KaKs_Calculator is a software package that calculates nonsynonymous (Ka) and synonymous (Ks) substitution rates through model selection and model averaging. Since existing methods for this estimation adopt their s...KaKs_Calculator is a software package that calculates nonsynonymous (Ka) and synonymous (Ks) substitution rates through model selection and model averaging. Since existing methods for this estimation adopt their specific mutation (substitution) models that consider different evolutionary features, leading to diverse estimates, KaKs_Calculator implements a set of candidate models in a maximum likelihood framework and adopts the Akaike information criterion to measure fitness between models and data, aiming to include as many features as needed for accurately capturing evolutionary information in protein-coding sequences. In addition, several existing methods for calculating Ka and Ks are also incorporated into this software. KaKs_Calculator, including source codes, compiled executables, and documentation, is freely available for academic use at http://evolution.genomics.org.cn/software.htm.展开更多
In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity condi...In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.展开更多
本文给出了响应变量随机右删失情况下线性模型的FIC(focused information criterion)模型选择方法和光滑FIC模型平均估计方法,证明了兴趣参数的FIC模型选择估计和光滑FIC模型平均估计的渐近正态性,通过随机模拟研究了估计的有限样本性质...本文给出了响应变量随机右删失情况下线性模型的FIC(focused information criterion)模型选择方法和光滑FIC模型平均估计方法,证明了兴趣参数的FIC模型选择估计和光滑FIC模型平均估计的渐近正态性,通过随机模拟研究了估计的有限样本性质,模拟结果显示,从均方误差和一定置信水平置信区间的经验覆盖概率看,兴趣参数的光滑FIC模型平均估计均优于FIC,AIC(Akaike information criterion)和BIC(Bayesian information citerion)等模型选择估计;而FIC模型选择估计与AIC和BIC等模型选择估计相比,也表现出了一定的优越性.通过分析原发性胆汁性肝硬化数据集,说明了本文方法在实际问题中的应用.展开更多
基金grants from the Ministry of Science and Technology of China (No. 2001AA231061) the National Natural Science Foundation of China (No. 30270748)
文摘KaKs_Calculator is a software package that calculates nonsynonymous (Ka) and synonymous (Ks) substitution rates through model selection and model averaging. Since existing methods for this estimation adopt their specific mutation (substitution) models that consider different evolutionary features, leading to diverse estimates, KaKs_Calculator implements a set of candidate models in a maximum likelihood framework and adopts the Akaike information criterion to measure fitness between models and data, aiming to include as many features as needed for accurately capturing evolutionary information in protein-coding sequences. In addition, several existing methods for calculating Ka and Ks are also incorporated into this software. KaKs_Calculator, including source codes, compiled executables, and documentation, is freely available for academic use at http://evolution.genomics.org.cn/software.htm.
文摘In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.
文摘本文给出了响应变量随机右删失情况下线性模型的FIC(focused information criterion)模型选择方法和光滑FIC模型平均估计方法,证明了兴趣参数的FIC模型选择估计和光滑FIC模型平均估计的渐近正态性,通过随机模拟研究了估计的有限样本性质,模拟结果显示,从均方误差和一定置信水平置信区间的经验覆盖概率看,兴趣参数的光滑FIC模型平均估计均优于FIC,AIC(Akaike information criterion)和BIC(Bayesian information citerion)等模型选择估计;而FIC模型选择估计与AIC和BIC等模型选择估计相比,也表现出了一定的优越性.通过分析原发性胆汁性肝硬化数据集,说明了本文方法在实际问题中的应用.