时域漂移的泛克里格模型

将地质统计学中的泛克里格模型的漂移在时间域上进行计算, 在满足无偏性估计要求的条件下, 得到泛克里格模型的一种新形式。与原模型比较, 所得模型能够体现数据的时空特性, 并且模型形式得到简化。该模型适用于具有长期观测资料, 同时观测位置相对固定的估计和预测问题。     

自由网平差中若干问题的讨论

自由网平差主要有三种:秩亏网平差、拟稳平差和(范数)加权秩亏网平差,而(范数)加权秩亏网平差是自由网平差的普遍形式。三种自由网平差的结果可以相互转换,且无需求d阶逆阵。三种自由网平差的解与经典自由网平差的解一样,具有无偏性,且x_r是最优无偏估计,x_s是部分最优,x_p是加权最优。     

基于电流理论识别多物种扩散廊道的优先度——以西南西双版纳为例（英文）

Ecological corridor networks can efficiently improve regional landscape connectivity. Corridors for multiple faunal species movements are receiving increasing attention and graph theory is considered a promising way to explore landscape connectivity. In Xishuangbanna, the circuit theory was applied to explore the corridor networks for biodiversity for the first time. In addition, disturbances caused by the road network and the protection efficiency of National Nature Reserves and planned area for corridors were evaluated. Results indicated that the regional corridor networks could be estimated using a modified circuit method and Zonation model. Spatially, the key corridors were concentrated in the central-western, southeastern and northern regions. We detected 66 main intersections between key corridors and the road buffer. Of these points, 65% are forest, 23% grassland and 12% farmland. More than half of the area of National Nature Reserves constituted the top 50% of the corridors, and the planned corridor areas could efficiently protect some key corridors. However, these reserves only protected about 17% of regional key corridors, and the corridor conservation area in the western and northern regions were absent. The issues addressed in our study aided in the elucidation of the importance of regional landscape connectivity assessments and operational approaches in conservation planning.     

参数法CPⅢ精密三角高程控制网数据处理方法

针对差分法的局限性,提出了参数法CPⅢ精密三角高程控制网平差模型。试验证明,该模型算法简单、数据利用率高,避免了差分法观测值之间的相关性等问题,精度也较差分法有所提高。同时本文也对CPⅢ精密三角高程控制网严密定权问题进行了探讨,试验证明,在竖直角小于3°的情况下,三角高程测距误差的影响可忽略不计,因此严密定权前后CPⅢ精密三角高程控制网精度未有显著变化。     

On a stronger-than-best property for best prediction

The minimum mean squared error (MMSE) criterion is a popular criterion for devising best predictors. In case of linear predictors, it has the advantage that no further distributional assumptions need to be made, other then about the first- and second-order moments. In the spatial and Earth sciences, it is the best linear unbiased predictor (BLUP) that is used most often. Despite the fact that in this case only the first- and second-order moments need to be known, one often still makes statements about the complete distribution, in particular when statistical testing is involved. For such cases, one can do better than the BLUP, as shown in Teunissen (J Geod. doi: 10.1007/s00190-007-0140-6, 2006), and thus devise predictors that have a smaller MMSE than the BLUP. Hence, these predictors are to be preferred over the BLUP, if one really values the MMSE-criterion. In the present contribution, we will show, however, that the BLUP has another optimality property than the MMSE-property, provided that the distribution is Gaussian. It will be shown that in the Gaussian case, the prediction error of the BLUP has the highest possible probability of all linear unbiased predictors of being bounded in the weighted squared norm sense. This is a stronger property than the often advertised MMSE-property of the BLUP.     

Quadratic covariance estimation and equivalence of predictions

This article illustrates the use of linear and nonlinear regression models to obtain quadratic estimates of covariance parameters. These models lead to new insights into the motivation behind estimation methods, the relationships between different methods, and the relationship of covariance estimation to prediction. In particular, we derive the standard estimating equations for minimum norm quadratic unbiased translation invariant estimates (MINQUEs) from an appropriate linear model. Connections between the linear model, minimum variance quadratic unbiased translation invariant estimates (MIVQUEs), and MINQUEs are examined and we provide a minimum norm justification for the use of one-step normal theory maximum likelihood estimates. A nonlinear regression model is used to define MINQUEs for nonlinear covariance structures and obtain REML estimates. Finally, the equivalence of predictions under various models is examined when covariance parameters are estimated. In particular, we establish that when using MINQUE, iterative MINQUE, or restricted maximum likelihood (REML) estimates, the choice between a stationary covariance function and an intrinsically stationary semivariogram is irrelevant to predictions and estimated prediction variances.     

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