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Robust estimation of unknown parameters in linear models with only a single error component has been widely investigated. However only a small percentage of literature treats robust estimate of variance components in heteroscedastically mixed models. The correction-based pseudoobservation method, the p(x)-function-based robust maximum likelihood estimate (MLE) and restricted maximum likelihood estimate methods, as well as the robust Helmert method are the three kinds of typical robust methods for estimating variance components of linear mixed models. However, they are generally affected by different types of scoring functions and various tuning factors based on M estimate defined by Huber from the maximum likelihood type of estimation. In addition, the pseudoobservation method will encounter risks of incorrect corrections due to the misidentification of gross errors. In this paper, gross errors and random or normal errors are assumed to be occasionally additive, independent, normally distributed with different scales, and all are regarded as missing and/or unobservable data. Together with the observations, they form a complete data problem where the unknown parameters and variance components need to be estimated. The expectation-maximization (EM) algorithm for finding the MLEs is robustified by estimating the variances of gross errors by defining weights and proposing to jointly solve the robust estimation of the unknown parameters and variance components. A numerical example of a global positioning system baseline network shows that the robustified EM algorithm can find a reliable estimate of unknown parameters and variance components, and efficiently separate the gross errors and subrandom effects or errors by computing their respective Bayesian estimate. DOI: 10.1061/(ASCE)0733-9453(2009)135:1(1) CE Database subject headings: Geodetic surveys; Data processing; Methodology; Global positioning; China; Parameters; Algorithms.

The principal component analysis is a widely used method for dimensionality reduction of hyperspectral remote sensing images. In task - oriented work, the principal component selection method based on cumulative variance contribution rate is not ideal. To address the problem of principal component selection after principal component analysis transformation, a method of principal component selection based on spatial statistics is proposed. The selection of principal components is performed by calculating the values of the semi - variogram parameter range and partial sill/sill of each principal component. The magnitude of a range is used to judge the range of spatial correlation of each principal component, and the partial SII/SHI is used to judge the strength of spatial correlation of each principal component. The simulation proves that the variable range and partial siil/siil can effectively exprees the range and strength of spatial correlation of hyperspectral remote sensing images. Based on the experiment of real hyperspectral remote sensing images, the empirical threshold of principal component selection is determined from subjective and objective aspects, that is, the range is 2. 5, and the partial siil/siil is 0. 2. According to the classification results based on the support vector machine algorithm, compared with traditional methods, the principal components with beeter image quality can be screened by using variable range and partial siil/siil, which can not only achieve the purpose of dimensionality reduction, but also ensure high classification accuracy. [ABSTRACT FROM AUTHOR]

The first application of GaoFen-3 synthetic aperture radar (SAR) interferometry to monitoring co-seismic deformation of an earthquake is presented in this paper. First, two GaoFen-3 SAR images spanning the 2017 surface-wave magnitude ( M s ) 7.0 Jiuzhaigou earthquake were mosaicked, coregistered and processed into an interferogram with visible fringes. After the removal of severe orbital errors and unwrapping, the co-seismic deformation field is obtained. Finally, the fault parameters and slip distribution are determined based on the deformation field. To evaluate the performance of GaoFen-3 in this case, the interferogram, co-seismic deformation field, derived fault parameters and slip distribution are compared with those derived from Radarsat-2, Sentinel-1A and Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2). The comparisons between the deformation field obtained from GaoFen-3 and those obtained from Radarsat-2 and Sentinel-1A show Pearson's correlation coefficient ( r) values > 90%, demonstrating that GaoFen-3 interferometry can measure the co-seismic deformation effectively. The resultant slip model obtained by GaoFen-3 interferometric measurements shows a simple, elliptical pattern of slip, reaching a maximum of approximately 1 m at depths of 10–13 km. The rake angle is almost 0 ∘ , which suggests left-lateral strike-slip motion, consistent with previous studies. [ABSTRACT FROM AUTHOR]

Geographically weighted regression (GWR) is an important local method to explore spatial non-stationarity in data relationships. It has been repeatedly used to examine spatially varying relationships between epidemic diseases and predictors. Malaria, a serious parasitic disease around the world, shows spatial clustering in areas at risk. In this article, we used GWR to explore the local determinants of malaria incidences over a 7-year period in northern China, a typical mid-latitude, high-risk malaria area. Normalized difference vegetation index (NDVI), land surface temperature (LST), temperature difference, elevation, water density index (WDI) and gross domestic product (GDP) were selected as predictors. Results showed that both positively and negatively local effects on malaria incidences appeared for all predictors except for WDI and GDP. The GWR model calibrations successfully depicted spatial variations in the effect sizes and levels of parameters, and also showed substantially improvements in terms of goodness of fits in contrast to the corresponding non-spatial ordinary least squares (OLS) model fits. For example, the diagnostic information of the OLS fit for the 7-year average case is R2 = 0.243 and AICc = 837.99, while significant improvement has been made by the GWR calibration with R2 = 0.800 and AICc = 618.54. [ABSTRACT FROM AUTHOR]