Adaptive LASSO for linear mixed model selection via profile log-likelihood

Mixed model selection is quite important in statistical literature. To assist the mixed model selection, we employ the adaptive LASSO penalized term to propose a two-stage selection procedure for the purpose of choosing both the random and fixed effects. In the first stage, we utilize the penalized restricted profile log-likelihood to choose the random effects; in the second stage, after the random effects are determined, we apply the penalized profile log-likelihood to select the fixed effects. In each stage, the Newton–Raphson algorithm is performed to complete the parameter estimation. We prove that the proposed procedure is consistent and possesses the oracle properties. The simulations and a real data application are conducted for demonstrating the effectiveness of the proposed selection procedure.     

Robust image segmentation via Bayesian type criterion

Image segmentation plays an important role in image processing before image recognition or compression. Many segmentation solutions follow the information theoretic criteria and often have excellent results; however, they are not robust to reduce the noise effect in contaminated image data. To guarantee the optimal segmentation with possible noise, a robust Bayesian information criterion is proposed to segment a grayscale image and it is less sensitive to noise. The asymptotic properties are also studied. Monte Carlo numerical experiments along with a brain magnetic resonance image are conducted to evaluate the performance of the new method.     

Efficient estimation for time-varying coefficient longitudinal models

For estimation of time-varying coefficient longitudinal models, the widely used local least-squares (LS) or covariance-weighted local LS smoothing uses information from the local sample average. Motivated by the fact that a combination of multiple quantiles provides a more complete picture of the distribution, we investigate quantile regression-based methods to improve efficiency by optimally combining information across quantiles. Under the working independence scenario, the asymptotic variance of the proposed estimator approaches the Cramér–Rao lower bound. In the presence of dependence among within-subject measurements, we adopt a prewhitening technique to transform regression errors into independent innovations and show that the prewhitened optimally weighted quantile average estimator asymptotically achieves the Cramér–Rao bound for the independent innovations. Fully data-driven bandwidth selection and optimal weights estimation are implemented through a two-step procedure. Monte Carlo studies show that the proposed method delivers more robust and superior overall performance than that of the existing methods.     

Applying statistical tests to empirically compare tabu search parameters for MAX 3-SATISFIABILITY: A case study

The application of stochastic heuristic, like tabu search or simulated annealing, to address hard discrete optimization problems has been an important advance for efficiently obtaining good solutions in a reasonable amount of computing time. A challenge when applying such heuristics is assessing when a particular set of parameter values yields better performance compared to other such sets of parameter values. For example, it can be difficult to determine the optimal mix of memory types to incorporate into tabu search. This in turn prompts users to undertake a trial and error phase to determine the best combination of parameter settings for the problem under study. Moreover, for a given problem instance, one set of heuristic parameter settings may yield a better solution than another set of parameters, for a given initial solution. However, the performance of this heuristic on this instance for a single heuristic execution is not sufficient to assert that the first set of parameter settings will always produce superior results than the second set of parameters, for all initial solutions.     

A study on tuning parameter selection for the high-dimensional lasso

High-dimensional predictive models, those with more measurements than observations, require regularization to be well defined, perform well empirically, and possess theoretical guarantees. The amount of regularization, often determined by tuning parameters, is integral to achieving good performance. One can choose the tuning parameter in a variety of ways, such as through resampling methods or generalized information criteria. However, the theory supporting many regularized procedures relies on an estimate for the variance parameter, which is complicated in high dimensions. We develop a suite of information criteria for choosing the tuning parameter in lasso regression by leveraging the literature on high-dimensional variance estimation. We derive intuition showing that existing information-theoretic approaches work poorly in this setting. We compare our risk estimators to existing methods with an extensive simulation and derive some theoretical justification. We find that our new estimators perform well across a wide range of simulation conditions and evaluation criteria.     

我国公开选拔领导干部制度的发展与完善

公开选拔领导干部制度正逐步成为一种全国性、主体性的干部选拔任用制度.但是,伴随公开选拔领导干部制度的深入发展,实践中出现的职位确定与资格准入条件设置不合理、测评方法不科学、成本过高等诸多困境依然存在.因此,只有不断完善职位定位机制、测评机制、考察评价机制、监督机制及流动机制,才有可能促进制度朝更科学的方向发展.     

崔融对唐诗的三大影响

初唐诗人崔融,至今还没有任何论著对他进行全面、综合的研究。他对唐诗有三个方面突出的影响：完全合律的七言排律是初唐时期独一无二的作品,这样复杂的诗体也完全合律,可说明很多问题;编选《珠英学士集》体现了兼收并蓄的审美倾向,开启了唐诗选本的一个时代;代表国家所作的《唐朝新定诗体》,对格律诗的最终定型有规范的理论指导作用。     

职业技术学校学生的择业心理问题及教育对策

针对职业技术学校的学生在择业与就业的问题上存在着自卑心理、闭锁心理、倚赖退缩心理、紧张焦虑心理和思维定式心理等心理问题。职业学校必须结合社会需求和学生的实际情况,教育学生克服不良的心理状态,树立市场择业新观念,引导学生走出择业心理误区,排除心理障碍,树立正确的择业观。     

科技类评审中项目选择的两阶段综合集成方法

科技类评审中,如何合理地选择出若干优秀项目加以资助或激励至关重要。本文研究了科技类评审中的项目选择问题,指出在目前通行的两阶段评议法中存在通讯评议阶段的决策信息不能有效传递到会议评议阶段的情况,应采用适当的方法将两个阶段专家给出的评价信息进行综合处理,以得到更为合理的项目评审结果,进而给出了两阶段综合集成的框架与具体方法。算例表明,可以更充分的利用两阶段的专家评价信息,得到满意的选择结果。