Estimation Error Research Articles

Extended Kalman filter-based robust roll angle estimation method for spinning vehicles

Attitude measurement is a basic technique for monitoring vehicle motion states and safety. The spin motion of a vehicle couples the attitude angles with each other, which has an impact on the navigation and control of the vehicle. Global navigation satellite system (GNSS) signals-based roll angle measurement methods are important for vehicle attitude measurement. Most of existing studies use continuous signal power, but the case of loop lock loss leading to discontinuous power reception has not been considered. A robust estimation method for the roll angle based on the Tukey weight function is proposed to improve the measurement accuracy in cases of discontinuous reception. The characteristics of the GNSS signals, the geometric relationship between the signal power and roll angle of the vehicle are discussed. By installing a GNSS receiver with a single patched antenna on a rotating platform with a controllable rolling speed, the proposed method was verified by experiments. The robust estimation errors of different weight functions are analyzed. According to the characteristics of the gross measurement errors, a robust estimation method of multisatellite power observations is proposed to obtain a high-precision and stable estimation of the vehicle roll angle. The results show that the proposed algorithm can improve the accuracy of roll angle estimation even with gross measurement errors. As a result of the experiments, the estimation errors of the algorithm are 6.57° at a confidence level of 68 % and 15.49°at the confidence level of 95 %. In contrast, they are 11.38° and 37.31° for the traditional LS method. Moreover, the estimation accuracy of the algorithm is not significantly correlated with the vehicle rotational speed. Therefore, the vehicle roll angle can be estimated with high accuracy under a variety of rotational speeds.

Sensorless robot collision detection based on fuzzy momentum observer

During physical human–robot interaction, unwanted collisions occur, which also threaten human safety. That is why different collision detection algorithms are proposed for safe collaboration between human and robots. The current detection methods are model-based methods without using additional sensors. However, the estimation of the collision force using a model-based method still has the problem of an unavoidable trade-off between the collision sensitivity and the reduction of the peaking value, and, in addition, the immunity to the model uncertainties. In this paper, an improved momentum observer using fuzzy system is proposed for robot collision detection. First, to avoid using the robot’s inverse inertia matrix, the generalized moment state equations based on the robot’s dynamics are deduced. Second, the extended linear momentum observer is built based on robot generalized momentum equations. Third, a fuzzy system is designed to select an appropriate bandwidth for the observer. In the design of this fuzzy system, the estimation error is considered as a fuzzy input, and the observer’s bandwidth is considered as its output. This fuzzy generalized momentum observer (FGMO) is compared with the existing generalized moment observers (GMOs) and has been proven its ability to overcome the mentioned above issues due to its ability to intelligently adjust the online filter parameters during the observation process. Finally, simulations are conducted with a 6-degree-of-freedom (6-DOF) industrial robot manipulator, and the obtained results illustrate the effectiveness of proposed approach in term of sensitivity and peaking value.

A Computation Model to Estimate Interaction Intensity through Non-Verbal Behavioral Cues: A Case Study of Intimate Couples under the Impact of Acute Alcohol Consumption

This work introduced a novel analysis method to estimate interaction intensity, i.e., the level of positivity/negativity of interaction, for intimate couples (married and heterosexual) under the impact of alcohol, which has great influences on behavioral health. Non-verbal behaviors are critical in interpersonal interactions. However, whether computer vision-detected non-verbal behaviors can effectively estimate the interaction intensity of intimate couples is still unexplored. In this work, we proposed novel measurements and investigated their feasibility to estimate interaction intensities through machine learning regression models. Analyses were conducted based on a conflict-resolution conversation video dataset of intimate couples before and after acute alcohol consumption. Results showed the estimation error was at the lowest in the no-alcohol state but significantly increased if the model trained using no-alcohol data was applied to after-alcohol data, indicating that alcohol altered the interaction data in the feature space. While training a model using rich after-alcohol data is ideal to address the performance decrease, data collection in such a risky state is challenging in real life. Thus, we proposed a new State-Induced Domain Adaptation (SIDA) framework, which allows for improving estimation performance using only a small after-alcohol training dataset, pointing to a future direction of addressing data scarcity issues.

Interpreting PPV and NPV of Diagnostic Tests with Uncertain Prevalence.

Medical decision-making is often uncertain. The positive predictive value (PPV) and negative predictive value (NPV) are conditional probabilities characterizing diagnostic tests and assessing diagnostic interventions in clinical medicine and epidemiology. The PPV is the probability that a patient has a specified disease, given a positive test result for that disease. The NPV is the probability that a patient does not have the disease, given a negative test result for that disease. Both values depend on disease incidence or prevalence, which may be highly uncertain for unfamiliar diseases, epidemics, etc. Probability distributions for this uncertainty are usually unavailable. We develop a non-probabilistic method for interpreting PPV and NPV with uncertain prevalence. Uncertainty in PPV and NPV is managed with the non-probabilistic concept of robustness in info-gap theory. Robustness of PPV or NPV estimates is the greatest uncertainty (in prevalence) at which the estimate's error is acceptable. Four properties are demonstrated. Zeroing: best estimates of PPV or NPV have no robustness to uncertain prevalence; best estimates are unreliable for interpreting diagnostic tests. Trade-off: robustness increases as error increases; this trade-off identifies robustly reliable error in PPV or NPV. Preference reversal: sometimes sub-optimal PPV or NPV estimates are more robust to uncertain incidence or prevalence than optimal estimates, motivating reversal of preference from the putative optimum to the sub-optimal estimate. Trade-off between specificity and robustness to uncertainty: the robustness increases as test-specificity decreases. These four properties underlie the interpretation of PPV and NPV. The PPV and NPV assess diagnostic tests, but are sensitive to lack of knowledge that generates non-probabilistic uncertain prevalence and must be supplemented with robustness analysis. When uncertainties abound, as with unfamiliar diseases, assessing robustness is critical to avoiding erroneous decisions.

System error analysis and target localization of distributed pulse radars

AbstractTarget localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars.

A Networked Meta-Population Epidemic Model with Population Flow and Its Application to the Prediction of the COVID-19 Pandemic.

This article addresses the crucial issues of how asymptomatic individuals and population movements influence the spread of epidemics. Specifically, a discrete-time networked Susceptible-Asymptomatic-Infected-Recovered (SAIR) model that integrates population flow is introduced to investigate the dynamics of epidemic transmission among individuals. In contrast to existing data-driven system identification approaches that identify the network structure or system parameters separately, a joint estimation framework is developed in this study. The joint framework incorporates historical measurements and enables the simultaneous estimation of transmission topology and epidemic factors. The use of the joint estimation scheme reduces the estimation error. The stability of equilibria and convergence behaviors of proposed dynamics are then analyzed. Furthermore, the sensitivity of the proposed model to population movements is evaluated in terms of the basic reproduction number. This article also rigorously investigates the effectiveness of non-pharmaceutical interventions via distributively controlling population flow in curbing virus transmission. It is found that the population flow control strategy reduces the number of infections during the epidemic.

Distributionally Robust Newsvendor Under Stochastic Dominance with a Feature-Based Application

Problem definition: In this paper, we study the newsvendor problem under some distributional ambiguity sets and explore their relations. Additionally, we explore the benefits of implementing this robust solution in the feature-based newsvendor problem. Methodology and results: We propose a new type of discrepancy-based ambiguity set, the JW ambiguity set, and analyze it within the framework of first-order stochastic dominance. We show that the distributionally robust optimization (DRO) problem with this ambiguity set admits a closed-form solution for the newsvendor loss. This result also implies that the newsvendor problem under the well-known infinity-Wasserstein ambiguity set and Lévy ball ambiguity set admit closed-form inventory levels as a by-product. In the application of feature-based newsvendor, we adopt general kernel methods to estimate the conditional demand distribution and apply our proposed DRO solutions to account for the estimation error. Managerial implications: The closed-form solutions enable an efficient computation of optimal inventory levels. In addition, we explore the property of optimal robust inventory levels with respect to the nonrobust version via concepts of perceived critical ratio and mean repulsion. The results of numerical experiments and the case study indicate that the proposed model outperforms other state-of-the-art approaches, particularly in environments where demand is influenced by covariates and difficult to estimate. Funding: X. Li is supported by the Singapore Ministry of Education [Tier 1 Grant 23-0619-P0001, 24-0500-A0001] and National Natural Science Foundation of China [Grant 72331004]. L. Zhang is partially supported by the National Natural Science Foundation of China [Grants 72171156 and 72231002] and the Hong Kong Research Grants Council [Grant 16212419]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0159 .

How to better estimate bunch number at field level?

In viticulture, early-season estimation of average number of grape bunches per vine is crucial for vineyard planning, investments and marketing (Laurent et al., 2021). Although sampling is widely carried out to estimate grape bunches per vine, there is no clearly established sampling protocol that can be used as a reference when performing these estimations. Each practitioner therefore has their own sampling protocol. In this work, the effect of differences between sampling protocols in terms of estimation errors were investigated to give some recommendations on the best possible sampling practices.

Target decomposition-led light-weighted offline training strategy-aided data-driven state-of-charge online estimation during constant current charging conditions over battery entire lifespan

This paper develops a novel target decomposition-led light-weighted offline training strategy-aided data-driven state-of-charge (SOC) online estimation method during constant current (CC) charging conditions over battery entire lifespan. Firstly, real SOC is conceptually decomposed into base SOC and SOC error. Subsequently, taking voltage and real SOC of initial CC charging cycle as input and output, machine learning algorithm is adopted to offline establish base SOC acquisition model without considering battery aging. Thirdly, the errors between real SOC and acquired base SOC are calculated, where the extremely similar distribution of SOC error against different battery degradation with two aging-dependent peaks can be clearly observed. Following this precious characteristic, a SOC error calculation model is further built only via several typical CC charging cycles with base SOC and battery capacity as input. Finally, the acquired base SOC is compensated by the computed SOC error for real SOC calculation. The validation results demonstrate that the proposed target decomposition-led method has overwhelming advantages in light-weighted offline training and accurate SOC online estimation during CC charging conditions, where maximum mean absolute error and maximum root mean squared error of SOC estimation results over the same type of batteries’ entire lifespan are only 0.98 % and 1.2 %, respectively.

Developing normative values and predictive models for the 6-minute walk test across diverse adolescent developmental stages.

The six-minute walking test (6MWT) is commonly used to measure functional capacity in field settings, primarily through the distance covered. This study aims to establish reference curves for the six-minute walking distance (6MWD) and peak heart rate (PHR) and develop a predictive equation for cardiovascular capacity in Tunisian children and adolescents. A total of 1501 participants (706 boys and 795 girls), aged 10-18years, were recruited from schools in Tunisia. The Lambda (L), Mu (M), and Sigma (S) methods (LMS method) were employed to develop smoothed percentile curves for 6MWD and PHR. Multivariate linear regression was utilized to formulate a prediction equation for 6MWD. Smoothed percentiles (3rd, 10th, 25th, 50th, 75th, 90th, and 97th) for 6MWD and PHR were presented with age. All variables showed a strong positive correlation (p<0.001) with a six-minute walking distance (r ranged from 0.227 to 0.558 for girls and from 0.309 to 0.610 for boys), except resting heart rate, which showed a strong negative correlation (girls: r=-0.136; boys: r=-0.201; p<0.001). Additionally, PHR showed a weak correlation (p>0.05). The prediction equations, based on age as the primary variable, were established for both genders. For boys: 6MWD=66.181+38.142×Age (years) (R2=0.372; Standard Error of Estimate (SEE)=122.13), and for girls: 6MWD=105.535+28.390×Age (years) (R2=0.312; SEE=103.66). The study provides normative values and predictive equations for 6MWD and PHR in Tunisian children and adolescents. These findings offer essential tools for identifying, monitoring, and interpreting cardiovascular functional deficits in clinical and research settings.

Mittag-Leffler function based security control for fractional-order complex network system subject to deception attacks via Observer-based AETS and its applications

The goal of this paper is to investigate the security control for uncertain fractional-order delayed complex network systems under deception attacks using the Mittag-Leffler function and observer-based adaptive event-triggered scheme (AETS) with the fractional commensurate order in q ∈ (0, 1). The adaptive event-triggering scheme is used during the data transmission process from the sensors to the observer, where the triggering threshold can be dynamically modified to reduce resource waste. We make a novel model for the estimation error system that takes into account both the effects of the adaptive event-triggered scheme and the effects of deception attacks. A sufficient condition is obtained to guarantee the stochastic mean-square stability of the augmented error system using the Mittag-Leffler (M-L) functions and the Lyapunov functional method and by using the singular value decomposition (SVD) and linear matrix inequality (LMI) techniques, the co-design problem of desired observer and controller gains is found, and it is shown that the solution ensures the stability of a closed-loop uncertain fractional-order complex networked system. At the end of this study, two numerical examples and diesel engine system model are given to show that the above findings are correct.

VE-LIOM: A Versatile and Efficient LiDAR-Inertial Odometry and Mapping System

LiDAR has emerged as one of the most pivotal sensors in the field of navigation, owing to its expansive measurement range, high resolution, and adeptness in capturing intricate scene details. This significance is particularly pronounced in challenging navigation scenarios where GNSS signals encounter interference, such as within urban canyons and indoor environments. However, the copious volume of point cloud data poses a challenge, rendering traditional iterative closest point (ICP) methods inadequate in meeting real-time odometry requirements. Consequently, many algorithms have turned to feature extraction approaches. Nonetheless, with the advent of diverse scanning mode LiDARs, there arises a necessity to devise unique methods tailored to these sensors to facilitate algorithm migration. To address this challenge, we propose a weighted point-to-plane matching strategy that focuses on local details without relying on feature extraction. This improved approach mitigates the impact of imperfect plane fitting on localization accuracy. Moreover, we present a classification optimization method based on the normal vectors of planes to further refine algorithmic efficiency. Finally, we devise a tightly coupled LiDAR-inertial odometry system founded upon optimization schemes. Notably, we pioneer the derivation of an online gravity estimation method from the perspective of S2 manifold optimization, effectively minimizing the influence of gravity estimation errors introduced during the initialization phase on localization accuracy. The efficacy of the proposed method was validated through experimentation employing various LiDAR sensors. The outcomes of indoor and outdoor experiments substantiate its capability to furnish real-time and precise localization and mapping results.

Machine learning allows robust classification of visceral fat in women with obesity using common laboratory metrics

The excessive accumulation and malfunctioning of visceral adipose tissue (VAT) is a major determinant of increased risk of obesity-related comorbidities. Thus, risk stratification of people living with obesity according to their amount of VAT is of clinical interest. Currently, the most common VAT measurement methods include mathematical formulae based on anthropometric dimensions, often biased by human measurement errors, bio-impedance, and image techniques such as X-ray absorptiometry (DXA) analysis, which requires specialized equipment. However, previous studies showed the possibility of classifying people living with obesity according to their VAT through blood chemical concentrations by applying machine learning techniques. In addition, most of the efforts were spent on men living with obesity while little was done for women. Therefore, this study aims to compare the performance of the multilinear regression model (MLR) in estimating VAT and six different supervised machine learning classifiers, including logistic regression (LR), support vector machine and decision tree-based models, to categorize 149 women living with obesity. For clustering, the study population was categorized into classes 0, 1, and 2 according to their VAT and the accuracy of each MLR and classification model was evaluated using DXA-data (DXAdata), blood chemical concentrations (BLDdata), and both DXAdata and BLDdata together (ALLdata). Estimation error and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {R}^{2}$$\\end{document} were computed for MLR, while receiver operating characteristic (ROC) and precision-recall curves (PR) area under the curve (AUC) were used to assess the performance of every classification model. MLR models showed a poor ability to estimate VAT with mean absolute error ≥401.40\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge 401.40$$\\end{document} and R2≤0.62\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {R}^{2} \\le 0.62$$\\end{document} in all the datasets. The highest accuracy was found for LR with values of 0.57, 0.63, and 0.53 for ALLdata, DXAdata, and BLDdata, respectively. The ROC AUC showed a poor ability of both ALLdata and DXAdata to distinguish class 1 from classes 0 and 2 (AUC = 0.31, 0.71, and 0.85, respectively) as also confirmed by PR (AUC = 0.24, 0.57, and 0.73, respectively). However, improved performances were obtained when applying LR model to BLDdata (ROC AUC ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge $$\\end{document} 0.61 and PR AUC ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge $$\\end{document} 0.42), especially for class 1. These results seem to suggest that, while a direct and reliable estimation of VAT was not possible in our cohort, blood sample-derived information can robustly classify women living with obesity by machine learning-based classifiers, a fact that could benefit the clinical practice, especially in those health centres where medical imaging devices are not available. Nonetheless, these promising findings should be further validated over a larger population.

Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning

In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure.

An Aging-Optimized State-of-Charge-Controlled Multi-Stage Constant Current (MCC) Fast Charging Algorithm for Commercial Li-Ion Battery Based on Three-Electrode Measurements

This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estimations and control precisely the charging algorithm. The results of the experiments showed that the Root Mean Square Error (RMSE) of SOC estimation is 1.08%, and the charging time from 0% to 80% SOC is reduced by 30%.

Nearly optimal fixed time sliding mode controller for leader–follower consensus problem with partially unknown nonlinear agents

This paper presents a conceptual framework for addressing the consensus problem in multi-agent systems with unknown nonlinear dynamics using reinforcement learning (RL) based nearly optimal sliding mode controller (SMC). The agents’ dynamics are assumed to have uncertainty and mismatched disturbance. An adaptive fixed-time estimator is introduced to estimate uncertain dynamics and disturbances for each agent at a certain time. The paper proposes two control strategies. In the first strategy, a controller is designed, incorporating adaptive SMC and an optimal controller. Adaption law in SMC estimates the bound of fixed time estimator error before convergence, ultimately achieving asymptotic convergence to the sliding surface and converting the agent’s dynamics to linear. This enables solving a linear consensus problem using an RL-based adaptive optimal controller through an on-policy critic–actor method. The second control strategy enhances the adaptive SMC into a fixed-time controller, reducing the time to reach the sliding surface regardless of initial conditions. Consequently, the convergence time of the consensus error to zero is diminished. This reduction in reaching time results in faster convergence of the consensus error to zero. The effectiveness of both strategies is validated through numerical experiments on two real system models, aligning with the theoretical proofs.

Carbohydrate Estimation Accuracy of Two Commercially Available Smartphone Applications vs Estimation by Individuals With Type 1 Diabetes: A Comparative Study.

Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce. The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D). Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate's deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire. Participants' mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both P > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods. SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated.

A novel adaptive unscented kalman filter algorithm for SOC estimation to reduce the sensitivity of attenuation coefficient

A new exponential adaptive strong tracking unscented Kalman filtering algorithm (ASTRUKF) is proposed to address the issues of slow tracking speed, untimely response of model parameter changes, and inaccurate noise statistical characteristics when estimating the State of Charge (SOC) of batteries at different temperatures, in order to reduce the sensitivity of attenuation coefficients. This algorithm improves upon the traditional method of calculating attenuation factor by incorporating the correlation between current and past residuals. It dynamically updates the Kalman gain and contribution rate using an exponential model, which increases the impact of current data. This algorithm also ensures the stability of the noise covariance matrix by stabilizing the attenuation factor within a fixed range. The contribution rate is utilized to estimate the statistical characteristics of detection noise, however it may exhibit bias. The ASTRUKF method exhibits a maximum estimation error of 10−8 at 0 °C and 10−12 at 15 and 25 °C. This value is well below the levels of the UKF and EKF algorithms. The ASTRUKF algorithm outperforms the UKF and EKF algorithms in terms of prediction accuracy at different temperatures. The ASTRUKF method is well-suited for estimating SOC and has excellent adaptability to temperature variations.

Supplemental Material for Minimizing Measurement Error in Treatment Outcome Estimates: A Meta-Analysis Comparing Estimates Between the Gambling Timeline Followback and Other Self-Report Assessments of Gambling Behavior

Supplemental Material for Minimizing Measurement Error in Treatment Outcome Estimates: A Meta-Analysis Comparing Estimates Between the Gambling Timeline Followback and Other Self-Report Assessments of Gambling Behavior

Minimizing measurement error in treatment outcome estimates: A meta-analysis comparing estimates between the gambling timeline followback and other self-report assessments of gambling behavior.

The aim of the current meta-analysis was to examine potential differences in posttreatment effect size estimates for gambling frequency (i.e., the number of days gambled) and gambling expenditure (i.e., the amount of money gambled) when using the gambling timeline followback (G-TLFB) versus other self-report assessments. Using an open-access meta-analysis database of studies on cognitive behavioral treatment for gambling disorder, 22 studies representing 2,824 participants were identified for inclusion. Hedges's g effect sizes representing posttreatment differences on gambling frequency and expenditure between cognitive behavioral treatment versus inactive and minimal treatment controls were calculated, and mixed-effect subgroup analyses examined the effect sizes for each outcome between studies using the G-TLFB versus other self-report assessments. Mixed-effect subgroup analyses indicated that the effect size for gambling frequency was significantly lower for studies using the G-TLFB (g = -0.15) than studies using other self-report assessments (g = -0.71). When examining whether the use of the G-TLFB was associated with the posttreatment effect size for gambling frequency in a random-effect metaregression model that controlled for study grant funding status, the use of the G-TLFB was not significantly associated with effect size. The effect size for gambling expenditure was not significantly different between studies using the G-TLFB (g = -0.22) versus studies using other self-report assessments (g = -0.38). The G-TLFB yields more conservative and precise effect size estimates of posttreatment gambling frequency, but not gambling expenditure, than other self-report assessments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).