demonstrate that transformation of each column of a Latin hypercube design (LHD) based on a monotonic function can substantially improve the EEC. Two types of input transformations are considered: a quantile function of a symmetric Beta distribution chosen to optimize the EEC, and a nonparametric transformation corresponding to the quantile function of a symmetric density chosen to optimize the EEC. Numerical studies show that the proposed transformations of the LHD are efficient and effective for building robust maximum EEC designs. These designs give projections with markedly higher entropies and lower maximum prediction variances (MPV''s) at the cost of small increases in average prediction variances (APV''s) compared to state-of-the-art space-filling designs over wide ranges of covariance parameter values.     

The impact of rice research expenditure policy in Thailand

This study explored the economic, social and environmental impact of Thai rice research expenditure (categorized as breeding expenditure, production expenditure and processing expenditure) during 2008–2015, using the simultaneous equation modeling technique. The results showed that production-research expenditure was the most explicit to reduce the amount of nitrogen fertilizer usage, while breeding-research expenditure was the most explicit in terms of increasing farmers’ economic status from planting rice. Processing expenditure seemed to be the least effective spending among the three types.     

普通大学生有氧工作能力分析及有氧工作能力标准的制定

目的,通过12分钟跑评价在校大学生的心肺适能水平,提出提高普通大学生心肺功能的有氧工作能力的对策。方法：抽取男大学生299人,女大学生174人进行12分钟跑测试,观察大学生心肺功能现状并分析。结果：（1）学生的心肺功能逐年下降。（2）心肺功能可提升学生自信心。（3）女生平时缺乏长时间高强度的锻炼,及克服困难的意志品质有关。（4）制定出普通大学生12分钟跑测试成绩评定。结论：大学生目前的心肺功能水平尤其是女生下降明显。     

Hierarchical Bayesian meta-analysis models for cross-platform microarray studies

The development of new technologies to measure gene expression has been calling for statistical methods to integrate findings across multiple-platform studies. A common goal of microarray analysis is to identify genes with differential expression between two conditions, such as treatment versus control. Here, we introduce a hierarchical Bayesian meta-analysis model to pool gene expression studies from different microarray platforms: spotted DNA arrays and short oligonucleotide arrays. The studies have different array design layouts, each with multiple sources of data replication, including repeated experiments, slides and probes. Our model produces the gene-specific posterior probability of differential expression, which is the basis for inference. In simulations combining two and five independent studies, our meta-analysis model outperformed separate analyses for three commonly used comparison measures; it also showed improved receiver operating characteristic curves. When combining spotted DNA and CombiMatrix short oligonucleotide array studies of Geobacter sulfurreducens, our meta-analysis model discovered more genes for fixed thresholds of posterior probability of differential expression and Bayesian false discovery than individual study analyses. We also examine an alternative model and compare models using the deviance information criterion.     

Comparison of Bayesian sample size criteria: ACC, ALC, and WOC

A challenge for implementing performance-based Bayesian sample size determination is selecting which of several methods to use. We compare three Bayesian sample size criteria: the average coverage criterion (ACC) which controls the coverage rate of fixed length credible intervals over the predictive distribution of the data, the average length criterion (ALC) which controls the length of credible intervals with a fixed coverage rate, and the worst outcome criterion (WOC) which ensures the desired coverage rate and interval length over all (or a subset of) possible datasets. For most models, the WOC produces the largest sample size among the three criteria, and sample sizes obtained by the ACC and the ALC are not the same. For Bayesian sample size determination for normal means and differences between normal means, we investigate, for the first time, the direction and magnitude of differences between the ACC and ALC sample sizes. For fixed hyperparameter values, we show that the difference of the ACC and ALC sample size depends on the nominal coverage, and not on the nominal interval length. There exists a threshold value of the nominal coverage level such that below the threshold the ALC sample size is larger than the ACC sample size, and above the threshold the ACC sample size is larger. Furthermore, the ACC sample size is more sensitive to changes in the nominal coverage. We also show that for fixed hyperparameter values, there exists an asymptotic constant ratio between the WOC sample size and the ALC (ACC) sample size. Simulation studies are conducted to show that similar relationships among the ACC, ALC, and WOC may hold for estimating binomial proportions. We provide a heuristic argument that the results can be generalized to a larger class of models.     

Real-time reliability evaluation of two-phase Wiener degradation process

Reliability modeling and evaluation for the two-phase Wiener degradation process are studied. For many devices, the degradation rates could possibly increase or decrease in a non smooth manner at some point in time due to the change of degradation mechanism. A two-phase Wiener degradation process with an unobserved change point is used to model the degradation process. And we assume that the change point varies randomly from device to device. Furthermore, we integrate historical data and up-to-date observation data to improve the degradation modeling and evaluation based on Bayesian method. The change point between the two phases was obtained based on the Akaike information criterion (AIC) and the criterion of the residual sum of squares. Finally, a real example of liquid coupling devices (LCDs) and a numeric example are discussed to demonstrate the effectiveness of the proposed method. The results show that the proposed method is effective and efficient.     

Objective Bayesian hypothesis testing in regression models with first-order autoregressive residuals

This article considers the objective Bayesian testing in the normal regression models with first-order autoregressive residuals. We propose some solutions based on a Bayesian model selection procedure to this problem where no subjective input is considered. We construct the proper priors for testing the autocorrelation coefficient based on measures of divergence between competing models, which is called the divergence-based (DB) priors and then propose the objective Bayesian decision-theoretic rule, which is called the Bayesian reference criterion (BRC). Finally, we derive the intrinsic test statistic for testing the autocorrelation coefficient. The behavior of the Bayes factor-based DB priors is examined by comparing with the BRC in a simulation study and an example.