1)  local Mestimation
局部M-估计
2)  one-step local M-estimation
一步局部M-估计
1.
The estimators of parametric component on partial linear models are developed by one-step local M-estimation method and average method,the estimators of nonparametric component are given by one-step M-estimation.
讨论了部分线性回归模型的一步局部M-估计
3)  local
局部
1.
Relationship between local recurrence and distant metastases in human breast cancer;
乳腺癌局部复发与远处转移关系的回顾性分析
2.
Lipid emulsion:a new remedy for cardiac toxicity induced by local anesthetics;
脂肪乳剂——局部麻醉药心脏毒性反应救治的新发现
3.
Application of local anaesthesia in tension-free inguinal hernia repair;
局部麻醉在腹股沟疝无张力修补术中的应用
4)  part
局部
1.
The importance of pedestrian street in urban partial renewal;
步行街在城市局部更新中的重要作用
2.
Discussion of processing methods in borer-probe trough checking and partial basement problems;
关于钎探验槽和地基局部问题处理方法的探讨
3.
A Kind of Fractals Based on Part of Real Number;
一类基于实数局部的分形
5)  regional
局部
1.
Analysis of the Effectiveness of Regional Infusion Combinated Hypotonic Intraperitoneal Chemohyperthermia for Prevention of Recurrence and Metastasis After Radical Recection of Gastric Carcinoma;
局部灌注结合腹腔低渗热化疗预防胃癌复发和转移的疗效分析
2.
3-D Echocardiographic Evaluation of LV Regional Function in Patients with Myocardial Infarction;
三维超声心动图评价心肌梗死患者左室局部心功能
3.
CT perfusion imaging and stages of regional cerebral hypoperfusion in pre-infarction period;
脑梗死前期脑局部低灌注的CT灌注成像表现及分期
6)  partial
局部
1.
Three-dimensional finite element analysis of stress distribution on the edentulous mucosa of separate removable partial denture and traditional removable partial denture;
分割式及普通可摘局部义齿缺牙区黏膜的三维有限元分析
2.
Three-dimensional finite element analysis of SD attachment retained distal-extension removable partial denture;
SD附着体固位远中游离端可摘局部义齿三维有限元应力分析
3.
Experience in removable partial splint dentures for elderly patients;
老年人夹板式可摘局部义齿设计体会
参考词条
补充资料:Bayes估计量


Bayes估计量
Bayesian estimator

Bayes估计量【Bayesi助始廿ma.件;D自狱.。眨..界..] 用BayeS方法(Bayesian aPProach)由观察值对一未知参数所作的估计.统计问题使用这样的方法时,一般都假定未知参数所0 gR“是一具有给定先验分布7r=武do)的随机变量,决策空间D与集合0重合.且损失L(0,d)表示变量0与估计d的偏离.因此,函数L勿,d)通常假定为有形式L勿,d)=a(e)又(口一d),其中又是误差向量0一d的某个非负函数,若k二1,则常取又勿一d)={0一d}“(“>0).最有用且在数学上最方便的是平方损失函数L(口,d)=}‘一d1’.对这一损失函数,Bayes估计量(Ba卿决策函教(Bavesian dedsion function))占’二亡厂(x)定义为使最小总损失 !;‘p‘二·“,一,‘薯必,“一”‘·’2’〕口‘么,叮‘““,达到的函数,或与之等价,了是使最小条件损失 ,母‘E{[口一占(x)]2+“)达到的函数,由此推出,在平方损失函数的场合,B竹es估计量与后验均值占‘(x)=E勿{x)相等,而Bayesj双险(Bayes risk)为 。‘二,占‘)二E!D矿夕}x)]‘此处O(0}劝是后验分布的方差: o(口{x)二任{{口一E(0{x)12!,、}. 例设二=(x,,,二,戈),这里x,,,二,x。为具正态分布N勿,。’)的独立同分布变量,护己知,而未知参数0有正态分布N扭,铲).因为当x给定时口的后验分布为正态N(拜。,T:一、其中 n又。2一十“下一2 灿。二一—,,。一二n口‘一奋了一_ n口一汁~下且万=(x,十一+凡)/。,可知在平方损失函数{分一引’之下,Bayes估计量为占’(x)=线,而Bayes风险则为《二犷六伽铲十护).AH川畔即撰[补注]
说明:补充资料仅用于学习参考,请勿用于其它任何用途。