检索结果-内蒙古大学图书馆

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Xu, F Liu, GZ Zeng, ZH Xian Jiaotong Univ Xian 710049 Peoples R China

ISBN: (纸本)0780376633

An improved parallel interference cancellation (PIC) detector based on conditional likelihood function is presented. The improved PIC benefits from an additional correcting module after the last stage, where the maximum likelihood algorithm using only the conditional likelihood function, which is calculated from the information of the tentative decisions, is employed. Analysis and simulation show that the new algorithm can improve the bit error rate (BER) performance of PIC with much lower computation complexity than optimum detector.

关键词： computational complexity error statistics radiofrequency interference interference suppression maximum likelihood detection code division multiple access spread spectrum communication multiuser detection parallel interference cancellation detector PIC detector conditional likelihood function maximum likelihood algorithm tentative decision information bit error rate BER computational complexity DS-CDMA multiuser detection multiple access interference MAI maximum likelihood detection

来源：评论

学校读者我要写书评

暂无评论

An adaptive Kriging reliability analysis method based on novel condition likelihood function

引用

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2022年第8期36卷 3911-3922页

作者： Lu, Mingming Li, Huacong Hong, Linxiong Northwestern Polytech Univ Sch Power & Energy Xian 710072 Peoples R China

To carry out the reliability analysis, whose performance functions are presented in a nonlinear form, many studies propose the reliability analysis methods involving the active Kriging model. Though some learning functions have been developed to refine the Kriging model around the limit state surface (LSS) effectively, most of them rely on the Kriging predictor and its variance. In this research, a new learning function, formed by the combination of the conditional likelihood function and clustering constrain function through adaptive weight coefficient, is raised to reconstruct Kriging by the candidate samples near the LSS. With the conditional likelihood function, the likelihood that the Kriging predictor reaches the LSS mainly contributes to the selection of the best next point. Three numerical applications with different complexities are used to investigate the validity of the proposed reliability method. In addition, the performance of the proposed reliability method is tested by an engineering application.

关键词： Reliability analysis Kriging model Active learning conditional likelihood function Active weight coefficient

来源：评论

学校读者我要写书评

暂无评论

Statistical analysis of monopulse-synthetic aperture radar for constant false-alarm rate detection of ground moving targets

引用

IET RADAR SONAR AND NAVIGATION 2015年第6期9卷 641-652页

作者： Wu, Di Zhu, Daiyin Shen, Mingwei Li, Yong Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 210016 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 211100 Jiangsu Peoples R China

An efficient approach to achieve ground moving target indication (GMTI) for synthetic aperture radar (SAR) is to use the monopulse-SAR system. In this kind of SAR system, moving targets are detected over a so called, called monopulse ratio diagram (MRD) generated by a pixel-to-pixel comparison between the two SAR images acquired from the sum and difference channels. The sample derived from this diagram for the pixel under test (PUT), i.e. the value of monopulse ratio (MR), is employed as the test statistic. This paper examines the statistics of MR when complex Gaussian clutter-plus-noise background in considered. The conditional probability density function (pdf) of MR, given the occurrence that the amplitude of the sum signal is greater than a predetermined threshold, is analysed in detail. Especially, the conditional likelihood function of MR under the null hypothesis is given in a closed-form. Based on these results, an automatic constant false-alarm rate (CFAR) detector for moving target detection (MTD) is proposed and extended to a so called multi-MRD form to further improve the final detection performance. Simulations, as well as experimental results obtained from two groups of real dataset, are presented to examine the detection performance and validate the theoretical analysis.

关键词： statistical analysis automatic constant false-alarm rate detector conditional probability density function conditional likelihood function radar imaging image motion analysis ground moving target detection monopulse-synthetic aperture radar probability ΣΔ-SAR system detection performance improvement Optical, image and video signal processing complex SAR images Radar equipment, systems and applications multiMRD sum and difference channels amplitude threshold detector two-dimensional diagram Gaussian processes synthetic aperture radar monopulse ratio diagram monopulse-SAR null hypothesis pixel-to-pixel comparison radar clutter object detection MR threshold detector complex Gaussian clutter-plus-noise background Other topics in statistics

来源：评论

学校读者我要写书评

暂无评论

conditional likelihood AND UNconditional OPTIMUM ESTIMATING EQUATIONS

引用

BIOMETRIKA 1976年第2期63卷 277-284页

作者： GODAMBE, VP UNIV WATERLOO DEPT CHEM & BIOCHEMWATERLOOONTARIOCANADA

SUMMARY: Godambe & Thompson (1974) characterized optimum estimating equations for some situations when the underlying frequency function contained a nuisance parameter. In the present paper the nuisance parameter case is related to concepts of conditional likelihood and complete sufficiency. Under suitable conditions it is shown that the maximum conditional likelihood equation provides the optimum estimating equation, the criterion of optimality being independent of conditioning. In the light of this, some definitions of ancillarity and sufficiency in the presence of nuisance parameters are discussed. © 1976 Biometrika Trust.

关键词： conditional likelihood function Estimating equations Maximum likelihood equation Nuisance parameter

来源：评论

学校读者我要写书评

暂无评论

Bayesian conditional inference for Rasch models

引用

ASTA-ADVANCES IN STATISTICAL ANALYSIS 2018年第2期102卷 245-262页

作者： Draxler, Clemens Univ Hlth & Life Sci EWZ 1 A-6060 Hall In Tirol Austria

This paper is concerned with Bayesian inference in psychometric modeling. It treats conditional likelihood functions obtained from discrete conditional probability distributions which are generalizations of the hypergeometric distribution. The influence of nuisance parameters is eliminated by conditioning on observed values of their sufficient statistics, and Bayesian considerations are only referred to parameters of interest. Since such a combination of techniques to deal with both types of parameters is less common in psychometrics, a wider scope in future research may be gained. The focus is on the evaluation of the empirical appropriateness of assumptions of the Rasch model, thereby pointing to an alternative to the frequentists' approach which is dominating in this context. A number of examples are discussed. Some are very straightforward to apply. Others are computationally intensive and may be unpractical. The suggested procedure is illustrated using real data from a study on vocational education.

关键词： Bayesian inference Discrete conditional probability distribution Hypergeometric distribution conditional likelihood function Rasch model

来源：评论

学校读者我要写书评

暂无评论

Bayesian classification of ARMA sources with unknown order

引用

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2001年第6期30卷 1143-1154页

作者： Shaarawy, SM Daif, AL Zahran, AR Cairo Univ Fac Econ & Polit Sci Dept Stat Cairo Egypt

Time series classification is a very rich subject and may be found in many areas such as economics, business, chemistry, engineering, and environmental data. This paper proposes an approximate Bayesian scheme to assign a univariate time series realization into one of several autoregressive moving average sources, with unknown coefficients and precision, that share a common unknown order. Using either a normal gamma density or a Jeffreys' prior and an approximate conditional likelihood function, the proposed assignment technique is to develop the marginal posterior mass function of a classification vector assuming the maximum of the unknown order is known. A time series realization is assigned to the r-th autoregressive moving average source whenever the classification vector has its largest value at the r-th mass point. A simulation study is carried out in order to examine the behavior and adequacy of the proposed technique with moderate realization sizes. The selected sources are chosen in such a way to include different parameters. The numerical results show that the proposed technique is practical, easy to program, and efficient in handling the classification problem of autoregressive moving average sources with unknown order.

关键词： classification vector ARMA sources conditional likelihood function posterior mass function

来源：评论

学校读者我要写书评

暂无评论

BAYESIAN CLASSIFICATION WITH MULTIVARIATE AUTOREGRESSIVE SOURCES THAT MIGHT HAVE DIFFERENT ORDERS

引用

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 1995年第3期24卷 567-581页

作者： SHAARAWY, SM ALMAHMEED, MA CAIRO UNIV FAC ECON & POLIT SCICAIROEGYPT KUWAIT UNIV COLL COMMERCE ECON & POLIT SCISAFATKUWAIT

The main objective of this paper is to develop an exact Bayesian technique that can be used to assign a multivariate time series realization to one of several autoregressive sources, with unknown coefficients and precision, that might have different orders. The foundation of the proposed technique is to develop the posterior mass function of a classification vector, in an easy form, using the conditional likelihood function. A multivariate time series realization is assigned to the multivariate autoregressive source with the largest posterior probability. A simulation study, with uniform prior mass function, is carried out to demonstrate the performance of the proposed technique and to test its adequacy in handling the multivariate classification problems. The analysis of the numerical results supports the adequacy of the proposed technique in solving the classification problems with multivariate autoregressive sources.

关键词： MULTIVARIATE AUTOREGRESSIVE SOURCES CLASSIFICATION conditional likelihood function POSTERIOR MASS function

来源：评论

学校读者我要写书评

暂无评论

Bayesian identification of multivariate autoregressive processes

引用

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2008年第5期37卷 791-802页

作者： Shaarawy, Samir M. Ali, Sherif S. Cairo Univ Dept Stat Fac Econ & Polit Sci Cairo Egypt

Identification is one of the most important stages of a time series analysis. This paper develops a direct Bayesian technique to identify the order of multivariate autoregressive processes. By employing the conditional likelihood function and a matrix normal-Wishart prior density, or Jeffrey' vague prior, the proposed identification technique is based on deriving the exact posterior probability mass function of the model order in a convenient form. Then one may easily evaluate the posterior probabilities of the model order and choose the order that maximizes the posterior mass function to be the suitable order of the time series data being analyzed. Assuming the bivariate autoregressive processes, a numerical study, with different prior mass functions, is carried out to assess the efficiency of the proposed technique. The analysis of the numerical results supports the adequacy of the proposed technique in identifying the orders of multivariate autoregressive processes.

关键词： conditional likelihood function identification matrix normal-Wishart distribution multivariate autoregressive processes probability mass function

来源：评论

学校读者我要写书评

暂无评论

Bayesian Identification of Seasonal Multivariate Autoregressive Processes

引用

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2015年第4期44卷 823-836页

作者： Shaarawy, Samir M. Ali, Sherif S. Cairo Univ Dept Stat Cairo Egypt

The main objective of this article is to develop an approximate Bayesian procedure to identify the orders of seasonal multivariate autoregressive processes. Using either a matrix normal-Wishart prior density or a non informative prior, which is combined with an approximate conditional likelihood function, the foundation of the proposed technique is to derive the joint posterior mass function of the model orders in a convenient form. Then one may easily evaluate the joint posterior probabilities of all possible values of the model orders and choose the orders at which the joint posterior mass function attains its maximum to be the identified orders. A simulation study, with different prior mass functions, is carried out to determine the effectiveness of the proposed technique in handling the identification problem of the seasonal bivariate autoregressive processes.

关键词： Identification Seasonal multivariate autoregressive processes conditional likelihood function Matrix normal-Wishart distribution Posterior mass function

来源：评论

学校读者我要写书评

暂无评论

Bayesian identification of seasonal autoregressive models

引用

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2003年第5期32卷 1067-1084页

作者： Shaarawy, SM Ali, SS Cairo Univ Fac Econ & Polit Sci Dept Stat Cairo Egypt

A very important and essential phase of time series analysis is identifying the model orders. This article develops an approximate Bayesian procedure to identify the orders of seasonal autoregressive processes. Using either a normal-gamma prior density or a noninformative prior, which is combined with an approximate conditional likelihood function, the foundation of the proposed technique is to derive the joint posterior mass function of the model orders in an easy form. Then one may inspect the posterior mass function and choose the orders with the largest posterior probability to be the suitable orders of the time series being analyzed. A simulation study, with different priors mass functions, is carried out to test the adequacy of the proposed technique and compare it with some non-Bayesian automatic criteria. The analysis of the numerical results supports the adequacy of the proposed technique in identifying the orders of the autoregressive processes.

关键词： identification seasonal autoregressive models conditional likelihood function normal-gamma density posterior mass function automatic criteria

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：