Robust Weighted Least Squares Research Articles

Empirical analysis of the economic complexity boost on the impact of energy transition on economic growth: A panel data study of 124 countries

This study investigates the moderating role of economic complexity in the relationship between energy transition and economic growth, utilizing panel data from 124 countries spanning 2000–2020 and employing the robust weighted least squares (RWLS) estimation method. Our findings indicate an overall negative impact of energy transition on economic growth. Notably, economic complexity emerges as a positive moderator, alleviating the adverse effects of energy transition. This novel insight challenges existing literature, contributing to a more comprehensive understanding of the interplay between renewable energy adoption, economic growth, and economic complexity. Policymakers should acknowledge the intricate dynamics involved in transitioning to renewable energy sources and consider the role of economic complexity in shaping outcomes.

Passive IR-UWB Localization System for UAV-Based Electric Facility Inspection During GPS Outage

A practical localization system is proposed for a substation inspection unmanned aerial vehicle (UAV) performing its mission under global positioning system (GPS) signal blockage. The sensor hardware consists of a single transmitter and a cruciform receiver array, which produces the range difference (RD) information used for UAV positioning. The use of impulse-radio ultra wide-band (IR-UWB) devices secures the robustness against RF interference often caused by an exogenous electromagnetic field. With this hardware configuration, the UAV localization can be formulated as the state estimation problem for an uncertain linear state space model and be solved by using the computationally efficient robust weighted least squares (RWLS) estimator. Since the standard RWLS estimator could be sensitive to the imperfectness of prior knowledge on the noise statistic, a geometric constraint expressed in terms of a UAV position is exploited to secure the reliable localization performance in practice. Through experimental results, it is verified that the proposed solution provides reliable UAV positioning performance despite using the imperfect measurement noise statistics for the localization system.

Does Religious Proximity Affect FDI Location Choice? An Empirical Analysis of Outward FDI from Morocco to 54 Host Countries


 
 
 The present paper investigates the effect of religious distance on the choice of location of Moroccan Multinational Enterprises (MNEs) when investing abroad. The main research hypothesis considers the religion as an element of the psychic distance between the home country and host countries that can affect the FDI location decision. The results of the Robust Weighted Least Squares (RWLS) estimation method using panel data of outward FDI flows from Morocco to 54 host countries from 2007 to 2020 show that the more the local Muslim population is important in the host country, the more it receives FDI from Morocco. The results of the study are highly relevant to policymakers as they prove that the religion is important for inward and outward foreign direct investment. On the one hand, policy makers in charge of FDI attractiveness in the host country have to adopt religious strategies accompanied by public–private partnerships to integrate main foreign religions to facilitate the integration of MNEs and reduce their transaction costs. On the other hand, policy makers in charge of promoting outward FDI from the home country have to establish a liaison office in host countries to support the MNEs and facilitate their internationalization process.
 
 

Forecasting the real prices of crude oil: A robust weighted least squares approach

The ordinary least squares (OLS) estimator inflates the estimation variance of parameters in the presence of outliers, thus providing poor out-of-sample forecasts. We forecast the real price of crude oil using a robust weighted least squares (RWLS) approach that has the potential to improve forecasting performance by dealing with outliers. This approach down-weights extreme observations using a class of kernel functions. Our results show that the RWLS improves forecasting accuracy relative to the standard OLS approach. Furthermore, the predictability of oil prices revealed by the RWLS is statistically significant for longer horizons. The predictive ability of the RWLS comes from more efficient estimates and the trade-off between forecast bias and variance. Sensitivity analyses indicate that the findings are robust to alternative validation samples, kernel functions, and combination strategies.

Robust RSS-Based Source Localization With Unknown Model Parameters in Mixed LOS/NLOS Environments

In this paper, we address the received-signal-strength (RSS) based source localization problem in mixed line-of-sight/non-line-of-sight (LOS/NLOS) environments, where the model parameters are unknown. To accommodate the additional path losses incurred by NLOS signal propagations, we introduce a random variable to the outdoor RSS measurement model to represent the additional path loss. Based on this modified model, we propose a robust localization method for the case of unknown model parameters. Specifically, we introduce a balancing parameter and express the additional path loss term as the sum of the balancing parameter and an error term, and then formulate a robust weighted least squares (RWLS) problem to jointly estimate the source location, the unknown model parameters, and the balancing parameter. The RWLS problem is solved in an iterative manner, where the S-Lemma and the semidefinite relaxation technique are used. Both simulated and real experimental data verify that the proposed method works well in both dense and sparse NLOS environments.

Cooperative Multiple Target Nodes Localization Using TOA in Mixed LOS/NLOS Environments

In this paper, the time-of-arrival based cooperative localization problem under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions is addressed. Under the conditions of unknown statistics of NLOS errors and unknown path status, we formulate a non-convex robust weighted least squares (RWLS) problem to locate the multiple stationary target nodes. In this problem, we introduce some “balancing parameters” to alleviate the possible improper upper bounds problem of the existing robust localization methods, and jointly estimate the target node positions and the balancing parameters. With the aid of the semidefinite relaxation technique, the RWLS problem is transformed into a convex mixed semidefinite and second order cone programming problem, which can be solved efficiently. The idea is then extended to the moving target nodes localization problem, where the velocity is assumed to be fixed within a short time period. Both simulated and real experimental data are used to verify the performance of the proposed method. The results show that the proposed method works well for both the sparse and dense NLOS environments, and also confirm the superior performance of the proposed method over the existing methods.

Efficient Weighted Least-Squares Frequency Synchronization Scheme for Digital Terrestrial Broadcasting System

The integrated services digital broadcasting-terrestrial (ISDB-T) system provides the interesting advantages of hierarchical transmission and partial reception using band segmented transmission orthogonal frequency division multiplexing scheme. In ISDB-T, the hierarchical configuration varies depending on whether a segment is modulated coherently or differentially. Thus, it is important to accomplish frequency synchronization regardless of segment type. For this purpose, an effective weighted least-squares (WLS) carrier frequency synchronization method is presented using transmission and multiplexing configuration control (TMCC) and auxiliary channel (AC) in a blind manner. Thus, the proposed estimation scheme is performed without resorting to a priori knowledge on segment type that can be known after correct TMCC decoding. To eliminate bias caused by using non-uniformly distributed TMCC and AC subcarriers, an appropriate subset is selected to form the basis of robust WLS estimation. Extensive simulations are conducted to verify the efficiency of the proposed WLS estimation method and compare its performance with that of the conventional WLS estimation scheme.

Improved Robust TOA-Based Localization via NLOS Balancing Parameter Estimation

In this paper, the time-of-arrival-based localization problem under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions is addressed. Previous studies show that existing robust methods perform well in dense NLOS environments, but generally perform badly in sparse NLOS environments. To alleviate this problem, we introduce a “balancing parameter” related to the NLOS errors and formulate a new robust weighted least squares (RWLS) problem with the source position and the NLOS balancing parameter as the estimation variables. The proposed method does not require the statistics of NLOS errors and the path status. By leveraging the S-Lemma, the RWLS problem is transformed into a non-convex optimization problem, which is then relaxed into a convex semidefinite program. Simulation results show that the proposed method works well for both the sparse and dense NLOS environments.

Robust Weighted Least Squares Method for TOA-Based Localization Under Mixed LOS/NLOS Conditions

In this letter, the time-of-arrival-based localization problem under mixed line-of-sight/non-line-of-sight conditions is addressed. Based on the a priori information of known path status, we propose a new robust weighted least squares (RWLS) method to improve the performance of the existing RWLS method, and the weights in the new RWLS problem are derived and explicitly given. The proposed RWLS problem is approximately solved by employing the second-order cone relaxation technique. Furthermore, when the path status is incorrectly identified, we propose a procedure of correcting the incorrect path status to make the RWLS method robust against the path status identification errors. The performance of the new RWLS method is verified by computer simulations.

Robust centralized and weighted measurement fusion Kalman estimators for uncertain multisensor systems with linearly correlated white noises

This paper addresses the design of robust centralized fusion (CF) and weighted measurement fusion (WMF) Kalman estimators for a class of uncertain multisensor systems with linearly correlated white noises. The uncertainties of the systems include multiplicative noises, missing measurements, and uncertain noise variances. By introducing the fictitious noises, the considered system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case system with the conservative upper bounds of uncertain noise variances, the robust CF and WMF time-varying Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the Lyapunov equation approach, their robustness is proved in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Using the information filter, their equivalence is proved. Their accuracy relations are proved. The computational complexities of their algorithms are analyzed and compared. Compared with CF algorithm, the WMF algorithm can significantly reduce the computational burden when the number of sensors is larger. A robust weighted least squares (WLS) measurement fusion filter is also presented only based on the measurement equation, and it is proved that the robust accuracy of the robust CF or WMF Kalman filter is higher than that of robust WLS filter. The corresponding robust fused steady-state estimators are also presented, and the convergence in a realization between the time-varying and steady-state robust fused estimators is proved by the dynamic error system analysis (DESA) method. A simulation example shows the effectiveness and correctness of the proposed results.

Item Parameter Estimation for the MIRT Model

The accuracy of item parameter estimates in the multidimensional item response theory (MIRT) model context is one that has not been researched in great detail. This study examines the ability of two confirmatory factor analysis models specifically for dichotomous data to properly estimate item parameters using common formulae for converting factor loadings and thresholds to discrimination and difficulty indices. The two MIRT estimation methods included in this research, unweighted least squares (ULS) and robust weighted least squares (RWLS), and the unidimensional estimation approach used are accessible in the widely distributed software packages NOHARM, Mplus, and BILOGMG, respectively. These techniques have been assessed in terms of the overall accuracy, bias, and standard error of item parameter estimates under a variety of sample sizes, test lengths, intertrait correlations, pseudo-guessing, and latent trait distribution conditions. Results indicate that there exists a complex relationship between these manipulated factors and the estimation accuracy of these methods. Recommendations for practice in light of these results are provided.

An Empirical Evaluation of Alternative Methods of Estimation for Confirmatory Factor Analysis With Ordinal Data.

Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likert-type items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent process determines each observed variable. The extent to which violations of this assumption undermine CFA estimation is not well-known. In this article, the authors empirically study this issue using a computer simulation study. The results suggest that estimation of polychoric correlations is robust to modest violations of underlying normality. Further, WLS performed adequately only at the largest sample size but led to substantial estimation difficulties with smaller samples. Finally, robust WLS performed well across all conditions.