两样本队列半数生存期检验:反射和检基统计量

依据反射或检基原理，本文提出用于截尾资料的两样本队列半数生存期（ＣＨＬ）检验．两样本合并ＣＨＬ经指数内插取自Ｋａｐｌａｎ－Ｍｅｉｅｒ或Ｂｅｒｋｓｏｎ－Ｇａｇｅ估计值．连续性校正，经以有效样本容量取代样本容量，扩展自Ｙａｔｅｓ校正．合并标准误来自同源性生存率方差估计值，后者经有效样本容量扩展自二项分布方差．无截尾时，这些统计量还原为经典中位数检验．与反射统计量相比，检基统计量具有更高的功效．附有工作实例描述其临床应用．

创新工作思路 强化统计执法

夯实统计基础,推进改革创新,服务科学发展,提高数据质量,完善机制的自身需要。统计部门要坚持依法治统、依法统计,以制度规范统计各项工作,依据法律行使统计行政权力。数据质量是统计的生命线,提高数据质量是统计工作永恒的主题。统计部门作为政府统计工作的行政机关,是贯彻落实统计法律法规和各项统计制度的执行者,涉及社会各行各业的统计行政行为,责任重大。

提高统计服务水平之我见

一、问题的提出贯彻和实践“三个代表”重要思想,遵循科学发展观的要求,积极探索统计工作发展的新路子,开创统计工作新局面,必须紧密围绕充分发挥统计工作的整体功能,提高统计数据质量,实现统计工作优质服务这个中心。笔者认为,过去我们更多的是强调数据质量是统计工作的生命,但是,需明确保证统计数据质量是为了更好地实现统计工作的优质服务。统计信息是各级党委和政府科学决策。

转发市统计局关于加强服务业统计工作意见的通知

津政办发[2012]52号各区、县人民政府,各委、局,各直属单位:市统计局《关于加强服务业统计工作的意见》已经市人民政府同意,现转发给你们,请照此执行。二○一二年五月十日关于加强服务业统计工作的意见为贯彻落实《国务院办公厅转发统计局关于加强和完善服务业统计工作意见的通知》(国办发[2011]42号),全面、客观、及时地反映我市服务业发展状况,促进服务业又好又快发展,现就进一步加强全市服务业统计工作提出以下意见:一。

天基MIMO雷达统一杂波模型及特性分析

天基MIMO雷达杂波特性分析对其杂波抑制技术研究具有重要意义.在分析天基统计MIMO雷达与杂波几何关系的基础上,构建了天基MIMO雷达杂波统一模型.根据模型对天基集中式MIMO雷达(单基地MIMO雷达、双基地MIMO雷达)和天基统计MIMO雷达进行了杂波特性仿真分析,验证了模型的通用性.仿真结果表明,受地球自转影响,天基雷达的空时杂波谱均存在弯曲和展宽,天基MIMO雷达的非线性变化程度小于天基相控阵雷达;对于统计MIMO雷达系统而言,可从多个角度观测目标,接收到的不同发射雷达的杂波脊线不重合,有利于后续的杂波抑制.

Customized Optimization for Vehicle Acoustic Statistical Energy Analysis

Statistical Energy Analysis(SEA) is one of the conventional tools for predicting vehicle high-frequency acoustic responses.This study proposes a new method that can provide customized optimization solutions to meet NVH targets based on the specific needs of different project teams during the initial project stages.This approach innovatively integrates dynamic optimization,Radial Basis Function(RBF),and Fuzzy Design Variables Genetic Algorithm(FDVGA) into the optimization process of Statistical Energy Analysis(SEA),and also takes vehicle sheet metal into account in the optimization of sound packages.In the implementation process,a correlation model is established through Python scripts to link material density with acoustic parameters,weight,and cost.By combining Optimus and VaOne software,an optimization design workflow is constructed and the optimization design process is successfully executed.Under various constraints related to acoustic performance,weight and cost,a globally optimal design is achieved.This technology has been effectively applied in the field of Battery Electric Vehicle(BEV).

基于径向基统计网络的数据流分析模型

在传统径向基函数网络的基础上,提出一种用于数据流分析的径向基统计网络,采用复合多维高斯函数来拟合输入空间中不同区域的概率密度.基于数理统计方法的径向基统计网络学习算法的时间和空间复杂度,均与输入变量维数和隐层节点个数的乘积成线性关系,而与数据流规模无关,能够满足数据流分析的实时性要求.仿真结果表明,径向基统计网络能够有效解决数据流的回归和分类问题.

Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model

Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system.However,linear models sometimes are unable to model complex nonlinear autocorrelation.To solve this problem,this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model,and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system.The performance of this method for checking the trend and sustained shift is analyzed.The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

Improved hidden Markov model for speech recognition and POS tagging

In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.

Baseband Design for 5G UDN Base Stations:Methods and Implementation

Baseband design and implementation for micro/pico base stations （mBS） in 5G ultra-dense network （UDN） is studied. Low cost is an essential requirement for mBS baseband in UDN. Digital baseband cost of ASIC/ASIP （Application Specific Integrated Circuit / Instruction-set processor） is of the most uncertainty in roBS system. However. the actual costs and hardware feasibility of the baseband are yet unknown to network deployers and researchers. In this paper, we studied the baseband hardware system design and implementation for low-cost roBS. We analyzed popular baseband algorithms and architectures for both full-digital and hybrid beamforming （BF） for UDN. We then proposed feasible chip-level solutions for the baseband with up to 128-antenna BS system, and estimated their implementation cost. Results show that among lull-digital BF algorithms, zero-forcing is a choice of high performance and low cost; for hybrid BF, 4×32 architecture （32 RF chains） provides good reduction in baseband cost with acceptable performance loss, thus it can be a preferable solution under low cost consider- ation. The proposed system planning method can also be used for the design of other related systems.

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios is proposedin this paper. The key idea of the algorithm is to pursue the maximum total spectrum utilization of thesystem by adopting a parallel technique in every spectrum allocation, and to ensure the statistical fairnessrule by deploying a particular scheme during a series of allocations. The simulation results show that theproposed algorithm not only achieves a fairer and more efficient allocation of spectrum resources, but alsohas much shorter allocation duration than the color sensitive graph coloring (CSGC) algorithm.

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

Resolution performance analysis of cumulants-based rank reduction estimator in presence of unexpected modeling errors

Compared to the rank reduction estimator （RARE） based on second-order statistics （called SOS-RARE）, the RARE employing fourth-order cumulants （referred to as FOC-RARE） is capable of dealing with more sources and mitigating the negative influences of the Gaussian colored noise. However, in the presence of unexpected modeling errors, the resolution behavior of the FOC-RARE also deteriorate significantly as SOS-RARE, even for a known array covariance matrix. For this reason, the angle resolution capability of the FOC-RARE was theoretically analyzed. Firstly, the explicit formula for the mathematical expectation of the FOC-RARE spatial spectrum was derived through the second-order perturbation analysis method. Then, with the assumption that the unexpected modeling errors were drawn from complex circular Gaussian distribution, the theoretical formulas for the angle resolution probability of the FOC-RARE were presented. Numerical experiments validate our analytical results and demonstrate that the FOC-RARE has higher robustness to the unexpected modeling en＇ors than that of the SOS-RARE from the resolution point of view.

Improved Keystroke Authentication Accuracy Based on Statistics and Weight

Abstract： In order to improve the recognition accuracy of key stroke authentication, a methodology based on feature extraction of keystroke sequence is presented in this paper. Firstly, the data of the users＇ keystroke feature information that has too much deviation with the mean deviation is filtered out. Secondly, the probability of each input key is calculated and 10 values which do not have the best features are selected. Thirdly, they are weighed and a score evaluating the extent to which the user could be authenticated successfully is calculated. The benefit of using a third-party data set is more objective and comparable. At last,

Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes

In this paper, an improved nonlinear process fault detection method is proposed based on modified kernel partial least squares(KPLS). By integrating the statistical local approach(SLA) into the KPLS framework, two new statistics are established to monitor changes in the underlying model. The new modeling strategy can avoid the Gaussian distribution assumption of KPLS. Besides, advantage of the proposed method is that the kernel latent variables can be obtained directly through the eigen value decomposition instead of the iterative calculation, which can improve the computing speed. The new method is applied to fault detection in the simulation benchmark of the Tennessee Eastman process. The simulation results show superiority on detection sensitivity and accuracy in comparison to KPLS monitoring.

Statistical Monitoring of Chemical Processes Based on Sensitive Kernel Principal Components

The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.

Thomasine Logia： A Collection of Sapiential Traditions

This paper argues the sapiential characteristic of an ancient text in the history of early Christianity. Although Thomas is a creative and distinctive text for its own community, the major framework of the Logia in a textual-statistic way was based on the traditional Sophia literature that had affected the culture, custom, and authority of the Hellenised Jewish societies. Then, what kind of scrolls, books, and writings could be the religio-historical sources for Thomas？ How was Thomas transformed from those sapiential manuscripts？ While three data of ＂the Pauline Sophia concept＂, ＂the sapiential Logia of Q＂, and ＂the sapiential themes of the Jewish apocalyptic writings＂ are quite relevant, the Greek fragment of Papyrus Oxyrhynchus （P. Oxy.） 654 and NHC II, 2.32：10-51：28 will evince the new insight that the ancient Logia of the Thomasine community was not ＂the traditional Q＂, but could be ＂a new Q＂ coming from the common （oral, or written, or both） Sophia tradition, as an independently developed one.

Clinical significance of serum expression of GROβ in esophageal squamous cell carcinoma

AIM:To determine the association between serum levels of growth-related gene product β(GROβ) and clinical parameters in esophageal squamous cell carcinoma(ESCC).METHODS:Using enzyme-linked immunosorbent assay,serum GROβ levels were measured in ESCC patients(n = 72) and healthy volunteers(n = 83).The association between serum levels of GROβ and clinical parameters of ESCC was analyzed statistically.RESULTS:The serum GROβ levels were much higher in ESCC patients than in healthy controls(median:645 ng/L vs 269 ng/L,P < 0.05).Serum GROβ levels were correlated positively with tumor size,lymph node metastasis,and tumor-node-metastasis(TNM) staging,but not with gender or the histological grade of tumors in ESCC patients.The sensitivity and specificity of the assay for serum GROβ were 73.61% and 56.63%,respectively.CONCLUSION:GROβ may function as an oncogene product and contribute to tumorigenesis and metastasis of ESCC.

Simultaneous Selection for Yield and Stability in Sugarcane Using Parametric Statistics

Sugarcane advanced variety trials are planted across several locations and harvested for several crop-years to determine genotype by environment interaction and yield stability. Previous studies describe methods for simultaneous screening for yield and stability but did not use parametric statistical tests for comparing genotypes. The objective of this study was to describe a parametric statistical method for simultaneous screening of sugarcane genotypes for yield and stability. Data from 26 crops were collected from trials established at five locations and harvested in the plant, first, second, third and fourth ratoon crops. The mixed procedure of SAS was used for data analysis. The intercept and slope were used to represent yield and stability, respectively. There were significant （P 〈 0.05） differences in yield and stability among the genotypes. Test genotypes were classified into groups of genotypes that produced high yield, or high stability or both. The method provides fast statistical tests for simultaneous screening for yield and stability. The method was also used to compare two genotypes, an application for variety choice at time of release.

Review of China Mobile English Learning： Achievements and Shortcomings -Based on statistical analysis of literature for 10 years （2004-2014） in CNKI （China National Knowledge Infrastructure）

The CNKI includes 153 pieces of paper for 10-year period of 2004-2014 about mobile English learning. We conducted a statistical analysis of 10 years of research among mobile English learning achievements and shortcomings and summarized in order to provide advice and reference for study in the future.