In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
Jiang Shaohong is my best friend.He is 14years old and he is in Class 1,Grade 8.He is clever and can learn things quickly.He works hard at all his subjects.He has many hobbies,such as reading,singing,dancing,and playi...Jiang Shaohong is my best friend.He is 14years old and he is in Class 1,Grade 8.He is clever and can learn things quickly.He works hard at all his subjects.He has many hobbies,such as reading,singing,dancing,and playing sports.He loves animals,so he keeps a small dog as a pet and takes good care of it at home.He is kind and helpful.He often helps his classmates with their studies.展开更多
I have a best friend・His name is Li Xuan・We live in the same neighborhood and go to the same school.Li Xuan is a kind and clever boy.He always smiles and is ready to help others.One day,I had a problem with my math ho...I have a best friend・His name is Li Xuan・We live in the same neighborhood and go to the same school.Li Xuan is a kind and clever boy.He always smiles and is ready to help others.One day,I had a problem with my math homework and I was very worried.展开更多
目的:调查BEST(better&systematic team training)培训法在护士PDA静脉输液查对培训中的效果。方法:采用BEST培训法,包括理论授课+讨论+操作练习+反馈性汇报+录像回放+分析总结流程等对护士进行查对培训,并调查培训的效果。结果:护...目的:调查BEST(better&systematic team training)培训法在护士PDA静脉输液查对培训中的效果。方法:采用BEST培训法,包括理论授课+讨论+操作练习+反馈性汇报+录像回放+分析总结流程等对护士进行查对培训,并调查培训的效果。结果:护士长及骨干和科室普通护士在培训后视频纠错的得分均高于培训前(P<0.001);科室护士查对培训前后得分差异与学历(r=-0.167,P=0.007)、年龄(r=-0.136,P=0.030)、工作年限(r=-0.154,P=0.014)呈负相关,即学历越低,年龄越小,工作年限越短,培训前后得分差异越大,效果越好。结论:BEST训练法能有效提高护士,尤其是低年资护士的查对意识,强化操作流程,是一种理想、科学的在职操作培训方法。展开更多
The problem of strong uniqueness of best approximation from an RS set in a Banach space is considered. For a fixed RS set G and an element x∈X , we proved that the best approximation g * to x from ...The problem of strong uniqueness of best approximation from an RS set in a Banach space is considered. For a fixed RS set G and an element x∈X , we proved that the best approximation g * to x from G is strongly unique.展开更多
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
文摘Jiang Shaohong is my best friend.He is 14years old and he is in Class 1,Grade 8.He is clever and can learn things quickly.He works hard at all his subjects.He has many hobbies,such as reading,singing,dancing,and playing sports.He loves animals,so he keeps a small dog as a pet and takes good care of it at home.He is kind and helpful.He often helps his classmates with their studies.
文摘I have a best friend・His name is Li Xuan・We live in the same neighborhood and go to the same school.Li Xuan is a kind and clever boy.He always smiles and is ready to help others.One day,I had a problem with my math homework and I was very worried.
文摘目的:调查BEST(better&systematic team training)培训法在护士PDA静脉输液查对培训中的效果。方法:采用BEST培训法,包括理论授课+讨论+操作练习+反馈性汇报+录像回放+分析总结流程等对护士进行查对培训,并调查培训的效果。结果:护士长及骨干和科室普通护士在培训后视频纠错的得分均高于培训前(P<0.001);科室护士查对培训前后得分差异与学历(r=-0.167,P=0.007)、年龄(r=-0.136,P=0.030)、工作年限(r=-0.154,P=0.014)呈负相关,即学历越低,年龄越小,工作年限越短,培训前后得分差异越大,效果越好。结论:BEST训练法能有效提高护士,尤其是低年资护士的查对意识,强化操作流程,是一种理想、科学的在职操作培训方法。
文摘The problem of strong uniqueness of best approximation from an RS set in a Banach space is considered. For a fixed RS set G and an element x∈X , we proved that the best approximation g * to x from G is strongly unique.
