<span style="font-family:Verdana;">In the applications of Tobit regression models we always encounter the data sets which contain too many variables that only a few of them contribute to the model. The...<span style="font-family:Verdana;">In the applications of Tobit regression models we always encounter the data sets which contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “non-effective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model by using an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size based on Tobit regression model. Fixed design is considered for numerical simulation.</span>展开更多
[目的/意义]探讨论文被引频次的影响因素及其关系,有助于在计量评价过程中合理地使用基于被引频次的计量指标。[方法/过程]以Web of Science数据库2000年发表的所有论文为基础,利用Tobit多元回归模型探索作者数、机构数、跨学科性、国...[目的/意义]探讨论文被引频次的影响因素及其关系,有助于在计量评价过程中合理地使用基于被引频次的计量指标。[方法/过程]以Web of Science数据库2000年发表的所有论文为基础,利用Tobit多元回归模型探索作者数、机构数、跨学科性、国内合作、国际合作和参考文献数量6个常见的影响因素对论文被引频次的影响。[结果/结论]研究发现机构数存在明显的多重共线性,因此进一步通过全子集回归分析筛选最佳回归模型。研究结果表明,作者数、机构数、跨学科性、国际合作和参考文献数量对论文被引频次有积极的影响,其中国内合作对被引频次的影响力很微弱,容易受到机构数的干扰,而机构数和参考文献数量是论文特征中影响被引频次最重要的因素。展开更多
文摘<span style="font-family:Verdana;">In the applications of Tobit regression models we always encounter the data sets which contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “non-effective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model by using an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size based on Tobit regression model. Fixed design is considered for numerical simulation.</span>
文摘[目的/意义]探讨论文被引频次的影响因素及其关系,有助于在计量评价过程中合理地使用基于被引频次的计量指标。[方法/过程]以Web of Science数据库2000年发表的所有论文为基础,利用Tobit多元回归模型探索作者数、机构数、跨学科性、国内合作、国际合作和参考文献数量6个常见的影响因素对论文被引频次的影响。[结果/结论]研究发现机构数存在明显的多重共线性,因此进一步通过全子集回归分析筛选最佳回归模型。研究结果表明,作者数、机构数、跨学科性、国际合作和参考文献数量对论文被引频次有积极的影响,其中国内合作对被引频次的影响力很微弱,容易受到机构数的干扰,而机构数和参考文献数量是论文特征中影响被引频次最重要的因素。
