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For spatial linear models, the classical maximum-likelihood estimators of both regression coefficients and variance components can be biased when the covariates are measured with errors. This work introduces a theoretically backed-up estimation framework for the spatial linear errors-in-variables model in a functional approach. Compared with the structural models, the functional approach treats the unobserved true covariates as fixed unknown parameters without imposing additional structures, thus leading to more robust parameter inference. Our model parameters are estimated simultaneously based on a set of unbiased estimating equations. Under some regularity conditions, we prove the consistency of the proposed estimating-equation estimators and derive their asymptotic distribution. In addition, a consistent variance estimator is developed for the estimating-equation estimators. To handle large spatial datasets, we provide two approaches to obtain scalable estimations based on our proposed estimating equations, where the required computational time and storage are reduced to be linear with sample size for each estimating-function evaluation. Simulation studies under different settings show that our estimators are consistent and the scalable algorithms work well. Finally, the proposed method is applied to studying the relationship between Arctic sea ice and related geophysical variables. for this article are available online. [ABSTRACT FROM AUTHOR]

Motivated by a satellite remote sensing mission, this article proposes a multivariable errors-in-variables (EIV) regression model with heteroscedastic errors for relating the satellite data products to similar products from a well-characterized but globally sparse ground-based dataset. In the remote sensing setting, the regression model is used to estimate the global divisor for the satellite data. The error structure of the proposed EIV model comprises two components: A random-error component whose variance is inversely proportional to sample size of underlying individual observations which are aggregated to obtain the regression data, and a systematic-error component whose variance remains the same as the underlying sample size increases. In this article, we discuss parameter identifiability for the proposed model and obtain estimates from two-stage parameter estimation. We illustrate our proposed procedure through both simulation studies and an application to validating measurements of atmospheric column-averaged CO2 from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. The validation uses coincident target-mode OCO-2 data that are temporally and spatially sparse and ground-based measurements from the Total Carbon Column Observing Network (TCCON) that are spatially sparse but more accurate. Supplementary materials for the article are available online. [ABSTRACT FROM AUTHOR]

Arctic sea ice extent has been of considerable interest to scientists in recent years, mainly due to its decreasing temporal trend over the past 20 years. In this article, we propose a hierarchical spatio‐temporal generalized linear model for binary Arctic sea‐ice‐extent data, where statistical dependencies in the data are modeled through a latent spatio‐temporal linear mixed effects model. By using a fixed number of spatial basis functions, the resulting model achieves both dimension reduction and non‐stationarity for spatial fields at different time points. An EM algorithm is proposed to estimate model parameters, and an empirical–hierarchical‐modeling approach is applied to obtain the predictive distribution of the latent spatio‐temporal process. We illustrate the accuracy of the parameter estimation through a simulation study. The hierarchical model is applied to spatial Arctic sea‐ice‐extent data in the month of September for 20 years in the recent past, where several posterior summaries are obtained to detect the changes of Arctic sea ice cover. In particular, we consider a time series of latent 2 × 2 tables to infer the spatial changes of Arctic sea ice over time. [ABSTRACT FROM AUTHOR]

The H 2 Y⋯Ag X (X = F, Cl, Br, I; Y = O, S) complexes have been investigated by ab initio methods. All complexes are non-planar with C s symmetry, and the Ag X bond is strengthened during complexation processes. The binding energies of complexes were accurately calculated using the CCSD(T)/CBS by extrapolation method. The interaction between H 2 Y and AgX is weakened as X varies from F to I. The intermolecular interactions in these complexes show partial covalent character; moreover, the intermolecular interactions in H 2 S⋯Ag X (X = F, Cl, Br, I) complexes are stronger than those in H 2 O⋯Ag X complexes. [ABSTRACT FROM AUTHOR]

In this paper, a broadband high-efficiency supply modulator is proposed. This modulator, with a node controlled by \Delta \Sigma modulator's output, consists of a conventional Class-E amplifier and a Class-E rectifier. The configurations and operation principles of this modulator are introduced to explain the envelope reconstruction process. The performance of this modulator in steady state is analyzed to show the reason for high-efficiency operations assisted by a shunt diode, especially for short-circuited load resistance. Its performance for broadband envelopes amplification is analyzed using numerical difference equations. The results show that efficiency degradation and nonlinearity of this modulator are inevitable due to transient responses. The most significant factors related to the performance degradation, such as switch loss and conduction loss, are determined and verified by numerical simulations. The simulations for one-carrier wavelength code-division multiple-access (WCDMA) envelopes show that over 80% average efficiency and normalized mean square error (NMSE) of -\16 dB can be obtained in numerical examples even considering the nonidealities. An experimental supply modulator is given to verify the proposed modulator. In this experiment, 48.6% average efficiency and NMSE of -\15.9 dB are measured for WCDMA envelopes of 6.1-dB peak-to-average-power ratio. [ABSTRACT FROM PUBLISHER]