A minimum average risk approach to shrinkage estimators of the normal mean

For the problem of estimating the normal mean based on a random sample X ₁,...,X _n when a prior value ₀ is available, a class of shrinkage estimators % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-qqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xHapdbiqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaubeaeqaje% aWbaGaamOBaaWcbeqdbaGafqiVd0MbaKaaaaGccaqGGaGaaiikaiaa% dUgacaGGPaGaeyypa0Jaam4AaiaacIcadaqfqaqabKqaahaacaqGUb% aaleqaneaacaqGubaaaOGaaiykaiaabccadaqfqaqabKqaahaacaWG% UbaaleqaneaaceqGybGbaebaaaGccaqGGaGaey4kaSIaaeiiaiaacI% cacaaIXaGaaeiiaiabgkHiTiaabccacaWGRbGaaiikamaavababeqc% baCaaiaab6gaaSqab0qaaiaabsfaaaGccaGGPaGaaiykamaavababe% qcbaCaaiaad6gaaSqab0qaaiabeY7aTbaaaaa!5615!\[\mathop {\hat \mu }\nolimits_n {\rm{ }}(k) = k(\mathop {\rm{T}}\nolimits_{\rm{n}} ){\rm{ }}\mathop {{\rm{\bar X}}}\nolimits_n {\rm{ }} + {\rm{ }}(1{\rm{ }} - {\rm{ }}k(\mathop {\rm{T}}\nolimits_{\rm{n}} ))\mathop \mu \nolimits_n \] is considered, where % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-qqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xHapdbiqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaubeaeqaje% aWbaGaamOBaaWcbeqcdawaaiaadsfaaaGccaqGGaGaaeypaiaabcca% caWGUbWaaWbaaSqabeaacaaIXaGaai4laiaaikdaaaGccaGGOaWaa0% aaaeaacaWGybaaamaaBaaajeaWbaGaamOBaaWcbeaakiaabccacqGH% sislcaqGGaWaaubeaeqajeaWbaGaaGimaaWcbeqdbaGaaeiVdaaaki% aacMcacaqGGaGaae4laiabeccaGiabeo8aZbaa!4C33!\[\mathop T\nolimits_n {\rm{ = }}n^{1/2} (\overline X _n {\rm{ }} - {\rm{ }}\mathop {\rm{\mu }}\nolimits_0 ){\rm{ /}} \sigma \] and k is a weight function. For certain choices of k, % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-qqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xHapdbiqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaubeaeqaje% aWbaGaamOBaaWcbeqdbaGafqiVd0MbaKaaaaGccaqGGaGaaiikaiaa% dUgacaGGPaaaaa!3CEE!\[\mathop {\hat \mu }\nolimits_n {\rm{ }}(k)\] coincides with previously studied preliminary test and shrinkage estimators. We consider choosing k from a natural non-parametric family of weight functions so as to minimize average risk relative to a specified prior p. We study how, by varying p, the MSE efficiency (relative to \-X) properties of % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-qqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xHapdbiqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaubeaeqaje% aWbaGaamOBaaWcbeqdbaGafqiVd0MbaKaaaaGccaqGGaGaaiikaiaa% dUgacaGGPaaaaa!3CEE!\[\mathop {\hat \mu }\nolimits_n {\rm{ }}(k)\] can be controlled. In the process, a certain robustness property of the usual family of posterior mean estimators, corresponding to the conjugate normal priors, is observed.     

α—甲基苯乙烯离子聚合体分子量分布

聚α-甲基苯乙烯-丁二烯-α-甲基苯乙烯嵌段共聚物是一种性能优异的热塑性弹性体。常温下,α-甲基苯乙烯的聚合是在极性添加剂存在下进行的,添加剂的种类,用量及聚合温度不仅影响聚合动力学,尚且影响聚合物分子量及其分布,控制α-甲基苯乙烯聚合速率、     

用透射积分法研究的穆斯堡尔谱学——一个在聚合二茂铁研究中的应用

本文利用透射积分法，在聚合二茂铁研究中发现，这种方法不但能够得到超精细参数，而且能够得到一般拟合方法得不到的另外两个重要参数—样品的无反冲分数和相应的德拜温度，由此能进一步了解到样品在结合中晶格的质量及化学键的强弱，为穆斯堡尔谱研究提供了一条新的途径。     

聚合物连续分级

本文较系统地介绍了聚合物连续分级的理论基础、实验室装置及其实际应用。     

因子分析—伏安法同时测定波峰重叠的混合物组份

本文将目标因子分析用于伏安分析法同时测定波峰重叠的混合物组份。用此法对Ｐｂ（Ⅱ）、Ｔｌ（Ｉ）混合体系的导数脉冲伏安重叠峰数据进行解析，获得了较理想的结果。     

聚2,6-二甲基苯醚树脂分子量分布及其与冲击强度关系的研究

本工作建立了室温下用凝胶色谱法(GPC)测定聚2,6-二甲基苯醚(PPO)树脂分子量、分子量分布的方法。淋洗剂不采用文献申报道的毒性较大者,也不在较高柱温下进行,而采用甲苯(或氯仿)在室温下测定。对样品在甲苯中溶解温度、浓度和稳定性等都进行了系统研究,找到了最优化条件。GPC数据结合改性PPO(MPPO)产品性能测定结果,经仔细分析找到了影响MPPO产品质量的一个重要因素是PPO树脂中低分子量部分含量。它与产品冲击强度有明显的依赖关系。提出了一个从CPC谱图确定低分子量部分含量的方法。     

对NO生理作用的新认识及其电化学实时检测

本文综述了近年来学术界对ＮＯ生理作用的新认识，并介绍了现场实时检测生物活体中释放的ＮＯ浓度的电化学方法．     

New PVC materials for medical applications—the release profile of PVC/polycaprolactone–polycarbonate aged in aqueous environments

Medical grade PVC plasticised with polycaprolactone–polycarbonate (PCL–PC) was subjected to aqueous environments at different temperatures. The release profile during ageing was determined by solid phase microextraction (SPME) and GC–MS. At the same time changes in the surface composition due to, for example, migration of PCL–PC from the blend were followed by FTIR. Almost no changes in the material or its surface composition were observed during 98 days at 37 °C in water or phosphate buffer. Only trace amount of 6-hydroxyhexanoic acid the final hydrolysis product of PCL–PC was detected in the GC–MS chromatograms and the weight loss was negligible. Even when the ageing temperature was raised to 70 °C only minor increase in the amount of 6-hydroxyhexanoic acid was observed and the weight loss after 98 days was under 1%. Changes in the FTIR spectra indicating migration of PCL–PC towards the surface of the PVC/PCL–PC tubing were observed first after 70 days at 70 °C. Large increase in the hydrolysis rate of PCL–PC and almost complete depletion of PCL–PC from the blend was observed when the ageing temperature was raised to 100 °C.     

近红外漫反射光谱法对固态样品的无损分析

本文应用目标因子分析研究了固态样品的近红外漫反射光谱，用目标因子分析成功地确定了固态样品的物种数、物种种类和各物种的含量。     

Averaged molecular weight of complex mixtures as determined by rapid measurements of diffusion

Summary The concept of a diffusion-averaged molecular weight, M_d is presented, and M_d is defined for polydisperse samples. It is shown via oligostyrene mixtures that M_d can be determined in 20 minutes with common chromatographic equipment. If M_d is measured and M_n or M_w is known, the polydispersity can be obtained.