Bias Estimation Method Research Articles

Dynamic Cube Attacks against Grain-128AEAD

In this paper, we revisit the division property based dynamic cube attack on the full Grain-128 presented by Hao et al. at FSE 2020 and demonstrate that their attack on the full Grain-128 is invalid, that is, no key information could be successfully recovered. The theoretical framework for the dynamic cube attack provided by Hao et al. is correct, but the technique for building the MILP model in the dynamic cube attack has flaws. Besides, strong evidence indicates that their bias estimation method is not applicable to Grain-128AEAD and Grain-128. Accordingly, we introduce the three-subset division property without unknown subset (3SDP/u) into dynamic cube attacks and present a correct MILP modeling technique. In addition, we propose a heuristic technique called Polynomial Approximation with regard to Bias (PAB) to evaluate the bias in superpolies in the dynamic cube attack, which can provide a more accurate bias evaluation for high-dimension cubes. As a result, we implemented the dynamic cube attack based on 3SDP/u on 190-round Grain-128AEAD, and we could recover 3 key bits with a complexity 2103.44 and the success probability was evaluated to be 99.68%. For Grain-128, some zero-sum distinguishers of cube size 80 are given for the first time.

New ridge parameter estimators for the quasi-Poisson ridge regression model

The quasi-Poisson regression model is used for count data and is preferred over the Poisson regression model in the case of over-dispersed count data. The quasi-likelihood estimator is used to estimate the regression coefficients of the quasi-Poisson regression model. The quasi-likelihood estimator gives sub-optimal estimates if regressors are highly correlated—multicollinearity issue. Biased estimation methods are often used to overcome the multicollinearity issue in the regression model. In this study, we explore the ridge estimator for the quasi-Poisson regression model to mitigate the multicollinearity issue. Furthermore, we propose various ridge parameter estimators for this model. We derive the theoretical properties of the ridge estimator and compare its performance with the quasi-likelihood estimator in terms of matrix and scalar mean squared error. We further compared the proposed estimator numerically through a Monte Carlo simulation study and a real-life application. We found that both the simulation and application results show the superiority of the ridge estimator, particularly with the best ridge parameter estimator, over the quasi-likelihood estimator in the presence of multicollinearity issue.

A Novel Method for Gyro Bias Estimation of On-Board Inertial Navigation System Prior to Launch of Autonomous Drone

A Novel Method for Gyro Bias Estimation of On-Board Inertial Navigation System Prior to Launch of Autonomous Drone

High-resolution regional gravity field modeling in data-challenging regions for the realization of geopotential-based height systems

Modern height systems are based on the combination of satellite positioning and gravity field models of high resolution. However, in many regions, especially developing or newly industrializing countries, there is no (reliable) regional gravity model at all, due to challenges such as limited data availability, unknown/low data quality, and missing metadata. This paper addresses this issue in a case study of Colombia, where eight decades of historical terrestrial and airborne gravity measurements are available but widely contain systematic errors, outliers, and biases. Correspondingly, processing strategies and structures are proposed and applied to validate and improve the quality of old gravity datasets. A novel method is developed based on spherical radial basis functions (SRBFs) for estimating biases, which are found in different airborne surveys with values exceeding 40 mGal. The validity of this bias estimation method is demonstrated both by a simulation test and by the evaluation of the airborne data in comparison to the SATOP (SAtellite-TOPography) model, which merges the satellite-only global gravity model GOCO06s with the Earth2014 topography model. The terrestrial and airborne data are then combined with a global gravity model (GGM), ultra-high-resolution topography models, as well as altimetry-derived gravity anomalies from DTU21GRA for the offshore areas. The results are presented in terms of height anomalies (QGeoidCOL2023), and they are thoroughly validated using GPS/leveling data both in the absolute and relative manner. The standard deviation in comparison to the GPS/leveling data after applying a correction surface to account for the datum inconsistencies amounts to 15.76 cm, which is 27% smaller compared to the mean standard deviation value given by five recent high-resolution GGMs, and 36% smaller than the one delivered by the latest South American quasi-geoid model QGEOID2021. The relative validation results show that QGeoidCOL2023 performs better, i.e., delivers lower RMS errors than the GGMs and QGEOID2021 in all the baseline length groups. These results indicate the validity and benefits of the developed methods and procedures, which can be used for other data-challenging areas to facilitate the realization of geopotential-based height systems.Graphical

BENN: Bias Estimation Using a Deep Neural Network.

Utilizing existing methods for bias detection in machine learning (ML) models is challenging since each method: 1) explores a different ethical aspect of bias, which may result in contradictory output among the different methods; 2) provides output in a different range/scale and therefore cannot be compared with other methods; and 3) requires different input, thereby requiring a human expert's involvement to adjust each method according to the model examined. In this article, we present BENN, a novel bias estimation method that uses a pretrained unsupervised deep neural network. Given an ML model and data samples, BENN provides a bias estimation for every feature based on the examined model's predictions. We evaluated BENN using three benchmark datasets, one proprietary churn prediction model used by a European telecommunications company, and a synthetic dataset that includes both a biased feature and a fair one. BENN's results were compared with an ensemble of 21 existing bias estimation methods. The evaluation results show that BENN provides bias estimations that are aligned with those of the ensemble while offering significant advantages, including the fact that it is a generic approach (i.e., can be applied to any ML model) and does not require a domain expert.

Study of a dual RIS-assisted beam assignment algorithm in a high-speed moving scenario

In order to improve the frequent Doppler bias in the high-speed mobile scenario, improve the communication quality on HSR (High-Speed Railway). An algorithm can improve communication performance through RIS (intelligent metasurface) phase optimization. The optimization process is modeled as a reinforcement learning task, where the agent in the channel learns to select the optimal RIS phase by interacting with the environment to reduce the effect of Doppler shift.The model covers characteristic parameters such as signal propagation path and frequency related to RIS, and the agent influences the beam direction by adjusting the phase, and learns the optimal phase configuration in the high-speed moving scene.The RIS phase optimization method implemented based on the deep reinforcement learning algorithm has high convergence and has significant advantages in improving the Doppler frequency shift problem. Compared with the traditional frequency bias estimation method, this method can significantly improve the transmission performance. The effectiveness of the proposed algorithm is verified by performing large-scale training and evaluation in the simulation environment.

Kibria–Lukman estimator for the zero inflated negative binomial regression model: theory, simulation and applications

The zero inflated negative binomial model is an appropriate choice to model count response variables with excessive zeros and over-dispersion simultaneously. This article addresses the parameter estimation for the zero-inflated negative binomial model when there are many predictors and the problems of multicollinearity are present. Since, under multicollinearity, the widely used maximum likelihood estimator becomes unstable, this article proposes an alternative estimator, called Kibria–Lukman estimator for the zero inflated negative binomial model and provided some new biasing parameters. Then compared the performance of Kibria–Lukman estimator with some of the traditional biased estimators, that is, ridge and Liu estimators. The superiority of the proposed estimator is systematically scrutinized both theoretically and numerically. For numerical assessment, we conduct a Monte Carlo simulation study under different controlled factors. A numerical example is also applied to appraise the performance of proposed estimator. Based on the simulation and example results, we observed that the performance of the proposed estimator under different biasing parameters was better than that of the maximum likelihood estimator and other involved biased estimation methods when there exists a high but imperfect multicollinearity.

Cauchy non-convex sparse feature selection method for the high-dimensional small-sample problem in motor imagery EEG decoding.

The time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information. In this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers. We conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively. The experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time.

A Simple Approach to Determine Single-Receiver Differential Code Bias Using Precise Point Positioning.

In this study, a precise single-receiver differential code bias (DCB) estimation method using the precise point positioning (PPP) model is presented. The first step is to extract the high-precision ionospheric observations, including DCBs, based on the PPP model. Then, the satellite DCBs are corrected using International GNSS Service (IGS) products. Lastly, the algorithm for the minimization of the standard deviation of vertical total electron content (VTECmstd) is employed to determine the value of receiver DCB. To check the method, GNSS data from more than 200 IGS stations around the globe on four days with various geomagnetic and solar activity circumstances are processed. The receiver DCBs are compared to those obtained using previous carried-to-code level (CCL) models. The experimental results show that, compared to the CCL model, the values of VTECmstd for most stations are significantly reduced, the mean number of stations with negative ionospheric measurements is reduced by 40% after correcting the receiver DCBs, and the mean error of estimated receiver DCBs is reduced by approximately 0.6 ns using the PPP model. These results suggest that this method can provide more high-precision receiver DCB estimation.

High-rate GNSS multi-frequency uncombined PPP-AR for dynamic deformation monitoring

Precise point positioning (PPP) is an important alternative to real-time kinematic (RTK) positioning for deformation monitoring. Global Navigation Satellite System (GNSS) multi-frequency and multi-system integration can benefit PPP dynamic deformation monitoring due to increased measurement redundancy and facilitated ambiguity resolution (AR). This study investigates the performance of GNSS multi-frequency uncombined (UC) PPP-AR with GPS triple-frequency, BDS-3 triple-frequency, and Galileo five-frequency signals for monitoring high-rate vibrations based on 20-Hz observations from a shaking table. The GNSS multi-frequency UC fractional cycle bias (FCB) estimation method and PPP-AR method are developed. The results of vibration tests indicate that the displacement monitoring accuracy of GPS/BDS-3/Galileo multi-frequency PPP-AR solution is 5.3–8.4 mm. BDS-3 dual-frequency PPP-AR, BDS-3 triple-frequency PPP-AR, and GPS/BDS-3/Galileo multi-frequency PPP-AR solutions improve the accuracy by 8%, 17%, and 46% over the BDS-3 dual-frequency float solutions, respectively. GNSS multi-frequency UC PPP-AR can accurately identify the vibration frequency and amplitude, but the PPP-derived displacement is nosier than that of RTK.

Application of Initial Bias Estimation Method for Inertial Navigation System (INS)/Doppler Velocity Log (DVL) and INS/DVL/Gyrocompass Using Micro-Electro-Mechanical System Sensors.

This article proposes a novel initial bias estimation method using a trajectory generator (TG). The accuracy of attitude and position estimation in navigation after using the inertial navigation system/Doppler velocity log (INS/DVL) and INS/DVL/gyrocompass (IDG) for 1 h were evaluated, and the results were compared to those obtained using the conventional Kalman filter (KF) estimation method. The probability of a horizontal position error < 1852 m (1 nautical mile) with a bias interval > 400 s was 100% and 9% for the TG and KF, respectively. In addition, the IDG average horizontal position errors over 1 h were 493 m and 507 m for the TG and KF, respectively. Moreover, the amount of variation was 2 m and 27 m for the TG and the KF, respectively. Thus, the proposed method is effective for initial bias estimation of INS/DVL and IDG using micro-electro-mechanical system sensors on a constantly moving vessel.

SABER: A Model-Agnostic Postprocessor for Bias Correcting Discharge from Large Hydrologic Models

Hydrologic modeling is trending toward larger spatial and temporal domains, higher resolutions, and less extensive local calibration and validation. Thorough calibration and validation are difficult because the quantity of observations needed for such scales do not exist or is inaccessible to modelers. We present the Stream Analysis for Bias Estimation and Reduction (SABER) method for bias correction targeting large models. SABER is intended for model consumers to apply to a subset of a larger domain at gauged and ungauged locations and address issues with data size and availability. SABER extends frequency-matching postprocessing techniques using flow duration curves (FDC) at gauged subbasins to be applied at ungauged subbasins using clustering and spatial analysis. SABER uses a “scalar” FDC (SFDC), a ratio of simulated to observed FDC, to characterize biases spatially, temporally, and for varying exceedance probabilities to make corrections at ungauged subbasins. Biased flows at ungauged locations are corrected with the scalar values from the SFDC. Corrected flows are refined to fit a Gumbel Type 1 distribution. We present the theory, procedure, and validation study in Colombia. SABER reduces biases and improves composite metrics, including Nash Sutcliffe and Kling Gupta Efficiency. Recommendations for future work and a discussion of limitations are provided.

The Solution of Multicollinearity Problem via Biased Regression Analysis in Southern Anatolian Red Cattle

The aim of this study is to investigate the effectiveness of biased estimation methods, principal component regression (PC) and ridge regression (RR) methods, according to unbiased the least squares (LS) method, against the multiple linearity problem (multicollinearity) encountered in regression methods. For this purpose to fit a model on account to predict body weight from some body measurements of 32 South Anatolian Red Kilis (SAR) cattle of different ages. R2, RMSE, MSE, and CV were used as the goodness of fit criteria for the performance of the models. According to these criteria respectively, 0.9970, 0.0224, 0.0005, 0.0099 for LS; 0.9970, 0.0224, 0.0005, 0.0099 for PC; and 0.9876, 0.0455, 0.0021, 0.0201 of k=0.02 for RR gave the best fit values. According to these results, LR and PC showed the best fit. But RR and PC techniques from biased prediction techniques provided more consistent, valid, stable, and theoretical expectations than LS technique, since LR did not provide the necessary assumptions.

Principal component ridge type estimator for the inverse Gaussian regression model

The inverse Gaussian regression model (IGRM) is applied when the response variable is continuous, positively skewed and well fitted to the inverse Gaussian distribution. In the presence of multicollinearity, the maximum likelihood estimation (MLE) is not a right choice. Therefore, we proposed a new estimator called the principal component ridge estimator for the IGRM which combines the principal component estimator and the ridge estimator. We also consider a two-parameter estimator (TPE) and other biased estimators to see a clear image of our proposed estimator. A Monte Carlo simulation study is also presented to examine the performance of the proposed estimators. Furthermore, we analysed a dataset to assess the superiority of the proposed estimator. Based on the simulation and application results, it is evident that the proposed estimator dominates the classical MLE, and other considered biased estimation methods.

A self-alignment method for gravitational apparent acceleration identification and accelerometer bias estimation based on repeated navigation solution.

Estimating inertial measurement unit error without any external reference information has always been a difficult problem. The method based on the reconstruction of the gravitational apparent motion in the inertial frame can estimate the accelerometer bias, but a long identification time is needed to get high alignment accuracy. In order to reduce the convergence time and improve the accuracy of estimation, a fast estimation method based on repetitive navigation under the excitation of swing motion is proposed. Two data processing methods, the forward-forward loop and the backward-forward loop, are studied and compared in this paper. In addition, the influence of data length on the experiment result was analyzed. The simulation and experimental results show that the forward-forward loop calculation cannot solve the contradiction between speed and accuracy, while the backward-forward loop calculation can effectively estimate the accelerometer bias and realize self-alignment, with a short alignment time and high alignment accuracy.

Real-time GPS receiver bias estimation

In this paper, we present the new method for real-time GPS receiver bias estimation by using Lagrange interpolation, which is also compared to the two current methods, polynomial and minimization of standard deviation. The estimated method is proposed to reduce the complexity and time of the GPS receiver bias estimation. Lagrange interpolation is the method to find the derivatives and integrals of discrete functions in GPS receiver bias data. The test site is located on Chumphon station, Thailand. The test period of data method is during the year 2004–2019. In the quiet and disturbed days, the polynomial method gives the highest value of the GPS receiver bias at −5.75 ns and −4.25 ns, respectively, but the Lagrange interpolation shows the lowest value of GPS receiver bias at −6.85 ns and −5.25 ns, in order. The results and comparisons among the polynomial GPS receiver bias method, the minimization of standard deviation of GPS receiver bias method, and Lagrange interpolation method show that the calculated time for Lagrange interpolation is shorter compared to other methods and it can be given more time points for finding GPS receiver biases than others.

A novel phase bias estimation method for multichannel HRWS SAR system in azimuth based on RDS analysis

ABSTRACT Multichannel synthetic aperture radar (SAR) systems in azimuth can realize high-resolution and wide-swath (HRWS) imaging, which has attracted intensive research interests. The azimuth ambiguities caused by non-uniform sampling can be suppressed by digital beamforming technique. However, the presence of inevitable channel errors in the multichannel SAR system deteriorates the performance of ambiguity suppression. Based on the analysis of the characteristics of the multichannel SAR echoes, it was found that phase bias among channels can cause jumping in the reconstructed Doppler spectrum (RDS). This letter presents a novel data-based phase bias estimation method for HRWS SAR system. With the assumption that the splice of echo energy inside the Doppler bandwidth is continuous, there is no jumping in the RDS. The estimation of phase biases in the multichannel HRWS SAR system can be translated into the optimization solution issue. Simulation experiments and real data processing results validated the effectiveness of the proposed method.

A new biased estimation method in tobit regression: theory and application

In this study, the effects of multicollinearity on the maximum likelihood estimator are analyzed in the tobit regression model. It is known that the near-linear dependencies in the design matrix affect the maximum likelihood estimation negatively, namely, the standard errors become so large so that the estimations are said to be inconsistent. Therefore, a new biased estimator being a generalization of the well-known Liu estimator is introduced as an alternative to the maximum likelihood estimator. Mean squared error properties of the estimators are investigated theoretically. In order to evaluate the performances of the estimators, a Monte Carlo simulation study is designed and simulated mean squared error is used as a performance criterion. Finally, the benefits of the new estimator is illustrated via real data applications.

The Performance Of Some Biased Estimators With Different Biased Parameter In Linear Regression Model

To circumvent the problem of multicollinearity, biased estimation method has been suggested to improve the precision of estimators. In this paper, we study types of biased estimators that can help to reduce the effect of multicollinearity on estimation. A simulation study is carried out to study the relative effectiveness of certain types of biased estimators in comparison to some proposed estimated ridge parameter (k) that have been shown in the literature . Moreover, a real data set has been considered to support the simulation results based on the estimated mean square error criterion.

On the James-Stein estimator for the poisson regression model

The Poisson regression model (PRM) aims to model a counting variable y, which is usually estimated by using maximum likelihood estimation (MLE) method. The performance of MLE is not satisfactory in the presence of multicollinearity. Therefore, we propose a Poisson James-Stein estimator (PJSE) as a solution to the problems of inflated variance and standard error of MLE with multicollinear explanatory variables. For assessing the superiority of proposed estimator, we present a theoretical comparison based on the matrix mean squared error (MMSE) and scalar mean squared error (MSE) criterions. A Monte Carlo simulation study is performed under different conditions in order to investigate the performance of the proposed estimator where MSE is considered as an evaluation criterion. In addition, an aircraft damage data is also considered to assess the superiority of proposed estimator. Based on the results of simulation and real data application, it is shown that the PJSE outperforms the classical MLE and other biased estimation methods in a sense of minimum MSE criterion.