Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria

There is currently much discussion about lasso-type regularized regression which is a useful tool for simultaneous estimation and variable selection. Although the lasso-type regularization has several advantages in regression modelling, owing to its sparsity, it suffers from outliers because of using penalized least-squares methods. To overcome this issue, we propose a robust lasso-type estimation procedure that uses the robust criteria as the loss function, imposing L₁-type penalty called the elastic net. We also introduce to use the efficient bootstrap information criteria for choosing optimal regularization parameters and a constant in outlier detection. Simulation studies and real data analysis are given to examine the efficiency of the proposed robust sparse regression modelling. We observe that our modelling strategy performs well in the presence of outliers.     

Bootstrap forecast intervals for asymmetric volatilities via EGARCH model

Bootstrap forecast intervals are developed for volatilities having asymmetric features, which are accounted for by fitting EGARCH models. A Monte-Carlo simulation compares the proposed forecast intervals with those based on GARCH fittings which ignore asymmetry. The comparison reveals substantial advantage of addressing asymmetry through EGARCH fitting over ignoring it as the conventional GARCH forecast. The EGARCH forecast intervals have empirical coverage probabilities closer to the nominal level and/or have shorter average lengths than the GARCH forecast intervals. The finding is also supported by real dataset analysis of Dow–Jones index and financial times stock exchange (FTSE) 100 index.     

战后日本的科技研发思路及资金保障

第二次世界大战后,日本制定了具有本国特色的科技政策,并将科技政策纳入国家政策体系之中。其中,科技研发思路是制定科技政策的关键性步骤;政府对科技研发的保障性投入,也属于科技政策的重要组成部分。日本是当今世界科技和经济大国,探析并总结其战后科技研发思路及资金保障的经验教训,对后进国家制定科技发展战略无疑具有重要的借鉴和启示作用。     

论高师体育教育专业健美操普修课多元化发展

从健美操运动发展的实际需要出发,针对目前高等师范院校体育教育专业传统健美操普修课教学所存在的问题,提出了根据社会需求的多样化走多元化发展道路的改革思路,并从课程的多元化发展设计的角度,分别就课程目标的多元化、教学途径多边化、授课类型多样化、教学内容多样化、教学评价综合化等进行了阐述。     

新疆产业结构变迁与恩格尔系数协整关系的实证研究

通过构造一个由消费结构、城镇居民收入水平、第二产业结构和第三产业结构四个变量组成的VAR模型,对新疆城乡居民的消费结构与产业结构相互作用的关系及其动态特征进行了实证研究。根据设定的模型、格兰杰因果检验、脉冲响应函数、预测方差分解和协整检验,发现新疆城镇居民消费结构会影响第二产业结构的转换,随着居民收入水平的提高,恩格尔系数呈下降趋势。城乡居民收入水平对产业结构升级产生一定影响。     

滇西北民居客栈游客满意度研究——以丽江古城、大理古城和香格里拉古城为例

以丽江古城、大理古城和香格里拉古城的民居客栈的发展为例，从古城环境、住宿、交通、餐饮、娱乐、购物几方面进行了游客满意度的对比分析，针对目前存在的突出问题，提出了改善交通条件、提升服务质量、提供特色餐饮、三地协同发展等建议。     

Piecewise FARIMA models for long-memory time series

We consider the problem of modelling a long-memory time series using piecewise fractional autoregressive integrated moving average processes. The number as well as the locations of structural break points (BPs) and the parameters of each regime are assumed to be unknown. A four-step procedure is proposed to find out the BPs and to estimate the parameters of each regime. Its effectiveness is shown by Monte Carlo simulations and an application to real traffic data modelling is considered.     

Using agency data for evidence-based programming: a university-agency collaboration

Agencies providing residential treatment are encouraged, or even mandated, to collect outcomes data and to implement evidence based practices, yet little guidance has been provided on how to do so using agency administrative data that are collected on an ongoing basis. We examined data on Child and Adolescent Functional Assessment Scale (CAFAS) scores for 1608 admissions to a residential treatment center from 2002 through 2008. CAFAS scores were measured every 90 days, providing multiple CAFAS scores for each individual. Results demonstrated that on average residents improved in functioning over time. Sensitive to the evolving needs of residents who had been entering the program, the treatment center made significant program changes in 2006 to attempt to better serve residents through a broad array of specialized programming. Compared to the overall results, the analysis suggested that residents who entered the program since October 2006 appeared to have made larger improvements in their CAFAS scores. Results were derived by employing multilevel models appropriate for estimating growth trajectories with repeated measures data. Conversations with agency staff suggested that using administrative data, and advanced statistical models, were extremely helpful for organizational decision making and evidence-based programming.     

Inferences on functions of Pareto parameters with application to income inequality measures

The Theil, Pietra, Éltetö and Frigyes measures of income inequality associated with the Pareto distribution function are expressed in terms of parameters defining the Pareto distribution. Inference procedures based on the generalized variable method, the large sample method, and the Bayesian method for testing of, and constructing confidence interval for, these measures are discussed. The results of Monte Carlo study are used to compare the performance of the suggested inference procedures from a population characterized by a Pareto distribution.     

Modification of GEE1 and linear mixed-effects models for heteroscedastic longitudinal Gaussian data

A characterization of GLMs is given. Modification of the Gaussian GEE1, modified GEE1, was applied to heteroscedastic longitudinal data, to which linear mixed-effects models are usually applied. The modified GEE1 models scale multivariate data to homoscedastic data maintaining the correlation structure and apply usual GEE1 to homoscedastic data, which needs no-diagnostics for diagonal variances. Relationships among multivariate linear regression methods, ordinary/generalized LS, naïve/modified GEE1, and linear mixed-effects models were discussed. An application showed modified GEE1 gave most efficient parameter estimation. Correct specification of the main diagonals of heteroscedastic data variance appears to be more important for efficient mean parameter estimation.