部分变量误差模型的极大似然估计

针对变量误差模型系数矩阵含有常数项的情况,部分变量误差模型在平差时只提取系数矩阵中的随机元素进行处理,较大程度的减少了参数估计的个数.为了进一步提高部分变量误差模型的参数估计效率,文中根据极大似然估计原理提出了一种部分变量误差模型的极大似然估计算法,并分析和比较了该算法与文献中几种算法的关系,证明算法之间的等价性.最后...     

Mapping local regression for spatial object-pairs

Local regression methods such as geographically weighted regression (GWR) can provide specific information about individual locations (or places) in spatial analysis that is useful for mapping nonstationary covariate relationships. However, the distance-based weighting schemes used in GWR are only adaptable for spatial objects that are point or area features. In particular, spatial object-pairs pose a challenge for local analysis because they have a linear dimensionality rather than a point dimensionality. This paper proposes using an alternative local regression model – quantile regression (QR) – for investigating the stationarity of regression parameters with respect to these linear features as well as facilitating the visualization of the results. An empirical example of a gravity model analysis of trade patterns within Europe is used to illustrate the utility of the proposed method.     

A universal formula of maximum likelihood estimation of variance-covariance components

The derivation of a universal formula for the variance-covariance component estimation is discussed. The formula is derived adopting the universal functional model (the condition adjustment with unknown parameters and constraints among the parameters),and is based on the maximum likelihood principle. The derived formula in this paper can be applied to all adjustment models for estimating variance-covariance components, which expands the formulas given by K. Kubik (1970)and K. R. Koch (1986).Besides, it is proved that the estimator given in this paper is equivalent to that of Helmert type and best quadratic unbiased estimation (BQUE).     

A linear regression solution to the spatial autocorrelation problem

The Moran Coefficient spatial autocorrelation index can be decomposed into orthogonal map pattern components. This decomposition relates it directly to standard linear regression, in which corresponding eigenvectors can be used as predictors. This paper reports comparative results between these linear regressions and their auto-Gaussian counterparts for the following georeferenced data sets: Columbus (Ohio) crime, Ottawa-Hull median family income, Toronto population density, southwest Ohio unemployment, Syracuse pediatric lead poisoning, and Glasgow standard mortality rates, and a small remotely sensed image of the High Peak district. This methodology is extended to auto-logistic and auto-Poisson situations, with selected data analyses including percentage of urban population across Puerto Rico, and the frequency of SIDs cases across North Carolina. These data analytic results suggest that this approach to georeferenced data analysis offers considerable promise. Received: 18 February 1999/Accepted: 17 September 1999     

A sampling approach to estimate the log determinant used in spatial likelihood problems

Likelihood-based methods for modeling multivariate Gaussian spatial data have desirable statistical characteristics, but the practicality of these methods for massive georeferenced data sets is often questioned. A sampling algorithm is proposed that exploits a relationship involving log-pivots arising from matrix decompositions used to compute the log determinant term that appears in the model likelihood. We demonstrate that the method can be used to successfully estimate log-determinants for large numbers of observations. Specifically, we produce an log-determinant estimate for a 3,954,400 by 3,954,400 matrix in less than two minutes on a desktop computer. The proposed method involves computations that are independent, making it amenable to out-of-core computation as well as to coarse-grained parallel or distributed processing. The proposed technique yields an estimated log-determinant and associated confidence interval.

Geographically weighted regression with the integration of machine learning for spatial prediction

Journal of Geographical Systems - Conventional methods of machine learning have been widely used to generate spatial prediction models because such methods can adaptively learn the mapping...     

Empirical spatial econometric modelling of small scale neighbourhood

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.     

空间与光谱维度的高光谱图像噪声估计

高光谱图像能够获取地物精细的光谱诊断特征,但受限于多谱段分光的成像机制,图像各个谱段上光成像的能量不足,信噪比难以提升.高光谱图像噪声类型与强度的准确估计,是提升高光谱图像去噪性能的关键,也是优化其成像系统设计的重要依据.现有高光谱图像噪声估计算法通常将不同类型的图像噪声作为一个整体,并未充分考虑不同类型噪声的区别.本...     

Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method

Hedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms the spatial expansion method in terms of explanatory power and predictive accuracy.

南极IGS基准站时间序列极大似然法分析

针对南极大陆IGS的周期特性、噪声特性、趋势特性等问题,该文采用极大似然法和功率谱分析法分析南极地区8个IGS永久观测站坐标的时间序列。研究结果表明,该地区水平和垂直方向上均有较明显的年周期项,垂直方向上还普遍存在半年周期项。闪烁噪声与白噪声组合能更好描述南极基准站3个分量上噪声特性,各方向上的速率不确定度比只考虑白噪声时要大10~15倍。通过估算基准站水平方向的运动速率,发现该方向上具有较规律的运动趋势:南极洲正缓慢向南美洲移动。     

Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters

Because eigenvector spatial filtering (ESF) provides a relatively simple and successful method to account for spatial autocorrelation in regression, increasingly it has been adopted in various fields. Although ESF can be easily implemented with a stepwise procedure, such as traditional stepwise regression, its computational efficiency can be further improved. Two major computational components in ESF are extracting eigenvectors and identifying a subset of these eigenvectors. This paper focuses on how a subset of eigenvectors can be efficiently and effectively identified. A simulation experiment summarized in this paper shows that, with a well-prepared candidate eigenvector set, ESF can effectively account for spatial autocorrelation and achieve computational efficiency. This paper further proposes a nonlinear equation for constructing an ideal candidate eigenvector set based on the results of the simulation experiment.     

Robust support vector regression for biophysical variable estimation from remotely sensed images

This letter introduces the /spl epsiv/-Huber loss function in the support vector regression (SVR) formulation for the estimation of biophysical parameters extracted from remotely sensed data. This cost function can handle the different types of noise contained in the dataset. The method is successfully compared to other cost functions in the SVR framework, neural networks and classical bio-optical models for the particular case of the estimation of ocean chlorophyll concentration from satellite remote sensing data. The proposed model provides more accurate, less biased, and improved robust estimation results on the considered case study, especially significant when few in situ measurements are available.     

线性回归参数的总体最小二乘估计新算法

将一元线性回归总体最小二乘平差模型展开后,以因变量和自变量改正数的平方和最小为约束条件,推导其总体最小二乘的迭代算法,并将模型扩展到多元线性回归,进一步得到线性回归模型的总体最小二乘算法。通过实例分析,证明算法的可行性和合理性。     

Constrained variants of the gravity model and spatial dependence: model specification and estimation issues

In this paper, we distinguish three constrained variants of the gravity model of spatial interaction: doubly constrained, production constrained and attraction constrained exponential gravity models. These model variants include origin- and/or destination-specific balancing factors that act as constraints to ensure that the estimated rows and columns of the flow data matrix sum to the observed row and column totals. Because flows are typically counts, the Poisson rather than the normal probability model specification furnishes the appropriate statistical distribution, and parameter estimation can be achieved via Poisson regression. This probability model specification motivates the use of origin and/or destination fixed effects or—under certain conditions—the use of origin- and/or destination-specific random effects for model estimation. The paper establishes theoretical connections between balancing factors, fixed effects represented by binary indicator variables and random effects. The results pertaining to both the doubly and singly constrained cases of spatial interaction are illustrated with an empirical example while accounting for spatial dependence between flows from locations neighbouring both the origins and destinations during estimation.     

高光谱图像空间光谱维去相关噪声评估

高光谱图像噪声评估既是评价图像质量的重要内容,也是衡量传感器性能的重要指标。一般噪声评估方法通过对图像规则分割或利用某种距离准则对图像进行连续性分割,计算图像子块的局部标准差或多元线性回归的残差来实现对图像噪声的估计。但这些方法获取的图像子块并不是完全均匀的,图像子块中仍然会存在地物边界,导致图像噪声评估的结果不准确。为了有效提取图像中的均匀子块,本文提出了一种优化的空间光谱维去相关（OSSDC）方法,基于光谱角距离和欧氏距离双重判定,从光谱曲线的形状和数值上寻找相似像元,获取图像中的均匀子块,然后利用多元线性回归计算残差实现对图像噪声的估算。利用模拟图像和实际航空飞行实验获取的高光谱图像对优化算法进行检验,同时与几种常用噪声评估方法进行对比分析,结果表明优化后的算法计算结果更准确,稳定性和适用性优于其他方法。     

回归模型误差补偿半参数法中正则矩阵任意时的参数估计

用试验观测数据建立的回归拟合模型不可避免地存在模型误差,将模型误差视为非参数分量列入回归模型的半参数模型是一种重要的回归模型.本文利用补偿最小二乘原理,导出了顾及预测点的半参数回归模型参数及模型误差的估计量,并对估计量进行精度评定,给出了估计量及精度评定的相应公式.在理论分析的基础上,得出了只要正则矩阵R的选取使法方程...     

根据分形理论与五指标评价体系构建NDVI连续空间尺度转换模型

针对目前基于统计或物理模型方法的升尺度转换研究中存在的不足,以归一化差分植被指数NDVI为研究对象,基于分形理论提出一种连续空间升尺度转换模型CSSM(Continuous Spatial Scaling Model)构建方法。所构建的模型尺度适用范围更广,且具有一定的物理意义。针对已有研究尚未解决的模型构建的最合理尺度层级确定问题,结合原有的统计学四指标评价体系(r、p、rlo、rup),融入了真实性检验应用效能评价指标(Max_of_abs(Error)),建立了一个基于五指标评价体系的模型构建最合理尺度层级确定方法。以北海市沙田半岛Landsat ETM+影像为实验影像,设定r≥0.8,p0.05,rlo≥r≤rup及Max_of_abs(Error)≤0.05为评价体系的边界条件,从追求模型尺度适用范围更大的角度考虑,确定出该影像模型构建的最合理尺度层级Level=267,则该模型最高可对30 m×267即8 km分辨率遥感影像进行NDVI验证。通过动态调整此评价体系的边界条件,实现了最合理尺度层级取值的敏感性分析。这些工作使得基于分形理论的NDVI's CSSM构建研究更为系统。     

Solution and econometric estimation of spatial dynamic models in continuous space and continuous time

 This paper presents and demonstrates a general approach to solving spatial dynamic models in continuous space and continuous time that characterize the behaviour of intertemporally and interspatially optimizing agents and estimating from discrete data the parameters of such models. The approach involves the use of a projection method to solve the models and a quasi-Newton algorithm to update quasi-FIML parameter estimates. Received: 26 July 2000 / Accepted: 31 January 2001