基于线性约束的快速点云配准方法研究

三维视景仿真中,目标对象的重构需要多组测量数据进行配准,提高点云的配准速度和精度是点云配准的关键。因此,针对经典ICP配准算法存在计算量大、点状特征提取精度低的特点,文章结合改进的S-ICP算法对目标函数进行优化求解,同时在S-ICP算法基础上对初始旋转平移参数进行优化改进,最终得到更为精确的配准。实验结果表明,与经典ICP以及S-ICP算法相比,文章算法在配准速度和精度方面都有明显提高,能够实现点云的快速、准确配准。     

基于线性约束的快速点云配准方法研究

三维视景仿真中,目标对象的重构需要多组测量数据进行配准,提高点云的配准速度和精度是点云配准的关键。因此,针对经典ICP配准算法存在计算量大、点状特征提取精度低的特点,文章结合改进的S-ICP算法对目标函数进行优化求解,同时在S-ICP算法基础上对初始旋转平移参数进行优化改进,最终得到更为精确的配准。实验结果表明,与经典ICP以及S-ICP算法相比,文章算法在配准速度和精度方面都有明显提高,能够实现点云的快速、准确配准。     

具有AR(p)误差的半参数回归模型的参数估计

近年来,半参数模型是处理回归问题的有力工具,进年来,已经成为当今回归分析的热点,引起了众多学者的关注。文章研究了具有AR(p)误差的半参数回归模型,首先对其误差的相关性进行了消除,然后将模型转变成为经典的半参数回归模型,运用惩罚最小二乘估计方法对模型参数进行了估计。     

服装服务质量及其建模方法研究

文章介绍了服务质量及，阐述了我国服装服务质量的现状，给出了服装服务质量的测评指标，介绍了部分最小二乘回归构建服装服务质量模型的方法及其步骤，给出了提高我国服装服务质量的对策。     

GM(1,1)预测模型中原始数据的函数变换研究

灰色预测模型是灰色系统理论的重要内容之一,也是预测理论中被广泛使用的一种预测方法。为了提高预测精度,需要对原始数据序列作数据处理,提高数据序列的光滑度,本文主要是对灰色预测GM(1,1)模型利用函数变换理论对其原始数据函数变换处理进行研究,并对进过函数变换原的数据精度进行比较。     

基于主要战技指标的无人机系统采购成本快速估算方法研究

随着现代先进无人机成本越来越高,如何在只有主要战技指标的情况下快速估算无人机系统采购成本,已经成为人们普遍关注的问题。本文通过收集国外20余种无人机型号的技术、成本数据,采用偏最小二乘法进行建模,得到按主要系统分解的无人机系统采购成本的快速估算模型,并以应用于国内无人机型号成本的测算工作中,收到了良好的效果。     

社会核算矩阵平衡方法研究——最小二乘交叉熵法

针对初始不平衡SAM与真实SAM关系未知的情形,本文提出了最小二乘交叉熵(LSCE)平衡法。基于最小二乘法(LS)、交叉熵法(CE)以及LSCE方法的仿真分析表明,CE与LS的相对稳健性取决于初始不平衡SAM的误差特征:当初始不平衡SAM的交易流量更接近于真实SAM时,LS较优;当初始不平衡SAM的系数矩阵更接近于真实SAM时,CE较优。LSCE方法同时考虑了SAM表流量和系数矩阵信息,故可得到精度介于LS和CE间的平衡SAM表,从而保证了平衡后SAM表的相对精度。     

第三方支付用户满意度实证研究——以微信支付为例

现今发展最快且最具生命力的支付方式是第三方的移动支付功能,其中微信支付,以智能设备绑定客户银行卡为手法实现了移动支付能力。本文针对其客户满意度,提出了七大潜在满意度模型。通过问卷调查等方式获得数据,以调查数据为原型,运用偏最小二乘法运算分析,结果表明,对满意度有着明显影响的分别是信任、顾客维系策略、服务质量和三种不同的价值观念。本文最后为提高微信支付客户满意度提出了有关建议。     

齿轮视觉检测中测量基准确定方法研究

文章针对直齿渐开线圆柱齿轮的视觉测量系统,提出一种综合利用图像处理、边缘检测和尺寸测量等方法的齿轮测量基准确定算法,完成了对齿轮轴孔目标边缘的准确检测和对齿轮测量基准的精确计算。     

基于最小二乘支持向量机的VaR计算方法研究

VaR是使投资风险数量化的工具,旨在估计给定金融资产或组合在正常的资产价格波动下未来可能的或潜在的最大损失。支持向量机是一种基于传统统计学习理论的机器学习算法。波动率作为金融风险的度量,是风险管理中的重要指标。在对Va R的计算中,本文将最小二乘支持向量机与传统的蒙特卡罗模拟法结合,对波动率进行估计。实证分析表明,该方法可行有效。     

An application of approximate finite sample results to parameter estimation in a linear errors-in-variables model

In the simple errors-in-variables model the least squares estimator of the slope coefficient is known to be biased towards zero for finite sample size as well as asymptotically. In this paper we suggest a new corrected least squares estimator, where the bias correction is based on approximating the finite sample bias by a lower bound. This estimator is computationally very simple. It is compared with previously proposed corrected least squares estimators, where the correction aims at removing the asymptotic bias or the exact finite sample bias. For each type of corrected least squares estimators we consider the theoretical form, which depends on an unknown parameter, as well as various feasible forms. An analytical comparison of the theoretical estimators is complemented by a Monte Carlo study evaluating the performance of the feasible estimators. The new estimator proposed in this paper proves to be superior with respect to the mean squared error.     

Estimation in monotone single‐index models

Single‐index models are popular regression models that are more flexible than linear models and still maintain more structure than purely nonparametric models. We consider the problem of estimating the regression parameters under a monotonicity constraint on the unknown link function. In contrast to the standard approach of using smoothing techniques, we review different “non‐smooth” estimators that avoid the difficult smoothing parameter selection. For about 30 years, one has had the conjecture that the profile least squares estimator is an ‐consistent estimator of the regression parameter, but the only non‐smooth argmin/argmax estimators that are actually known to achieve this ‐rate are not based on the nonparametric least squares estimator of the link function. However, solving a score equation corresponding to the least squares approach results in ‐consistent estimators. We illustrate the good behavior of the score approach via simulations. The connection with the binary choice and current status linear regression models is also discussed.     

On the performance of the ordinary least squares method under an error component model

Starting from the one-dimensional results by Wang et al (1994) we consider the performance of the ordinary least squares estimator in comparison to the best linear unbiased estimator under an error component model with random effects in units and time. Upper bounds are derived for the first-order approximation to the difference between both estimators and for the spectral norm of the difference between their dispersion matrices.     

关于我国城镇最佳规模的实证检验

关于城镇最佳规模问题,在我国学术界和政府部门一直存在争议.通过对相关理论分歧的归纳和西方学者关于最佳城镇规模研究的梳理,借鉴英国学者巴顿(K.J.Buton)的理论,运用我国的城市统计资料构建计量模型,分别从行政管理角度最佳、市民角度最佳和企业生产角度最佳三个方面,实证检验了我国最佳的城镇人口规模.相关结论对于正确选择我国的城市化道路,推动政府部门合理规划城市发展,具有一定的参考价值.     

Estimation in a growth study with irregular measurement times

Between 1982 and 1988 a growth study was carried out at the Division of Pediatric Oncology of the University Hospital of Groningen. A special feature of the project was that sample sizes are small and that ages at entry may be very different. In addition the intended design was not fully complied with. This paper highlights some aspects of the statistical analysis which is based on (1) reference scores, (2) statistical procedures allowing for an irregular pattern of measurement times caused by missing data and shifted measurement times.     

The asymptotic behaviour of the estimated generalized least squares method in the linear regression model

Abstract  In the linear regression model the generalized least squares (GLS) method is only applicable if the covariance matrix of the errors is known but for a scalar factor. Otherwise an estimator for this matrix has to be used. Then we speak of the estimated generalized least squares (EGLS) method. In this paper the asymptotic behaviour of both methods is compared. Results are applied to some standard models commonly used in econometrics     

On the selection of forecasting models

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean squared error (PMSE) in simulated out-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and finite-sample properties of these methods in terms of their ability to mimimize the true out-of-sample PMSE, allowing for possible misspecification of the forecast models under consideration. We show that under suitable conditions the IC method will be consistent for the best approximating model among the candidate models. In contrast, under standard assumptions the SOOS method, whether based on recursive or rolling regressions, will select overparameterized models with positive probability, resulting in excessive finite-sample PMSEs.     

Re-examining 50-year-old OLS estimates of the Klein–Goldberger model

This paper examines the ordinary least squares estimates of the Klein–Goldberger model by Fox ( Journal of Political Economy , 64 , 1956, 128). Because Klein and Goldberger published the data set with the model, it is possible to re-examine Fox's results years later, and investigate the accuracy with which these estimates were calculated. The examination reported in this paper was conducted by making independent estimates using three different modern econometric software packages. This examination reveals that the Fox estimates for a number of the equations of this model are replicable, to the two or three digits reported by Fox. Fox's results for other equations cannot be replicated. Not all the reasons for this lack of replicability can be determined, but in several cases the computational methods used by Fox and his assistants have been found to be faulty by modern computational standards.     

Partial Minimax Estimation in Regression Analysis

The general minimax estimator of the linear regression model is applicable when the whole parameter vector is restricted to an ellipsoid. In many applications, however, it is more realistic to assume that only a part of the parameter set is constrained. For this case an alternative minimax approach is developed.