The Convergence Rates of Improveed Kernel Estimators for Regression Function

The kernel function of kernel estimator for regression function is often defined with compact support, the moment of Y is bigger than one. The convergence rate of the improved kernel estimator is discussed under the improved kernel function. The result is also discussed under random censorship. The class of applicable kernels include those having unbounded support and even not integral .The condition of moment of Y is discarded under the improved kernel estimator. The results are wholly the same as usual and generalize the relative results of the paper .     

星斑川鲽MHCⅡ恒定链Ii基因的克隆和表达特性

为了研究星斑川鲽MHCⅡ类分子的作用及调控机制,实验通过SMART-RACE技术克隆得到了星斑川鲽MHCⅡ恒定链(MHCⅡIi)的全长cDNA序列,其长度为1766 bp,包含135 bp的5′非编码区、837 bp的开放阅读框和794 bp的3′非编码区。该基因共编码279个氨基酸。理论分子量为30.848 ku,等电点为6.89。与已知物种MHCⅡIi进行同源性比对,结果与狼鲈、紫红笛鲷和鳜关系较近,同源性均为79%。利用quantitative realtime PCR(qRT-PCR)技术检测了MHCⅡIi在星斑川鲽不同组织中的表达,以及爱德华氏菌感染前后对该基因在不同组织中表达水平的影响,结果显示:在脾脏、头肾、肝脏、后肠、性腺、心脏、血液、鳃和肌肉组织中,MHCⅡIi mRNA均有表达,但在表达量上有明显差异,脾脏和头肾组织相对表达水平较高,鳃、血液、肌肉、心脏和性腺中的表达水平较低。病原感染后,免疫相关组织脾脏和头肾的表达水平升高最明显,肝脏和后肠的表达水平也略有升高,但变化不明显。本研究可为星斑川鲽MHCⅡ类分子的作用机理提供理论依据,同时为海水养殖鱼类的抗病遗传育种工作提供研究基础。     

具有风险相依结构的平衡信度估计

在保险实务中,风险之间具有一定的相依结构. 通过考虑保费的目标估计来对风险保费进行了研究,采用正交投影的方法求解了最优问题,在平衡损失函数下得到了风险等相关的齐次和非齐次信度估计. 结果表明得到的信度估计具有经典信度模型的加权形式.     

基于光照无关图理论的苹果表面阴影去除方法

阴影影响下苹果目标的快速准确识别是苹果采摘机器人视觉系统必须解决的关键技术之一。为了实现阴影影响下苹果目标的准确识别,该研究采用光照无关图理论实现了苹果表面阴影的去除。以自然场景下获取的受不同程度阴影影响的苹果目标图像为研究对象,首先利用光照无关图原理获取阴影苹果图像的光照无关图,达到突出苹果目标阴影区域的目的;其次提取原图像的红色分量信息并与关照无关图进行相加处理;最后将相加后的图像进行自适应阈值分割处理,达到去除阴影的目的。为了验证该算法的有效性与准确性,利用20幅受阴影影响的苹果目标图像进行了试验,并与Otsu算法、1.5*R-G色差算法进行了对比,试验结果表明：Otsu算法仅能识别出未受阴影影响的苹果区域;1.5*R-G 色差算法受光照影响较大,对于苹果图像的相对强光照区域和部分阴影区域不能有效识别;基于光照无关图的苹果表面阴影去除方法对阴影影响下的苹果目标图像分割效果较好,可以克服光照过强的问题,并准确识别出阴影影响下的苹果目标。文中算法的平均假阳性率为17.49%,比Otsu算法降低了52.84%,比1.5*R-G算法降低了26.18%;文中算法的平均重叠系数为86.59%,比Otsu算法提高了47.2%,比1.5*R-G算法提高了11.03%;表明利用光照无关图可以有效地去除苹果表面的阴影,将其应用于阴影影响下的苹果目标的识别是可行的。     

Accuracy of pairwise methods in the reconstruction of family relationships, using molecular information from turbot (Scophthalmus maximus)

Many estimators and algorithms have been developed to infer the genealogical relationships from molecular marker data when there is no pedigree information. Most pairwise methods provide estimates in a continuous range that needs to be converted into genealogical relationships (namely full-sibs, half-sibs and unrelated) if there is a previous knowledge of the population structure. Transformations are usually based on arbitrary thresholds, but the possibility of correcting the coancestry estimates via explicit pedigree reconstruction methods has been suggested. Using molecular data for ten highly polymorphic microsatellite loci on a population of turbot (Scophthalmus maximus) with a known genealogy, the behaviour of four pairwise marker-based coancestry estimators and the molecular coancestry has been evaluated. The population consisted on 138 families with 4 full-sib individuals each and one family with 8 full-sib individuals (up to 15 half-sib families were also present due to the sharing of parents between some of the full-sibs families). Our results suggested that transforming the pairwise estimators and the molecular coancestry to family relationships through the establishment of thresholds performs slightly better than the explicit pedigree reconstruction method, when accuracy is measured in a pairwise basis. However, if joint relationships between more than two individuals were tested at a time, the threshold methods led to a high percentage of incongruous triads of full-sib individuals, with Mendelian incompatibilities appearing in congruous full-sib families (more than 70% and 60% in our study, respectively). The explicit pedigree reconstruction approach, due to its nature, is free from such problems. Therefore, the pedigree reconstruction approach seems to be a valuable tool to provide a congruent and compatible pedigree when the pairwise marker-based coancestry matrices or the molecular coancestry need to be transformed.     

Small area estimations of proportion of forest and timber volume combining Lidar data and stereo aerial images with terrestrial data

Abstract

Methods for small area estimations were compared for estimating the proportion of forest and growing stock volume of temperate mixed forests within a district of a member state (canton) in Switzerland. The estimators combine terrestrial data with remotely sensed auxiliary data. By using different model types, different sources of auxiliary data and different methods of processing the auxiliary data, the increase in estimation precision was tested. Using the canopy height derived from remote sensing data, the growing stock volume and the proportion of forest were estimated. The regression models used for the small area estimation provided a coefficient of determination of up to 68% for the timber volume. The proportion of plots correctly classified into forest and non-forest plots ranged between 0.9 and 0.98. Models calibrated over forest area only resulted in a maximal coefficient of determination of 37%. Even though these coefficients indicate a moderate model quality, the use of remote sensing data clearly improved the estimation precision of both the proportion of forest and the growing stock volume. Generally, Lidar data led to slightly higher estimates compared to data from aerial photography. It was possible to reduce the variance of the estimated proportion of forest to nearly one tenth compared with the variance based on the terrestrial measurements alone. Similarly, the variance of the growing stock volume could be reduced to one fourth as compared with the variance based solely on the terrestrial measurements.     

估计量一致性的适用判别法

证明了3种关于一致估计量的适用判别法,并给出相应的应用实例.     

参数区间估计和样本容量确定在市场调查中的应用

简述了参数区间估计的概念，方法以及样本容量的确定方法，在此基础上，给出市场调查中比率的区间估计，样本容量确定和均值的区间估计实例。     

MIMO-OFDM系统中低复杂度重叠的ML信道估计

构造了一种适于MIMO-OFDM无线系统的低复杂度基于重叠训练图样的ML(Maximum likelihood)信道估计器。通过理论分析:当训练符号矩阵是酋矩阵且训练符号向量摆放在等间距的L子载波上时,重叠ML估计器实现最优的MSE(Mean square error)性能;当训练符号矩阵是归一化Hadamard矩阵时,重叠ML估计器复杂度等于正交。在相关的MIMO移动信道仿真表明:当信道在N个连续OFDM符号时间内保持基本不变时,二者的BER性能是一致的;增加信道时变性后,重叠ML表现了更优的BER(Bit error rate)性能。