Inference after separated hypotheses testing: an empirical investigation for linear models

Model selection problems arise while constructing unbiased or asymptotically unbiased estimators of measures known as discrepancies to find the best model. Most of the usual criteria are based on goodness-of-fit and parsimony. They aim to maximize a transformed version of likelihood. For linear regression models with normally distributed error, the situation is less clear when two models are equivalent: are they close to or far from the unknown true model? In this work, based on stochastic simulation and parametric simulation, we study the results of Vuong's test, Cox's test, Akaike's information criterion, Bayesian information criterion, Kullback information criterion and bias corrected Kullback information criterion and the ability of these tests to discriminate between non-nested linear models.     

Adaptive order selection for autoregressive models

Autoregressive model is a popular method for analysing the time dependent data, where selection of order parameter is imperative. Two commonly used selection criteria are the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), which are known to suffer the potential problems regarding overfit and underfit, respectively. To our knowledge, there does not exist a criterion in the literature that can satisfactorily perform under various situations. Therefore, in this paper, we focus on forecasting the future values of an observed time series and propose an adaptive idea to combine the advantages of AIC and BIC but to mitigate their weaknesses based on the concept of generalized degrees of freedom. Instead of applying a fixed criterion to select the order parameter, we propose an approximately unbiased estimator of mean squared prediction errors based on a data perturbation technique for fairly comparing between AIC and BIC. Then use the selected criterion to determine the final order parameter. Some numerical experiments are performed to show the superiority of the proposed method and a real data set of the retail price index of China from 1952 to 2008 is also applied for illustration.     

我国刑事法律援助制度的几个问题及完善——以国际刑事司法准则为视角

法律援助制度是法律程序公正的体现,通过实证考察法律援助的实施状况,从立法方面提出了法律援助范围过窄、可能判处死刑案件的援助阶段缺失以及实践中法律援助的责任主体不适当三个问题,继而研究国际刑事司法准则在这三个问题上设立的国际标准,通过比较提出对我国刑事法律援助制度改善的一些建议。     

对我国“犯罪既遂标准说”的反思——“犯罪对象侵害说”之倡导

"构成要件齐备说"作为犯罪既遂的认定标准存在重大缺陷。王志祥教授、徐光华博士为"构成要件齐备说"所进行的辩护难以奏效。学界对"犯罪目的实现说"、"犯罪结果发生说"还存在着重大误读。新近出现的"类型化标准说"提倡不同的犯罪类型适用不同的既遂标准,其旨在增强犯罪既遂标准理论的实践功效。可是,该学说不仅未跳出"构成要件齐备说"的藩篱,而且又制造了一些新的"麻烦"。当下的"犯罪既遂标准说"存有"立场错误"、"操作性差"等方面的问题,需要我们构建新的"犯罪既遂标准说"———"犯罪对象侵害说"。     

背离与融合:我国逮捕制度的功能与程序

从逮捕功能与程序设置关系的角度进行分析,是研究我国逮捕制度改革的全新视角.我国逮捕制度的预设功能在于,对逮捕进行事前的外部审查,决定是否长期羁押,这种“事前预防”功能应当与审查逮捕程序的“低标准”对应,以实现保护人身自由与保障侦查的平衡.但是,由于我国审查逮捕程序设置的“高标准”,批捕程序无法迅速控制犯罪嫌疑人,导致“事前预防”功能和羁押必要性审查机制的虚置.改革应当着眼于制度功能与程序配置的融合,降低审查逮捕的程序标准,以实现逮捕的功能.     

An efficient and fast algorithm for estimating the parameters of two-dimensional sinusoidal signals

In this paper we propose a computationally efficient algorithm to estimate the parameters of a 2-D sinusoidal model in the presence of stationary noise. The estimators obtained by the proposed algorithm are consistent and asymptotically equivalent to the least squares estimators. Monte Carlo simulations are performed for different sample sizes and it is observed that the performances of the proposed method are quite satisfactory and they are equivalent to the least squares estimators. The main advantage of the proposed method is that the estimators can be obtained using only finite number of iterations. In fact it is shown that starting from the average of periodogram estimators, the proposed algorithm converges in three steps only. One synthesized texture data and one original texture data have been analyzed using the proposed algorithm for illustrative purpose.     

政府绩效审计中的绩效标准应用思路及取向

绩效标准是政府绩效审计的核心。文章构建了一个包括“效果性”、“效率性”、“正当性”、“公平性”、“透明性”、“回应性”标准在内的完整的政府绩效评价标准体系,作为政府绩效水平定性评价的依据,在此基础上构建系列适合被审计单位或项目的评价指标,采用数学方法则可以实现对被审计单位或项目绩效水平的定量评价。      

Kernel partial correlation: a novel approach to capturing conditional independence in graphical models for noisy data

Graphical models capture the conditional independence structure among random variables via existence of edges among vertices. One way of inferring a graph is to identify zero partial correlation coefficients, which is an effective way of finding conditional independence under a multivariate Gaussian setting. For more general settings, we propose kernel partial correlation which extends partial correlation with a combination of two kernel methods. First, a nonparametric function estimation is employed to remove effects from other variables, and then the dependence between remaining random components is assessed through a nonparametric association measure. The proposed approach is not only flexible but also robust under high levels of noise owing to the robustness of the nonparametric approaches.     

Selecting the regularization parameters in high-dimensional panel data models: Consistency and efficiency

This article considers panel data models in the presence of a large number of potential predictors and unobservable common factors. The model is estimated by the regularization method together with the principal components procedure. We propose a panel information criterion for selecting the regularization parameter and the number of common factors under a diverging number of predictors. Under the correct model specification, we show that the proposed criterion consistently identifies the true model. If the model is instead misspecified, the proposed criterion achieves asymptotically efficient model selection. Simulation results confirm these theoretical arguments.     

Addition of runs to an s-level supersaturated design

The purpose of the present work is to extend the work of Gupta et al. (2010) to s  -level column balanced supersaturated designs. Addition of runs to an existing E(χ²)-optimal$E({\chi }^2)\mathrm{-}\mathrm{optimal}$ supersaturated design and to study the optimality of the resulting design is an important issue. This paper considers the study of the optimality of the resulting design. A lower bound to E(χ²)$E({\chi }^2)$ has been obtained for the extended supersaturated designs. Some examples and a small catalogue of E(χ²)-optimal$E({\chi }^2)\mathrm{-}\mathrm{optimal}$ supersaturated designs are also presented.