Bias In Estimates Research Articles

Mitral Annular Disjunction and Its Progression during Childhood in Marfan Syndrome.

Data on mitral annular disjunction (MAD) in children with Marfan syndrome (MFS) are sparse. To investigate the diagnostic yield of MAD by echocardiography and cardiac magnetic resonance imaging (CMR), its prevalence and progression during childhood. We included patients <21 years old with MFS, defined by 2010 Ghent criteria and a pathogenic FBN1 variant or ectopia lentis. Two readers measured systolic separation between the mitral valve (MV) posterior hinge point and left ventricular (LV) myocardium on initial and subsequent imaging. MAD was defined as MV-LV separation ≥2 mm, MV prolapse (MVP) as atrial displacement ≥2 mm. Kappa coefficients evaluated echocardiogram-CMR agreement. Bland-Altman and intraclass correlation coefficients (ICC) assessed interrater and intermodality reliability. Univariable mixed-effects linear regression was used to evaluate longitudinal changes of MAD. MAD was detected in 60% (110/185) eligible patients. MVP was present in 48% (53/110) of MAD and MAD in 90% (53/59) of MVP. MAD detection by CMR and echocardiography had 96% overall agreement (Kappa = 0.89, p < 0.001) and a 0.32-mm estimate bias (95%CI 0.00, 0.65). ICC by echocardiography, CMR, and between modalities were 0.97 (95%CI 0.93, 0.98), 0.92 (95%CI 0.79, 0.97), and 0.91 (95%CI 0.85, 0.94), respectively. MAD was associated with aortic root dilation (p < 0.001). MAD was found in children of all ages, increased +0.18 mm/year (95%CI +0.14, + 0.22) during a median duration of 5.5 years (IQR 3.1, 7.5 years). MAD indexed by height yielded a constant value +0.0002 mm/m/year (95%CI -0.0002, + 0.0005 mm/m/year). MAD was common in pediatric MFS and was associated with aortic root dilation. MAD detection by echocardiography and CMR was highly reliable, suggesting that routine assessment in MFS is feasible. MAD was present in neonates and progressed over time but remained constant when indexing by height. Further studies are needed to evaluate MAD as a biomarker for clinical outcomes in pediatric MFS.

Causality between sleep traits and the risk of frailty: a Mendelian randomization study

BackgroundResearch based on observation has demonstrated a relationship between sleep traits and frailty; however, it remains uncertain if this correlation indicates causation. The purpose of this study was to look at the causal relationship that exists between frailty and sleep traits.MethodUsing summaries from a genome-wide association study of self-reported sleep features and frailty index, we performed a bidirectional Mendelian randomization (MR) analysis. Examining the causal relationships between seven sleep-related traits and frailty was the goal. The major method used to calculate effect estimates was the inverse-variance weighted method, supplemented by the weighted median and MR-Egger approaches. The study investigated pleiotropy and heterogeneity using several methodologies, such as the MR-Egger intercept, the MR-PRESSO approach, and the Cochran’s Q test. We took multivariate Mendelian randomization and genetic correlations between related traits to enhance the confidence of the results. Furthermore, we used MRlap to correct for any estimation bias due to sample overlap.ResultsInsomnia, napping during the day, and sleep apnea syndrome exhibited a positive connection with the frailty index in forward MR analysis. Conversely, there is a negative link between getting up in the morning, snoring and sleep duration with the frailty index. During the reverse MR analysis, the frailty index exhibited a positive correlation with insomnia, napping during the day, and sleep apnea syndrome, while demonstrating a negative correlation with sleep duration. There was no direct correlation between snoring, chronotype, and frailty. In MVMR analyses, the causal effect of sleep characteristics on frailty indices remained consistent after adjusting for potential confounders including BMI, smoking, and triglycerides.ConclusionThe findings of our investigation yield novel evidence that substantiates the notion of a bidirectional causal connection between sleep traits and frailty. Through the optimization of sleep, it is potentially feasible to hinder, postpone, or even reverse the state of frailty, and we proposed relevant interventions.

Experimental validation of an advanced impedance pneumography for monitoring ventilation volume during programmed cycling exercise.

&#xD;Impedance pneumography (IP) has provided static assessments of subjects' breathing patterns in previous studies. Evaluating the feasibility and limitation of ambulatory IP based respiratory monitoring needs further investigation on clinically relevant exercise designs. The aim of this study was to evaluate the capacity of an advanced IP in ambulatory respiratory monitoring, and its predictive value in independent ventilatory capacity quantification during cardiopulmonary exercise testing (CPET).&#xD;Approach:&#xD;35 volunteers were examined with the same calibration methodology and CPET exercise protocol comprising phases of rest, unloaded, incremental load, maximum load, recovery and further-recovery. In 3 or 4 deep breaths of calibration stage, thoracic impedance and criterion spirometric volume were simultaneously recorded to produce phase-specific prior calibration coefficients (CCs). The IP measurement during exercise protocol was converted by prior CCs to volume estimation curve and thus calculate minute ventilation (VE) independent from the spirometry approach.&#xD;Main results:&#xD;Across all measurements, the relative error of IP-derived VE (VER) and flowrate-derived VE (VEf) was less than 13.8%. In Bland-Altman plots, the aggregate VE estimation bias was statistically insignificant for all 3 phases with pedaling exercise and the discrepancy between VERand VEffell within the 95% limits of agreement (95% LoA) for 34 or all subjects in each of all CPET phases.&#xD;Significance: &#xD;This work reinforces the independent use of IP as an accurate and robust alternative to flowmeter for applications in cycle ergometry CPET, which could significantly encourage the clinical use of IP and improve the convenience and comfort of CPET.

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.

Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects-from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Reliability evaluation for Weibull distribution with heavily Type II censored data

AbstractThe lifetime data collected from the field are usually heavily censored, in which case, getting an accurate reliability evaluation based on heavily censored data is challenging. For heavily Type‐II censored data, the parameters estimation bias of traditional methods (i.e., maximum likelihood estimation (MLE) and least squares estimation (LSE)) are still large, and Bayesian methods are hard to specify the priors in practice. Therefore, considering the existing range of shape parameter for Weibull distribution, this study proposes two novel parameter estimation methods, the three‐step MLE method and the hybrid estimation method. For the three‐step MLE method, the initial estimates of shape and scale parameters are first respectively derived using MLE, then are updated by the single parameter MLE method with the range constraint of shape parameter. For the hybrid estimation method, the shape parameter is estimated by the LSE method with the existing range constraint of shape parameter, then the scale parameter estimate can be obtained by MLE. On this basis, two numerical examples are performed to demonstrate the consistency and effectiveness of the proposed methods. Finally, a case study on turbine engines is given to verify the effectiveness and applicability of the proposed methods.

Bias-reduced neural networks for parameter estimation in quantitative MRI.

To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound. We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications. In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as nonlinear least-squares fitting, while state-of-the-art NNs show larger deviations. The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy.

The impact of foreign participation on risk-taking in Chinese commercial banks: The co-governance role of equity checks and foreign supervision

This study examines how foreign equity participation affects the risk-taking of commercial banks using Chinese bank-level data from 2012 to 2020. The results indicate that foreign equity participation significantly reduces bank risk, and there is a U-shaped relationship between the foreign shareholding ratio and bank risk. Mechanism analysis reveals that augmenting foreign ownership strengthens shareholder equilibrium and boosts the presence of foreign directors. This structure enhances dual governance mechanisms, lowering the risk in banking activities. Considering that confounding factors affecting mediator and dependent variables can cause bias in coefficient estimation, we used machine learning methods to determine the best predictive factors affecting bank risk-taking. We indirectly verified the theoretically driven empirical model by evaluating more explanatory variables and selecting subsets of explanatory variables. In addition, we find that governance enhancements are particularly pronounced in Chinese banks, characterized by smaller scale and less income diversification. Additionally, foreign ownership significantly impacts banks' market risk more than credit and operational risks. Our results imply that China's financial regulatory authorities need to shift the focus of supervision to examining foreign shareholding ratio and corporate governance ability.

Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure.

Regression calibration as developed by Rosner, Spiegelman, and Willett is used to adjust the bias in effect estimates due to measurement error in continuous exposures. The method involves two models: a measurement error model relating the mismeasured exposure to the true (or gold-standard) exposure and an outcome model relating the mismeasured exposure to the outcome. However, no comprehensive guidance exists for determining which covariates should be included in each model. In this article, we investigate the selection of the minimal and most efficient covariate adjustment sets under a causal inference framework. We show that to address the measurement error, researchers must adjust for, in both measurement error and outcome models, any common causes (1) of true exposure and the outcome and (2) of measurement error and the outcome. We also show that adjusting for so-called prognostic variables that are independent of true exposure and measurement error in the outcome model, may increase efficiency, while adjusting for any covariates that are associated only with true exposure generally results in efficiency loss in realistic settings. We apply the proposed covariate selection approach to the Health Professional Follow-up Study dataset to study the effect of fiber intake on cardiovascular disease. Finally, we extend the originally proposed estimators to a nonparametric setting where effect modification by covariates is allowed.

Mineralogical and environmental effects on the δ13C, δ18O, and clumped-isotope composition of modern bryozoans

Extensive bryozoan fossil records date back to the early Ordovician, forming an important part of sedimentary archives, yet the applicability of classical 18‑oxygen thermometry to bryozoan carbonate is still a matter of debate, with mineralogical and biological issues still hindering paleoclimate reconstructions. Complementing other methods (i.e., δ18O, Mg/Ca), clumped-isotope thermometry (Δ47) could provide more accurate paleotemperature estimates and help identify potential biotic factors influencing the isotopic composition of bryozoans. Here we report on the first investigation of clumped-isotope thermometry applied to bryozoan carbonate, spanning a broad range of modern species living in different environments from two localities (Atlantic Ocean and Mediterranean Sea). We confirm that bryozoan δ13C and δ18O records are affected by biotic and abiotic factors susceptible to bias in growth condition estimates. Our Atlantic bryozoans yield Δ47 derived temperatures (T47) consistent with spring/fall seawater temperature (but only after correcting for minor mineralogical effects), reflecting seasonal growth bias or, more likely, moderate isotopic disequilibrium. By contrast, Mediterranean samples display large positive offsets from Δ47 equilibrium values. We propose that this stronger disequilibrium is related to the higher salinity at this site, decreasing Carbonic Anhydrase activity, favoring CO2 hydroxylation over hydration, and slowing down DIC (dissolved inorganic carbon) equilibration reactions at the precipitation site. Our findings highlight how “vital effects” in bryozoans is not only species-specific as often assumed for other biocalcifiers, but could also depend on mineralogy and environmental factors.

Target and Nontarget Screening to Support Capacity Scaling for Substance Use Assessment through a Statewide Wastewater Surveillance Network in New York.

Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography-high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.

Something Out of Nothing? The Influence of Double-Zero Studies in Meta-analysis of Adverse Events in Clinical Trials

AbstractIn addition to clinical efficacy, safety is another important outcome to assess in randomized controlled trials. It focuses on the occurrence of adverse events, such as stroke, deaths, and other rare events. Because of the low or very low rates of observing adverse events, meta-analysis is often used to pooled together evidence from dozens or even hundreds of similar clinical trials to strengthen inference. A well-known issue in rare-event meta-analysis is that some or even majority of the available studies may observe zero events in both the treatment and control groups. The influence of these so-called double-zero studies has been researched in the literature, which nevertheless focuses on reaching a dichotomous conclusion—whether or not double-zero studies should be included in the analysis. It has not been addressed when and how they contribute to inference, especially for the odds ratio. This paper fills this gap using comparative analysis of real and simulated data sets. We find that a double-zero study contributes to the odds ratio inference through its sample sizes in the two arms. When a double-zero study has an unequal allocation of patients in its two arms, it may contain non-ignorable information. Exclusion of these studies, if taking a significant proportion of the study cohort, may result in inflated type I error, deteriorated testing power, and increased estimation bias.

A simple method to construct circular repeated measurement design classes and efficiently control carry over effects

Repeated measurement designs (RMDs) are economical and commonly utilized in pharmacology, psychology, and animal science experiments. However, RMDs might have carryover effects, which become the main source of bias in estimation of treatment effects. Carry over effects can economically be controlled with the use of minimal balanced RMDs, strongly balanced RMDs or their generalized classes. No general procedure is available in literature to obtain these important classes of RMDs. An R-Package has been developed in this article to check the given values v and p for the possible existence of minimal circular balanced and strongly balanced RMDs (along with their generalized classes), generate the sets of shifts for possible designs, and to calculate the efficiency of separability and carry over effects for the generated design.

Estimating Market Power Exertion in the U.S. Beef Packing Industry: An Illustration of Data Aggregation Bias Using Simulated Data

This study investigates data aggregation bias in estimating market power in the U.S. beef packing industry using New Empirical Industrial Organization (NEIO) models and shows empirical procedures that can alleviate the bias. Unlike many earlier studies in estimating market power exertion, our study examines the data aggregation bias when market-level data are used in place of firm-level data and show how the bias could be reduced. We first derive data aggregation bias analytically, then empirically investigate the aggregation bias by estimating both firm and aggregate industry models. Because the firm-level data are not available, we use simulated data generated from the Monte Carlo simulation method. Hybrid models, combining limited firm-level data with aggregate data, are also estimated to illustrate how the aggregation bias could be reduced. Our results show that aggregate models with industry-level data tend to underestimate market power exertion in the U.S. beef packing industry, and the aggregation bias is statistically significant at the 1% level. Comparing results from hybrid models with firm-level estimates, we find that hybrid models reduce the bias but do not remove the aggregation bias significantly. The sensitivity analysis shows that market power estimate and aggregation bias are sensitive to functional forms.

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle.

Introduction: Xinjiang Brown cattle constitute the largest breed of cattle in Xinjiang. Therefore, it is crucial to establish a genomic evaluation system, especially for those with low levels of breed improvement. Methods: This study aimed to establish a cross breed joint reference population by analyzing the genetic structure of 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle (Illumina GeneSeek GGP bovine 150K chip). The Bayes method single-step genome-wide best linear unbiased prediction was used to conduct a genomic evaluation of the joint reference population for the milk traits of Xinjiang Brown cattle. The reference population of Chinese Holstein cattle was randomly divided into groups to construct the joint reference population. By comparing the prediction accuracy, estimation bias, and inflation coefficient of the validation population, the optimal number of joint reference populations was determined. Results and Discussion: The results indicated a distinct genetic structure difference between the two breeds of adult cows, and both breeds should be considered when constructing multi-breed joint reference and validation populations. The reliability range of genome prediction of milk traits in the joint reference population was 0.142-0.465. Initially, it was determined that the inclusion of 600 and 900 Chinese Holstein cattle in the joint reference population positively impacted the genomic prediction of Xinjiang Brown cattle to certain extent. It was feasible to incorporate the Chinese Holstein into Xinjiang Brown cattle population to form a joint reference population for multi-breed genomic evaluation. However, for different Xinjiang Brown cattle populations, a fixed number of Chinese Holstein cattle cannot be directly added during multi-breed genomic selection. Pre-evaluation analysis based on the genetic structure, kinship, and other factors of the current population is required to ensure the authenticity and reliability of genomic predictions and improve estimation accuracy.

Distance mis-estimations can be reduced with specific shadow locations

Shadows in physical space are copious, yet the impact of specific shadow placement and their abundance is yet to be determined in virtual environments. This experiment aimed to identify whether a target’s shadow was used as a distance indicator in the presence of binocular distance cues. Six lighting conditions were created and presented in virtual reality for participants to perform a perceptual matching task. The task was repeated in a cluttered and sparse environment, where the number of cast shadows (and their placement) varied. Performance in this task was measured by the directional bias of distance estimates and variability of responses. No significant difference was found between the sparse and cluttered environments, however due to the large amount of variance, one explanation is that some participants utilised the clutter objects as anchors to aid them, while others found them distracting. Under-setting of distances was found in all conditions and environments, as predicted. Having an ambient light source produced the most variable and inaccurate estimates of distance, whereas lighting positioned above the target reduced the mis-estimation of distances perceived.

Distance and T-square sampling for spatial measures of tree diversity

Distance sampling and its statistically improved variant, T-square sampling, are important sampling methods in plant ecology. They have often been applied in the context of plant density estimations and are comparatively easy to implement, since they intuitively follow the nearest-neighbour principle and thus do not require the layout of sample plots. Previous research studying distance sampling suggested that T-square sampling may also lead to an improved estimation of spatial tree diversity indices. We simulated distance and T-square sampling in six large fully mapped forest areas for seven tree diversity indices of which some competed for the same diversity aspect, i.e. tree location (dispersion), tree species and tree size diversity. Our results demonstrated that both distance and T-square sampling are indeed robust methods for sampling spatial measures of tree diversity. The sample size required for a sampling error of 10% does not exceed 20% of the total number of trees in a sampling area. T-square sampling has the ability to adapt to different spatial patterns of tree locations and this ability is key to the way the method controls estimation bias. The sample size required for species mingling and size differentiation clearly depends on the underlying spatial tree pattern in the sampling area. With most diversity indices, sample size reductions between 0.06% and 40% could be achieved by the application of T-square sampling compared to traditional distance sampling. All other conditions being equal, we could identify the uniform angle index, the species mingling index and the size differentiation index as those diversity indices achieving lower sampling error values than their competitors. For tree density estimations the Diggle and Byth estimators performed best. Based on our results, T-square sampling can be considered a robust sampling method for spatial tree diversity indices that is easy to apply in the field.

A novel controllable bias reinforcement learning method for distributed automatic generation control integrated with large-scale electric vehicles

Due to the introduction of large-scale renewable energy resources and flexible load access, strong random disturbances will bring to the current integrated energy system. The traditional control methods fall short in adequately addressing the frequency regulation needs of distributed systems. From the perspective of distributed automatic generation control (AGC), an optimal coordinated control method is proposed based on reinforcement learning. The proposed method is developed to mitigate the estimation bias of the action-values via the minimum action-value from multiple action-value estimates. Further, the bias of action-value estimates can be adjusted from positive to negative to enhance the control accuracy. Then this method uses behaviour utility trace to achieve adaptive adjustment of the exploration rate, preventing repetitive and unbalanced exploration. Simulation experiments have been performed on the two-area LFC model integrated with electric vehicles and the multi-area integrated energy system model to verify the effectiveness of the proposed control method.

An Overestimation Reduction Method Based on the Multi-step Weighted Double Estimation Using Value-Decomposition Multi-agent Reinforcement Learning

The joint action-value function (JAVF) plays a key role in the centralized training of multi-agent deep reinforcement learning (MADRL)-based algorithms using the value function decomposition (VFD) and in the generating process of a collaborative policy between agents. However, under the influence of multiple factors such as environmental noise, inadequate exploration and iterative updating mechanism, estimation bias is inevitably introduced, causing its overestimation problem, which in turn prevents agents from obtaining accurate reward signals during the learning process, and fails to correctly approximate the optimal policy. To address this problem, this paper first analyzes the causes of joint action-value function overestimation, gives the corresponding mathematical proofs and theoretical derivations, and obtains the lower bound of the overestimation error; then, a MADRL overestimation reduction method based on the multi-step weighted double estimation named λWD QMIX is proposed. Specifically, the λWD QMIX method effectively achieves more stable and accurate JAVF estimation results using the bias correction estimation mechanisms based on the weighted double estimation and multi-step updating based on eligibility trace backup, without additionally adding or changing any network structure. The results of a series of experiments on the StarCraft II micromanipulation benchmark show that the proposed λWD QMIX algorithm can effectively improve the final performance and learning efficiency of the baseline algorithm, and can be seamlessly integrated with the partially MADRL algorithms based on communication learning.

How Does Queueing Information in Pre-Sales Call Centers Affect Customer Repurchase Behavior

With the growth of mobile e-business, it has become common for customers to experience an explosion of instant desire to consume in a short period of time. Companies are harnessing this momentum, hoping to better capture, retain and convert these consumer desires into actual sales orders. Consequently, companies also need to use pre-sales call centers to provide the satisfied service and motivate more customers to repurchase, thereby increasing the revenue. The function of the call centers is updated from providing post-sales service to providing pre-sales consultation. In this regard, this paper examines the impact of delayed announcements consisting of queue information on customer repurchase behavior in pre-sales call centers. We classify the queuing information into no-information, part-information and full-information according to the level of information by constructing a simulation model. In the simulation experiments, some application scenarios are set up to describe the load of the call center. In all the given scenarios, we find out the full-information, which provides the delay time, is always the best. Moreover, the different methods for estimating the delay time to provide the full-information, namely LES, EA, EA2 and WA–LES, are compared. We find optimal announcements for the application scenarios set out in the time-varying scenario as well. Consequently, considering there exists certain cost for the company to make WA–LES announcements, while some call centers can only provide part-information instead, this paper investigates the impact of estimate bias on customer repurchase behavior and company revenue. Subsequently, the company revenue is further investigated in two extended experiments, where repurchase influence power and repurchase number are limited. The analysis ideas and simulation logic of this paper have good reference significance for e-commerce pre-sales call centers, and the suggestions for setting information levels in practice while considering customer repurchase behavior are also clearly given in the conclusion of the paper.

Bias correction in species distribution models based on geographic and environmental characteristics

Correcting sampling bias in species distribution models (SDMs) is challenging. The difficulty lies in accurately identifying and quantifying bias and the scarcity of samples, which greatly impedes the implementation of bias correction. Current methods often adjust the distribution of presence or background points within geographic or environmental spaces to correct the sampling bias in probability estimation within SDMs. However, these methods may lead to information loss, rely on subjective assumptions, and often separate geography and environment when correcting for bias. This study proposes a novel and easily implementable method termed “aggregation background.” This method selects background data based on the aggregation degree of presence points in the geographic and environmental feature space, thereby approximating the representation and correction of sampling bias in the presence samples. We compared this new method with other prevalent sampling bias correction methods in the existing literature by analyzing ecological authenticity. Under varying biases and sample sizes, the aggregation background and geographic filtering methods achieved more accurate species distribution predictions compared to the target group background and other methods. Notably, when the sample size was small (≤70), the aggregation background was superior to that obtained using the geographic filtering method. These findings underscore the effectiveness of the aggregation background in improving bias correction using limited available presence sample data, without relying on assumptions about sampling bias. Our method provides a new approach for correcting complex unknown biases in SDMs.