In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likeliho...In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.展开更多
本文针对杂波条件下多扩展目标的状态估计,目标个数估计,扩展目标形状估计问题,提出了一种基于标签随机有限集(Labelled random finite sets,L-RFS)框架下多扩展目标跟踪学习算法,该学习算法主要包括两方面:多扩展目标动态建模和多扩展...本文针对杂波条件下多扩展目标的状态估计,目标个数估计,扩展目标形状估计问题,提出了一种基于标签随机有限集(Labelled random finite sets,L-RFS)框架下多扩展目标跟踪学习算法,该学习算法主要包括两方面:多扩展目标动态建模和多扩展目标的跟踪估计.首先,结合广义标签多伯努利滤波器(Generalized labelled multi-Bernoulli,GLMB)建立了扩展目标的量测有限混合模型(Finite mixture models,FMM),利用Gibbs采样和贝叶斯信息准则(Bayesian information criterion,BIC)准则推导出有限混合模型的参数来对多扩展目标形状进行学习,然后采用等效量测方法来替代扩展目标产生的量测,对扩展目标形状采用椭圆逼近建模,实现扩展目标形状与状态的估计.仿真实验表明本文所给的方法能够有效跟踪多扩展目标,并且在目标个数估计方面优于CBMeMBer算法.此外,与标签多伯努利滤波(LMB)计算比较表明:GLMB和LMB算法滤波估计精度接近,二者精度高于CBMeMBer算法.展开更多
This article introduces a resampling procedure called the truncated geometric bootstrap method for stationary time series process. This procedure is based on resampling blocks of random length, where the length of eac...This article introduces a resampling procedure called the truncated geometric bootstrap method for stationary time series process. This procedure is based on resampling blocks of random length, where the length of each blocks has a truncated geometric distribution and capable of determining the probability p and number of block b. Special attention is given to problems with dependent data, and application with real data was carried out. Autoregressive model was fitted and the choice of order determined by Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The normality test was carried out on the residual variance of the fitted model using Jargue-Bera statistics, and the best model was determined based on root mean square error of the forecasting values. The bootstrap method gives a better and a reliable model for predictive purposes. All the models for the different block sizes are good. They preserve and maintain stationary data structure of the process and are reliable for predictive purposes, confirming the efficiency of the proposed method.展开更多
针对高斯混合模型在模型训练之前无法确定最佳采样点组合方式以及无法确定最佳分布元个数的问题,提出一种基于GMM-Boost的WLAN室内定位方法。首先,采用第二类斯特林数枚举采样点组合方式,比较不同组合方式下高斯混合模型平均定位准确度...针对高斯混合模型在模型训练之前无法确定最佳采样点组合方式以及无法确定最佳分布元个数的问题,提出一种基于GMM-Boost的WLAN室内定位方法。首先,采用第二类斯特林数枚举采样点组合方式,比较不同组合方式下高斯混合模型平均定位准确度,进而确定最佳采样点组合方式。其次,针对每一种样本标签数,采用贝叶斯信息准则(Bayesian Information Criterion,BIC)选择高斯混合模型最优分布元个数。最后,结合Adaboost算法对高斯混合模型进行定位准确度提升。分析结果表明,该算法在定位误差为2 m时定位准确度为71.2%,在小样本量情况下可以获得较低的平均定位误差。与其他算法相比,该算法具有较好的定位准确度和泛化能力。展开更多
文摘In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.
文摘本文针对杂波条件下多扩展目标的状态估计,目标个数估计,扩展目标形状估计问题,提出了一种基于标签随机有限集(Labelled random finite sets,L-RFS)框架下多扩展目标跟踪学习算法,该学习算法主要包括两方面:多扩展目标动态建模和多扩展目标的跟踪估计.首先,结合广义标签多伯努利滤波器(Generalized labelled multi-Bernoulli,GLMB)建立了扩展目标的量测有限混合模型(Finite mixture models,FMM),利用Gibbs采样和贝叶斯信息准则(Bayesian information criterion,BIC)准则推导出有限混合模型的参数来对多扩展目标形状进行学习,然后采用等效量测方法来替代扩展目标产生的量测,对扩展目标形状采用椭圆逼近建模,实现扩展目标形状与状态的估计.仿真实验表明本文所给的方法能够有效跟踪多扩展目标,并且在目标个数估计方面优于CBMeMBer算法.此外,与标签多伯努利滤波(LMB)计算比较表明:GLMB和LMB算法滤波估计精度接近,二者精度高于CBMeMBer算法.
文摘This article introduces a resampling procedure called the truncated geometric bootstrap method for stationary time series process. This procedure is based on resampling blocks of random length, where the length of each blocks has a truncated geometric distribution and capable of determining the probability p and number of block b. Special attention is given to problems with dependent data, and application with real data was carried out. Autoregressive model was fitted and the choice of order determined by Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The normality test was carried out on the residual variance of the fitted model using Jargue-Bera statistics, and the best model was determined based on root mean square error of the forecasting values. The bootstrap method gives a better and a reliable model for predictive purposes. All the models for the different block sizes are good. They preserve and maintain stationary data structure of the process and are reliable for predictive purposes, confirming the efficiency of the proposed method.
文摘针对高斯混合模型在模型训练之前无法确定最佳采样点组合方式以及无法确定最佳分布元个数的问题,提出一种基于GMM-Boost的WLAN室内定位方法。首先,采用第二类斯特林数枚举采样点组合方式,比较不同组合方式下高斯混合模型平均定位准确度,进而确定最佳采样点组合方式。其次,针对每一种样本标签数,采用贝叶斯信息准则(Bayesian Information Criterion,BIC)选择高斯混合模型最优分布元个数。最后,结合Adaboost算法对高斯混合模型进行定位准确度提升。分析结果表明,该算法在定位误差为2 m时定位准确度为71.2%,在小样本量情况下可以获得较低的平均定位误差。与其他算法相比,该算法具有较好的定位准确度和泛化能力。
