把《统计与信息论坛》建设成高校哲学社会科学名刊——《统计与信息论坛》名刊建设笔谈摘登

首次入选CSSCI来源期刊，标志着《统计与信息论坛》步入了新的发展阶段。面对新的机遇与挑战，《统计与信息论坛》编辑部组织了本次笔谈，对《统计与信息论坛》未来的办刊理念、期刊定位、办刊特色、栏目设置、稿件内容、编排规范等进行广泛讨论。本次笔谈得到了广大编委和部分作者的积极支持，收到了许多宝贵的意见和建议。这些宝贵的建议将对本刊今后的发展起到积极的推动作用。这里摘登部分内容，以期广大关心本刊发展的朋友们对期刊的发展进一步建言献策。此外，天津财经大学肖红叶教授、东北财经大学蒋萍教授、中国人民大学易丹辉教授、首都经济与贸易大学纪宏教授等也对本刊发展给予了充分肯定，在此表示衷心的感谢。     

祝贺与期望

<正>作为一名《统计与信息论坛》的忠实读者,我有幸见证了《统计与信息论坛》的发展历程。从最初未进C刊的默默无闻,到励精图治、严控质量,发展为现在的知名核心期刊,受到学术界普遍的赞誉和好评。我的几次投稿,编辑部给我的感受是不仅回复快,而且精益求精,给出许多中肯的意见,使文章不断完善。可以说,《统计与信息论坛》能取得今天的辉煌成绩,最离不开的是编辑部老师和审稿人的辛     

《统计与信息论坛》入选“2013中国国际影响力优秀学术期刊”

<正>由中国学术期刊(光盘版)电子杂志社、清华大学图书馆、中国学术文献国际评价研究中心完成的"2013中国最具国际影响力学术期刊"、"2013中国国际影响力优秀学术期刊"遴选工作已经完成,《中国新闻出版报》2013年12月30日发布研究报告。《统计与信息论坛》入选"2013中国国际影响力优秀学术期刊"。"2013中国最具国际影响力学术期刊"、"2013中国国际影响力优秀学术期刊"从我国1 120种人文社会科学期刊中分别遴选出各56种,从3 502种自然科学与工程技术类期刊中分别遴选出各175种,前者为     

《统计与信息论坛》再次入选“2014中国国际影响力优秀学术期刊”

<正>2014年12月16日,2014年中国最具影响力学术期刊暨中国学术期刊国际、国内引证报告发布会在京举行,《统计与信息论坛》继入选"2013中国国际影响力优秀学术期刊"后,今年再次入选该榜单。中国学术文献国际评价研究中心从2012年开始对我国学术期刊的国际影响力进行评价研究。该中心以美国汤森路透Web of Science收录的12000余种期刊为引文统计源,采用国际影响力综合评价指标CI对中国期刊进行排序,     

《统计与信息论坛》再次入选“2014中国国际影响力优秀学术期刊”

<正>2014年12月16日,2014年中国最具影响力学术期刊暨中国学术期刊国际、国内引证报告发布会在京举行,《统计与信息论坛》继入选"2013中国国际影响力优秀学术期刊"后,今年再次入选该榜单。中国学术文献国际评价研究中心从2012年开始对我国学术期刊的国际影响力进行评价研究。该中心以美国汤森路透Web of Science收录的12 000余种期刊为引文统计源,采用国际影响力综合评价指标CI对中国期刊进行排序,将排序前10%的期刊评定为"中国最具国际影响力学术期刊"。     

投稿须知

<正>《统计与信息论坛》为公开发行的统计类专业学术期刊。集学术性、实用性于一体,主要刊登有创新性、探索性的学术论文。开设的栏目有:统计理论与方法、统计应用研究、统计调查与分析等。本刊被《中国学术期刊(光盘版)》《中国期刊网》全文收录;在《万方数据-数字化期刊群》和北京世纪超星信息技术发展有限公司的学术期刊"域出版"中全文上网,被《中国核心期刊(遴选)数据库》收录;加入《中文科技期刊     

投稿须知

<正>《统计与信息论坛》为公开发行的统计类专业学术期刊。集学术性、实用性于一体,主要刊登有创新性、探索性的学术论文。开设的栏目有:统计理论与方法、统计应用研究、统计调查与分析等。本刊被《中国学术期刊(光盘版)》《中国期刊网》全文收录;在《万方数据一数字化期刊群》和北京世纪超星信息技术发展有限公司的学术期刊"域出版"中全文上网,被《中国核心期刊(遴选)数据库》收录;加     

感谢审稿专家

<正>尊敬的审稿专家:各位好!一份好的学术期刊是主办单位、编委、审稿专家、编辑、作者、读者精诚合作、齐心协力、共同努力的结晶。学术质量与水平是期刊的生命之根,质量的好坏、水平的高低,关键在于审稿专家的工作质量。长期以来,你们以严谨的治学态度和无私的奉献精神,为《统计与信息论坛》鼓实劲、出实力,默默付出了许多心血和劳动,为《统计与信息论坛》的发展做出了巨大贡献。因为有你们保驾护航,《统计与信息论坛》由无名小草成长为参天大树,跻身于中国优秀学术期刊行列。在此,向各位专家表示崇高的敬意和衷心的感谢!     

我校《统计与信息论坛》入选“2018中国国际影响力优秀学术期刊”

<正>2018年12月,《中国学术期刊国际引证年报》正式发布,我校《统计与信息论坛》入选"2018年中国国际影响力优秀学术期刊"(人文社会科学)。这是《统计与信息论坛》继2013年首次入选之后第4次入选。《中国学术期刊国际引证年报》由中国学术文献国际评价研究中心和清华大学图书馆联合研制,自2012年     

投稿须知

《统计与信息论坛》是公开发行的统计类学术期刊。集学术性、实用性于一体,主要刊登有创新性、探索性的学术论文。开设的栏目有：统计理论与方法、统计应用研究、统计调查与分析等。本刊被《中国学术期刊（光盘版）》《中国期刊网》全文收录;在《万方数据—数字化期刊群》全文上网,被《中国核心期刊（遴选）数据库》收录;加入《中文科技期刊数据库》。     

2<Superscript><Emphasis Type="Italic">m</Emphasis></Superscript>4<Superscript>1</Superscript> designs with minimum aberration or weak minimum aberration

For measuring the goodness of 2 ^m 4¹ designs, Wu and Zhang (1993) proposed the minimum aberration (MA) criterion. MA 2 ^m 4¹ designs have been constructed using the idea of complementary designs when the number of two-level factors, m, exceeds n/2, where n is the total number of runs. In this paper, the structures of MA 2 ^m 4¹ designs are obtained when m>5n/16. Based on these structures, some methods are developed for constructing MA 2 ^m 4¹ designs for 5n/16<m<n/2 as well as for n/2≤m<n. When m≤5n/16, there is no general method for constructing MA 2 ^m 4¹ designs. In this case, we obtain lower bounds for A ₃₀ and A ₃₁, where A ₃₀ and A ₃₁ are the numbers of type 0 and type 1 words with length three respectively. And a method for constructing weak minimum aberration (WMA) 2 ^m 4¹ designs (A ₃₀ and A ₃₁ achieving the lower bounds) is demonstrated. Some MA or WMA 2 ^m 4¹ designs with 32 or 64 runs are tabulated for practical use, which supplement the tables in Wu and Zhang (1993), Zhang and Shao (2001) and Mukerjee and Wu (2001).     

Paired permutation testing in 2<Superscript><Emphasis Type="Italic">k</Emphasis></Superscript> unreplicated factorials

Exact permutation testing of effects in unreplicated two-level multifactorial designs is developed based on the notion of realigning observations and on paired permutations. This approach preserves the exchangeability of error components for testing up tok effects. Advantages and limitations of exact permutation procedures for unreplicated factorials are discussed and a simulation study on paired permutation testing is presented.     

A projection pursuit index for large <Emphasis Type="Italic">p</Emphasis> small <Emphasis Type="Italic">n</Emphasis> data

In high-dimensional data, one often seeks a few interesting low-dimensional projections which reveal important aspects of the data. Projection pursuit for classification finds projections that reveal differences between classes. Even though projection pursuit is used to bypass the curse of dimensionality, most indexes will not work well when there are a small number of observations relative to the number of variables, known as a large p (dimension) small n (sample size) problem. This paper discusses the relationship between the sample size and dimensionality on classification and proposes a new projection pursuit index that overcomes the problem of small sample size for exploratory classification.     

Distribution of extremes of<Emphasis Type="Italic">r</Emphasis><Superscript>th</Superscript> concomitant from the Morgenstern family

In the present paper the distribution theory of maximum and minimum of ther ^th concomitants from k independent subgroups each of same size m from the Morgenstern family is investigated. Some applications of the results in estimation of the scale parameter of a marginal variable in the bivariate uniform distribution and a selection problem are discussed.     

Extending mixtures of multivariate <Emphasis Type="Italic">t</Emphasis>-factor analyzers

Model-based clustering typically involves the development of a family of mixture models and the imposition of these models upon data. The best member of the family is then chosen using some criterion and the associated parameter estimates lead to predicted group memberships, or clusterings. This paper describes the extension of the mixtures of multivariate t-factor analyzers model to include constraints on the degrees of freedom, the factor loadings, and the error variance matrices. The result is a family of six mixture models, including parsimonious models. Parameter estimates for this family of models are derived using an alternating expectation-conditional maximization algorithm and convergence is determined based on Aitken’s acceleration. Model selection is carried out using the Bayesian information criterion (BIC) and the integrated completed likelihood (ICL). This novel family of mixture models is then applied to simulated and real data where clustering performance meets or exceeds that of established model-based clustering methods. The simulation studies include a comparison of the BIC and the ICL as model selection techniques for this novel family of models. Application to simulated data with larger dimensionality is also explored.     

Comparisons of the <Emphasis Type="Italic">r</Emphasis> − <Emphasis Type="Italic">k</Emphasis> class estimator to the ordinary least squares estimator under the Pitman’s closeness criterion

In the presence of multicollinearity, the r − k class estimator is proposed as an alternative to the ordinary least squares (OLS) estimator which is a general estimator including the ordinary ridge regression (ORR), the principal components regression (PCR) and the OLS estimators. Comparison of competing estimators of a parameter in the sense of mean square error (MSE) criterion is of central interest. An alternative criterion to the MSE criterion is the Pitman’s (1937) closeness (PC) criterion. In this paper, we compare the r − k class estimator to the OLS estimator in terms of PC criterion so that we can get the comparison of the ORR estimator to the OLS estimator under the PC criterion which was done by Mason et al. (1990) and also the comparison of the PCR estimator to the OLS estimator by means of the PC criterion which was done by Lin and Wei (2002).     

Robust mixture modeling using the skew <Emphasis Type="Italic">t</Emphasis> distribution

A finite mixture model using the Student's t distribution has been recognized as a robust extension of normal mixtures. Recently, a mixture of skew normal distributions has been found to be effective in the treatment of heterogeneous data involving asymmetric behaviors across subclasses. In this article, we propose a robust mixture framework based on the skew t distribution to efficiently deal with heavy-tailedness, extra skewness and multimodality in a wide range of settings. Statistical mixture modeling based on normal, Student's t and skew normal distributions can be viewed as special cases of the skew t mixture model. We present analytically simple EM-type algorithms for iteratively computing maximum likelihood estimates. The proposed methodology is illustrated by analyzing a real data example.