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We present a method for calculating first-order response properties in phaseless auxiliary field quantum Monte Carlo by applying automatic differentiation (AD). Biases and statistical efficiency of the resulting estimators are discussed. Our approach demonstrates that AD enables the calculation of reduced density matrices with the same computational cost scaling per sample as energy calculations, accompanied by a cost prefactor of less than four in our numerical calculations. We investigate the role of self-consistency and trial orbital choice in property calculations. We find that orbitals obtained using density functional theory perform well for the dipole moments of selected molecules compared to those optimized self-consistently. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of testing the hypothesis of what the authors call a nested block circular‐compound symmetric (NBCCS) covariance structure for the population covariance matrix. This is a covariance structure which has an outer block compound symmetric structure, where the diagonal blocks are themselves block circular matrices, while the off‐diagonal blocks are formed by all equal matrices. The NBCCS null hypothesis is decomposed into sub‐hypotheses, allowing this way for a simpler way to obtain a likelihood ratio test and its associated statistic. The exact moments of this statistic are derived, and its distribution is carefully examined. Given the complicated nature of this distribution, highly precise near‐exact distributions were developed. Numerical studies are conducted to assess the proximity between these near‐exact distributions and the exact distribution, highlighting the performance of these approximations, even in the case of very small sample sizes. Furthermore, three datasets, on bone mineral content, metabolic rates of glucose, and soil moisture are used to exemplify the practical application of the methodology derived in this study. [ABSTRACT FROM AUTHOR]

In this work, we introduce the probabilistic inversion of tomographic complex resistivity (CR) measurements using the Hamiltonian Monte Carlo (HMC) method. The posterior model distribution on which our approach operates accounts for the underlying complex-valued nature of the CR imaging problem accurately by including the individual errors of the measured impedance magnitude and phase, allowing for the application of independent regularization on the inferred subsurface conductivity magnitude and phase, and incorporating the effects of cross-sensitivities. As the tomographic CR inverse problem is nonlinear, of high dimension and features strong correlations between model parameters, efficiently sampling from the posterior model distribution is challenging. To meet this challenge we use HMC, a Markov-chain Monte Carlo method that incorporates gradient information to achieve efficient model updates. To maximize the benefit of a given number of forward calculations, we use the No-U-Turn sampler (NUTS) as a variant of HMC. We demonstrate the probabilistic inversion approach on a synthetic CR tomography measurement. The NUTS succeeds in creating a sample of the posterior model distribution that provides us with the ability to analyse correlations between model parameters and to calculate statistical estimators of interest, such as the mean model and the covariance matrix. Our results provide a strong basis for the characterization of the posterior model distribution and uncertainty quantification in the context of the tomographic CR inverse problem. [ABSTRACT FROM AUTHOR]

We introduce a structural vector autoregressive model with endogenously switching conditional covariance matrix. The structural shocks are identified by simultaneously diagonalizing the reduced form error covariance matrices. It is not, however, always clear whether the condition for the full statistical identification is satisfied, and its validity is difficult to justify formally. Therefore, we provide general sets of conditions, that allow to combine sign and zero restrictions on the impact matrix, for identifying a subset of the shocks when the condition for statistical identification of the model fails. In an empirical application to the effects of the U.S. monetary policy shock, we find that a contractionary monetary policy shock significantly decreases output in a persistent hump-shaped pattern. Prices decrease permanently, but there is short-run inertia in their response. The accompanying R package gmvarkit provides a comprehensive set of tools for numerical analysis of the model. [ABSTRACT FROM AUTHOR]

Motivated by the ongoing debate on whether optimal or naive diversification should be preferred when distributing wealth across investment alternatives, this article investigates how the choice of covariance estimator affects mean‐variance portfolio selection. In an environment tailored to ideal tradability, we construct optimal commodity futures portfolios based on 12 promising covariance matrix estimators and compare their out‐of‐sample investment performance to a simple, equally weighted investment strategy by means of bootstrap testing. We find that neither the naive allocation approach nor the advanced covariance estimators can outperform the traditional sample covariance matrix. Because this empirical result is robust to modifications of the research design (including alternative investigation periods, data frequencies, estimation window sizes, holding period lengths, weight constraint specifications, and transaction cost levels), it opposes the recurrent suggestion of the equity‐oriented literature that the sample covariance matrix should not be used for the purpose of portfolio optimization. [ABSTRACT FROM AUTHOR]

AbstractThe estimation of large precision matrices is crucial in modern multivariate analysis. Traditional sparsity assumptions, while useful, often fall short of accurately capturing the dependencies among features. This paper addresses this limitation by focusing on precision matrix estimation for multivariate data characterized by a flexible yet unknown group structure. We introduce a novel approach that begins with the detection of this unknown group structure, clustering features within the low-dimensional space defined by the leading eigenvectors of the sample covariance matrix. Following this, we employ group-wise multivariate response linear regressions, guided by the identified group memberships, to estimate the precision matrix. We rigorously establish the theoretical foundations of our proposed method for both group detection and precision matrix estimation. The superior numerical performance of our approach is demonstrated through comprehensive simulation experiments and a comparative analysis with established methods in the field. Additionally, we apply our method to a real breast cancer dataset, showcasing its practical utility and effectiveness. [ABSTRACT FROM AUTHOR]

ABSTRACT Analysis of healthcare utilization, such as hospitalization duration and medical costs, is crucial for policymakers and doctors in experimental and epidemiological investigations. Herein, we examine the healthcare utilization data of patients with systemic lupus erythematosus (SLE). The characteristics of the SLE data were measured over a 10‐year period with outliers. Multivariate linear models with multivariate normal error distributions are commonly used to evaluate long series of multivariate longitudinal data. However, when there are outliers or heavy tails in the data, such as those based on healthcare utilization, the assumption of multivariate normality may be too strong, resulting in biased estimates. To address this, we propose multivariate t‐linear models (MTLMs) with an autoregressive moving‐average (ARMA) covariance matrix. Modeling the covariance matrix for multivariate longitudinal data is difficult since the covariance matrix is high dimensional and must be positive‐definite. To address these, we employ a modified ARMA Cholesky decomposition and hypersphere decomposition. Several simulation studies are conducted to demonstrate the performance, robustness, and flexibility of the proposed models. The proposed MTLMs with ARMA structured covariance matrix are applied to analyze the healthcare utilization data of patients with SLE. [ABSTRACT FROM AUTHOR]

Active galactic nuclei (AGN) are the signposts of black hole growth, and likely play an important role in galaxy evolution. An outstanding question is whether AGN of different spectral types indicate different evolutionary stages in the coevolution of black holes and galaxies. We present the angular correlation function between an AGN sample selected from Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) optical photometry and Wide-field Infrared Survey Explorer mid-IR photometry and a luminous red galaxy (LRG) sample from HSC-SSP. We investigate AGN clustering strength as a function of luminosity and spectral features across three independent HSC fields totaling ∼600 deg2, for z ∈ 0.6 −1.2 and AGN with L 6 μ m > 3 × 1044 erg s−1. There are ∼28,500 AGN and ∼1.5 million LRGs in our primary analysis. We determine the average halo mass for the full AGN sample (M h ≈ 1012.9 h −1 M ⊙), and note that it does not evolve significantly as a function of redshift (over this narrow range) or luminosity. We find that, on average, unobscured AGN (M h ≈ 1013.3 h −1 M ⊙) occupy ∼4.5× more massive halos than obscured AGN (M h ≈ 1012.6 h −1 M ⊙), at 5 σ statistical significance using 1D uncertainties, and at 3 σ using the full covariance matrix, suggesting a physical difference between unobscured and obscured AGN, beyond the line-of-sight viewing angle. Furthermore, we find evidence for a halo mass dependence on reddening level within the Type I AGN population, which could support the existence of a dust-obscured phase. However, we also find that quite small systematic shifts in the redshift distributions of the AGN sample could explain current and previously observed differences in M h . [ABSTRACT FROM AUTHOR]

AbstractMissing data have emerged in broad disciplines such as biology, geophysics, economics, public health, and social science. This article explores the optimal estimation of high-dimensional covariance matrix with missing data over a general sparse space ℋε(cn, p). First, the upper bounds of adaptive entrywise thresholding estimator are proposed. Then the minimax lower bound is established by a simple and effective approach. Finally, numerical simulations and real data analysis demonstrate the advantages of our estimator Σ̂τ over the estimator Σ̂at of Cai and Zhang (2016). [ABSTRACT FROM AUTHOR]

ABSTRACT Generalized estimating equations (GEE) are of great importance in analyzing clustered data without full specification of multivariate distributions. A recent approach by Luo and Pan jointly models the mean, variance, and correlation coefficients of clustered data through three sets of regressions. We note that it represents a specific case of the more general estimating equations proposed by Yan and Fine which further allow the variance to depend on the mean through a variance function. In certain scenarios, the proposed variance estimators for the variance and correlation parameters in Luo and Pan may face challenges due to the subtle dependence induced by the nested structure of the estimating equations. We characterize specific model settings where their variance estimation approach may encounter limitations and illustrate how the variance estimators in Yan and Fine can correctly account for such dependencies. In addition, we introduce a novel model selection criterion that enables the simultaneous selection of the mean‐scale‐correlation model. The sandwich variance estimator and the proposed model selection criterion are tested by several simulation studies and real data analysis, which validate its effectiveness in variance estimation and model selection. Our work also extends the R package geepack with the flexibility to apply different working covariance matrices for the variance and correlation structures. [ABSTRACT FROM AUTHOR]

AbstractThis study introduces a novel multimode online monitoring approach that employs a covariance test for the analysis of multimode, high-dimensional, and non-normally distributed data from Internet of Things (IoT) devices. The methodology involves estimating the covariance matrix using a linear shrinkage estimation method, followed by the calculation of a sparse principal eigenvalue test statistic based on the estimated covariance matrix. Additionally, an Exponentially Weighted Moving Average (EWMA) control chart is designed, incorporating a sliding window and a mode transition constraint parameter, referred to as the MMEWMA chart. To evaluate the performance of the MMEWMA control chart, Monte Carlo simulations are conducted under various conditions, including dimensionality, data distribution, drift size, in-control sample size, and mode transition parameters. The results demonstrate that the proposed MMEWMA control chart significantly outperforms other covariance test-based control charts, particularly in scenarios characterized by substantial drift and non-normally distributed data. Furthermore, the method’s effectiveness is validated through the analysis of real IoT device data sourced from wind turbines. [ABSTRACT FROM AUTHOR]

New observations of ocean surface topography obtained by wide-swath satellite interferometry require new capabilities to process spatially correlated errors in order to assimilate these data into numerical models. The sea surface height (SSH) variations have to be weighted against other types of assimilated data using information on their precision, as represented by the inverse of the SSH error covariance matrix R. The latter can be well approximated by a block-circulant (BC) structure and, therefore, allows numerically efficient implementation in operational data assimilation (DA) systems. In this note, we extend the technique of approximating R for wide-swath altimeters by including the uncertainties associated with the state of the atmosphere. It is shown that such an extension keeps the BC approximation error within acceptable (±10%) bounds in a wide range of environmental conditions and could be beneficial for improving the accuracy of SSH retrievals from wide-swath altimeter observations. [ABSTRACT FROM AUTHOR]

This paper deals with the assessment of the effects of minor perturbations of data in the linear mixed measurement error models with Ridge estimation, based on the corrected score function. The local influence approach is used for assessing the influence of small perturbations on the parameter estimates. We examine different types of perturbation schemes including the covariance matrix of the conditional errors, case weight, response, and explanatory perturbation, to identify influential observations. A real data application and a simulation study illustrate the performance of the proposed diagnostics. [ABSTRACT FROM AUTHOR]

The usual Minimum Covariance Determinant (MCD) estimator of a covariance matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) that behave differently from the majority of cases, raising suspicion that they might belong to a different population. On the other hand, cellwise outliers are individual cells in the data matrix. When a row contains one or more outlying cells, the other cells in the same row still contain useful information that we wish to preserve. We propose a cellwise robust version of the MCD method, called cellMCD. Its main building blocks are observed likelihood and a penalty term on the number of flagged cellwise outliers. It possesses good breakdown properties. We construct a fast algorithm for cellMCD based on concentration steps (C-steps) that always lower the objective. The method performs well in simulations with cellwise outliers, and has high finite-sample efficiency on clean data. It is illustrated on real data with visualizations of the results. for this article are available online. [ABSTRACT FROM AUTHOR]

The super-resolution data assimilation (SRDA) enhances a low-resolution (LR) model with a Neural Network (NN) that has learned the differences between high and low-resolution models offline and performs data assimilation in high-resolution (HR). The method enhances the accuracy of the EnKF-LR system for a minor computational overhead. However, performance quickly saturates when the ensemble size is increased due to the error introduced by the NN. We therefore combine the SRDA with the mixed-resolution data assimilation method (MRDA) into a method called "Hybrid covariance super-resolution data assimilation" (Hybrid SRDA). The forecast step runs an ensemble at two resolutions (high and low). The assimilation is done in the HR space by performing super-resolution on the LR members with the NN. The assimilation uses the hybrid covariance that combines the emulated and dynamical HR members. The scheme is extensively tested with a quasi-geostrophic model in twin experiments, with the LR grid being twice coarser than the HR. The Hybrid SRDA outperforms the SRDA, the MRDA, and the EnKF-HR at a given computational cost. The benefit is the largest compared to the EnKF-HR for small ensembles. However, even with larger computational resources, using a mix of high and low-resolution members is worth it. Besides, the Hybrid SRDA, the EnKF-HR, and the SRDA, unlike the MRDA, prevent the smoothing of dynamical structures of the background error covariance matrix. The Hybrid SRDA method is also attractive because it is customizable to available resources. [ABSTRACT FROM AUTHOR]

In Gaussian graphical models, the likelihood equations must typically be solved iteratively. This paper investigates two algorithms: a version of iterative proportional scaling, which avoids inversion of large matrices, and an algorithm based on convex duality and operating on the covariance matrix by neighbourhood coordinate descent, which corresponds to the graphical lasso with zero penalty. For large, sparse graphs, the iterative proportional scaling algorithm appears feasible and has simple convergence properties. The algorithm based on neighbourhood coordinate descent is extremely fast and less dependent on sparsity, but needs a positive-definite starting value to converge. We provide an algorithm for finding such a starting value for graphs with low colouring number. As a consequence, we also obtain a simplified proof of existence of the maximum likelihood estimator in such cases. [ABSTRACT FROM AUTHOR]

Long-run covariance matrix estimation is the building block of time series inference. The corresponding difference-based estimator, which avoids detrending, has attracted considerable interest due to its robustness to both smooth and abrupt structural breaks and its competitive finite sample performance. However, existing methods mainly focus on estimators for the univariate process, while their direct and multivariate extensions for most linear models are asymptotically biased. We propose a novel difference-based and debiased long-run covariance matrix estimator for functional linear models with time-varying regression coefficients, allowing time series nonstationarity, long-range dependence, state heteroscedasticity and combinations thereof. We apply the new estimator to (i) the structural stability test, overcoming the notorious nonmonotonic power phenomena caused by piecewise smooth alternatives for regression coefficients, and (ii) the nonparametric residual-based tests for long memory, improving the performance via the residual-free formula of the proposed estimator. The effectiveness of the proposed method is justified theoretically and demonstrated by superior performance in simulation studies, while its usefulness is elaborated via real data analysis. Our method is implemented in the R package mlrv. [ABSTRACT FROM AUTHOR]

Finite mixture models have been widely used to model and analyze data from heterogeneous populations. In practical scenarios, these types of data often confront upper and/or lower detection limits due to the constraints imposed by experimental apparatuses. Additional complexity arises when measures of each mixture component significantly deviate from the normal distribution, manifesting characteristics such as multimodality, asymmetry, and heavy-tailed behavior, simultaneously. This paper introduces a flexible model tailored for censored data to address these intricacies, leveraging the finite mixture of skew-t distributions. An Expectation Conditional Maximization Either (ECME) algorithm, is developed to efficiently derive parameter estimates by iteratively maximizing the observed data log-likelihood function. The algorithm has closed-form expressions at the E-step that rely on formulas for the mean and variance of truncated skew-t distributions. Moreover, a method based on general information principles is presented for approximating the asymptotic covariance matrix of the estimators. Results obtained from the analysis of both simulated and real datasets demonstrate the proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

The degree of freedom (DOF) parameter plays a crucial role in the Student's t distribution as it affects the thickness of the distribution tails. Therefore, choosing an appropriate DOF parameter is essential for accurately modeling heavy-tailed noise. To improve estimation accuracy, this paper introduces a new robust Kalman filter based on moving window estimation to handle heavy-tailed noise. First, a sliding window based on Moving Horizon Estimation (MHE) is designed. By continuously utilizing the latest measurement information through the silding window, outliers that cause heavy-tailed noise can be better identified. Second, the noise is modeled as a Student's t distribution, and an appropriate conjugate prior distribution is selected for the unknown noise covariance matrix. The Variational Bayesian (VB) method is combined with the proposed MHE framework to jointly infer the unknown parameters, updating the DOF parameter to a Gamma distribution. Finally, through simulation experiments, the optimal number of iterations and MHE window length are determined to ensure estimation accuracy while reducing computational complexity. The simulation results show that the proposed filtering algorithm exhibits better robustness in handling heavy-tailed noise compared to traditional filters. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the universally optimal designs for estimating total effects under crossover models with partial interactions. We provide necessary and sufficient conditions for a symmetric design to be universally optimal, based on which algorithms can be used to derive optimal symmetric designs under any form of the within‐block covariance matrix. To cope with the computational complexity of algorithms when the experimental scale is too large, we provide the analytical form of optimal designs under the type‐H covariance matrix. We find that for a fixed number of treatments, say t$$ t $$, the number of distinct treatments appearing in the support sequences increases with the increase of the number of periods, k$$ k $$, until k≥t2$$ k\ge {t}^2 $$, in which case all t$$ t $$ treatments appear. The optimal design can be constructed from up to two representative sequences, within which each treatment appears in consecutive periods with equal or almost equal numbers of replications. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work considers Bayesian experimental design for the inverse boundary value problem of linear elasticity in a two-dimensional setting. The aim is to optimize the positions of compactly supported pressure activations on the boundary of the examined body in order to maximize the value of the resulting boundary deformations as data for the inverse problem of reconstructing the Lamé parameters inside the object. We resort to a linearized measurement model and adopt the framework of Bayesian experimental design, under the assumption that the prior and measurement noise distributions are mutually independent Gaussians. This enables the use of the standard Bayesian A-optimality criterion for deducing optimal positions for the pressure activations. The (second) derivatives of the boundary measurements with respect to the Lamé parameters and the positions of the boundary pressure activations are deduced to allow minimizing the corresponding objective function, i.e., the trace of the covariance matrix of the posterior distribution, by gradient-based optimization algorithms. Two-dimensional numerical experiments are performed to test the functionality of our approach: all introduced algorithms are able to improve experimental designs, but only exhaustive search reliably finds a global minimizer. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to extend the mixture factor analyzers (MFA) model to handle missing and heavy-tailed data. In this model, the distribution of factors loading and errors arise from the multivariate normal mean-variance mixture of the Birnbaum-Saunders (NMVBS) distribution. By using the structures covariance matrix, we introduce parsimonious MFA based on NMVBS distribution. An Expectation Maximization (EM)-type algorithm is developed for parameter estimation. Simulations study and real data sets represent the efficiency and performance of the proposed model. [ABSTRACT FROM AUTHOR]

Although conceptually attractive, the use of ensemble data assimilation methods, such as the ensemble Kalman filter (EnKF), can be constrained by intensive computational requirements. In such cases, the ensemble optimal interpolation scheme (EnOI), which works on a single model run instead of ensemble evolution, may offer a sub‐optimal alternative. This study explores different approaches of dynamic covariance matrix generation from predefined state vector repositories for assimilating synthetic water level observations with the EnOI scheme into a distributed rainfall‐runoff‐inundation model. Repositories are first created by storing open loop state vectors from the simulation of past flood events. The vectors are later sampled during the assimilation step, based on their closeness to the model forecast (calculated using vector norm). Results suggest that the dynamic EnOI scheme is inferior to the EnKF, but can improve upon the deterministic simulation depending on the sampling approach and the repository used. Observations can also be used for sampling to increase the background spread when the system noise is large. A richer repository is required to reduce analysis degradation, but increases the computation cost. This can be resolved by using a sliced repository consisting of only the vectors with norm close to the model forecast. [ABSTRACT FROM AUTHOR]

Modern data collecting methods and computation tools have made it possible to monitor high‐dimensional processes. In this article, we investigate phase II monitoring of high‐dimensional processes when the available number of samples collected in phase I is limited in comparison to the number of variables. A new charting statistic for high‐dimensional multivariate processes based on the diagonal elements of the underlying covariance matrix is introduced and we propose a unified procedure for phases I and II by employing a self‐starting control chart. To remedy the effect of outliers, we adopt a robust procedure for parameter estimation in phase I and introduce the appropriate consistent estimators. The statistical performance of the proposed method is evaluated in phase II using the average run length (ARL) criterion in the absence and presence of outliers. Results show that the proposed control chart scheme effectively detects various kinds of shifts in the process mean vector. Finally, we illustrate the applicability of our proposed method via a manufacturing application. [ABSTRACT FROM AUTHOR]

Time series classification finds widespread applications in civil, industrial, and military fields, while the classification performance of time series models has been improving with the recent development of deep learning. However, the issues of feature extraction effectiveness, model complexity, and model design uncertainty constrain the further development of time series classification. To address the above issues, we propose a Lightweight Spatio-Temporal Decoupling Transformer framework based on Automated Machine Learning technique (AutoLDT). The framework proposes a novel lightweight Transformer with fuzzy position encoding, TS-separable linear self-attention mechanism, and convolutional feedforward network, which mine the temporal and spatial features, as well as the local and global relationship of time series. Fuzzy positional encoding integrates fuzzy ideas to enhance the generalization performance of model information mining. TS-separable linear self-attention mechanism and convolutional feedforward network achieve feature extraction in a lightweight way by decoupling temporal and spatial features of time series. Notably, we adopt the Covariance Matrix Adaptation Evolution Strategy and global adaptive pruning technique to realize automated network structure design, which further improves the model training efficiency and automation, and avoids the uncertainty problem of network design. Finally, we validate the effectiveness of the proposed framework on publicly available UCR and UEA time series datasets. The experimental results show that the proposed framework not only improves the model classification performance in a lightweight way but also dramatically improves the model training efficiency. [ABSTRACT FROM AUTHOR]

Satellite measurements are essential for understanding Earth's complex system, yet they often lack a reliable a‐priori covariance matrix. This study presents a new methodology to enhance the reliability of satellite measurements by deriving experimental covariance matrices from the original observation. We focus on the accelerometer measurements (1A dataset) from the GRACE (Gravity Recovery and Climate Experiment) C satellite. Using autocorrelation analysis, we create a block‐diagonal covariance matrix for the 1A dataset. We then apply a low‐pass Gaussian filter that integrates this covariance matrix into the least squares estimation, resulting in a refined 1B dataset that minimizes spikes and spurious accelerations while preserving measurement error. Our variance analysis uncovers disturbances linked to geomagnetic storms and the satellite's transitions through Earth's shadow and terminator, with fluctuations notably peaking during the equinoxes. Plain Language Summary: We developed an effective method to generate covariance matrices directly from the raw accelerometer measurements of Gravity Recovery and Climate Experiment (GRACE) C (10 Hz $Hz$ sampling rate) that can be applied to any satellite measurements structured as time or data series. The result of this method is a weighted accelerometer 1B dataset, free of spurious spikes typically present in accelerometer measurements, while preserving error information through the inclusion of measurement variances and correlations. Our approach excels at detecting errors and preserving important signals. Notably, the accelerometer variances depend on latitude and local time, increase during equinoxes and geomagnetic storms, and are highest in the radial direction. Key Points: A new post‐processed 1B accelerometer dataset has been developed for GRACE C satelliteThe dataset variances exhibit significant dependence on equinoxes, terminator crossings, latitude, local time, and β' angleThese variances show strong signals associated with geomagnetic storms [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Time‐varying group formation tracking control problems for multi‐agent systems are investigated based on distributed multi‐sensor multi‐target filtering with intermittent observations. First, in order to estimate the states of multiple targets under the phenomenon of intermittent observations accurately, a distributed multi‐sensor multi‐target filtering algorithm is proposed based on cubature Kalman filtering. Second, a time‐varying group formation tracking protocol is designed for multi‐agent systems by using the state estimations obtained from the filtering algorithm and the neighboring interaction. The protocol enables multi‐agent systems to form time‐varying subformations and track multiple targets in the same subgroups, respectively. Third, the boundedness of the error covariance matrices is proved under the condition that the observation probability is higher than the minimum threshold. Then the estimation errors of the filtering algorithm are proved to be stochastically bounded by introducing a stochastic process. Furthermore, the boundedness of the group formation tracking errors is proved. Finally, a numerical example is used to verify the performance of the proposed algorithm and protocol. [ABSTRACT FROM AUTHOR]

Following White's approach of robust multiple linear regression [White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 1980;48(4):817–838], we give asymptotic confidence intervals for the multiple correlation coefficient $ R^2 $ R2 under minimal moment conditions. We also give the asymptotic joint distribution of the empirical estimators of the individual $ R^2 $ R2's. Through different sets of simulations, we show that the procedure is indeed robust (contrary to the procedure involving the near exact distribution of the empirical estimator of $ R^2 $ R2 is the multivariate Gaussian case) and can be also applied to count linear regression. Several extensions are also discussed, as well as an application to robust screening. [ABSTRACT FROM AUTHOR]

This work presents an innovative approach for tuning the Kalman filter in INS/GNSS integration, combining states from the inertial navigation system (INS) and data from the Global Navigation Satellite System (GNSS) to enhance navigation accuracy. The INS uses measurements from accelerometers and gyroscopes, which are subject to uncertainties in scale factor, misalignment, non-orthogonality, and bias, as well as temporal, thermal, and vibration variations. The GNSS receiver faces challenges such as multipath, temporary signal loss, and susceptibility to high-frequency noise. The novel approach for Kalman filter tuning involves previously performing Monte Carlo simulations using ideal data from a predetermined trajectory, applying the inertial sensor error model. For the indirect filter, errors from inertial sensors are used, while, for the direct filter, navigation errors in position, velocity, and attitude are also considered to obtain the process noise covariance matrix Q. This methodology is tested and validated with real data from Castro Leite Consultoria's commercial platforms, PINA-F and PINA-M. The results demonstrate the efficiency and consistency of the estimation technique, highlighting its applicability in real scenarios. [ABSTRACT FROM AUTHOR]

To improve the current indoor positioning algorithms, which have insufficient positioning accuracy, an ultra-wideband (UWB) positioning algorithm based on the Levenberg–Marquardt algorithm with improved Kalman filtering is proposed. An alternative double-sided two-way ranging (ADS-TWR) algorithm is used to obtain the distance from the UWB tag to each base station and calculate the initial position of the tag by the least squares method. The Levenberg–Marquardt algorithm is used to correct the covariance matrix of the Kalman filter, and the improved Kalman filtering algorithm is used to filter the initial position to obtain the final position of the tag. The feasibility and effectiveness of the algorithm are verified by MATLAB simulation. Finally, the UWB positioning system is constructed, and the improved Kalman filter algorithm is experimentally verified in LOS and NLOS environments. The average X-axis and the Y-axis positioning errors in the LOS environment are 6.9 mm and 5.4 mm, respectively, with a root mean square error of 10.8 mm. The average positioning errors for the X-axis and Y-axis in the NLOS environment are 20.8 mm and 18.0 mm, respectively, while the root mean square error is 28.9 mm. The experimental results show that the improved algorithm has high accuracy and good stability. At the same time, it can effectively improve the convergence speed of the Kalman filter. [ABSTRACT FROM AUTHOR]

A reduced-dimension robust Capon beamforming method using Krylov subspace techniques (RDRCB) is a diagonal loading algorithm with low complexity, fast convergence and strong anti-interference ability. The diagonal loading level of RDRCB is known to become invalid if the initial value of the Newton iteration method is incorrect and the Hessel matrix is non-positive definite. To improve the robustness of RDRCB, an improved RDRCB (IRDRCB) was proposed in this study. We analyzed the variation in the loading factor with the eigenvalues of the reduced-dimensional covariance matrix and derived the upper and lower boundaries of the diagonal loading level; the diagonal loading level of the IRDRCB was kept within the bounds mentioned above. The computer simulation results show that the IRDRCB can effectively solve the problems of a sharp decline in the signal-to-noise ratio gain and an invalid diagonal loading level. The experimental results demonstrate that the interference noise of the IRDRCB is 3~5 dB higher than that of conventional adaptive beamforming, and the computational complexity is reduced by 15% to 20% compared with that of the RCB method. [ABSTRACT FROM AUTHOR]

Difficulties may arise when analyzing longitudinal data using mixed-effects models if nonparametric functions are present in the linear predictor component. This study extends semiparametric mixed-effects modeling in cases when the response variable does not always follow a normal distribution and the nonparametric component is structured as an additive model. A novel approach is proposed to identify significant linear and non-linear components using a double-penalized generalized estimating equation with two penalty terms. Furthermore, the iterative approach intends to enhance the efficiency of estimating regression coefficients by incorporating the calculation of the working covariance matrix. The oracle properties of the resulting estimators are established under certain regularity conditions, where the dimensions of both the parametric and nonparametric components increase as the sample size grows. We perform numerical studies to demonstrate the efficacy of our proposal. [ABSTRACT FROM AUTHOR]

Multivariate control charts are practical tools that simultaneously monitor several correlated quality characteristics in a process. Monitoring high‐dimensional data structures is challenging because, in most cases, the process sample size for monitoring parameters is greater than the number of process characteristics. Many researchers have used the multivariate Hotelling's T2 chart to monitor high‐dimensional data using the maximum‐likelihood methods (MLM) to estimate the covariance matrices. However, the multivariate Hotelling's T2chart based on MLM suffers from low statistical performance. In this paper, we proposed a multivariate Hotelling's T2 chart based on the minimum vector variance (MVV) and some regularized methods for monitoring high‐dimensional data structures. The performance of the proposed chart is evaluated in terms of the average run length (ARL). The results reveal the superiority of the proposed MVV Hotelling's T2 chart over the existing Hotelling's T2 charts for high‐dimensional correlated processes. [ABSTRACT FROM AUTHOR]