Classical Estimation Research Articles

Detrimental effects of scene manipulations on temperature-based time since death estimation

In forensic casework, time since death (TSD) estimations may play a crucial role to establish chains of events as well as for alibi assessment in homicide cases. Classical TSD estimation relies on reasonably stable ambient temperatures and a correct documentation of ambient and rectal temperatures. This constancy is in some cases disturbed by post-discovery alterations of the crime scene, e.g. opening a window. In order to develop a better understanding of this alteration-based detrimental impact on TSD estimation as well as to identify feasible recommendations for casework, the present pilot study examined ambient temperature effects of different window opening scenarios regarding various time intervals (5 to 360 min) in a furnished 10 m2 apartment during winter. In this context, in addition to the ambient temperature and thus the cooling rate of the room, re-approximation to initial room temperature, potential influences on a nomogram-based time since death estimation using a fictitious case, and the impact of the measurement height above the ground were investigated. Our data indicate a significant reduction of the mean temperature decrease rate after 15 min regardless of the remaining opening time and a correlation with the size of the respective opening surfaces. Re-approximation to initial room temperatures was observed with up to three times longer than the initial opening time. There was no evidence of a substantial advantage of temperature measurements above the level of the corpse (> 0.1 m). The limitations of the study and its applicability for forensic casework are critically reviewed.

Improved Underwater Single-Vector Acoustic DOA Estimation via Vector Convolution Preprocessing

Remote passive sonar detection with underwater acoustic vector sensor (UAVS) has attracted increasing attention due to its merit in measuring the full sound field information. However, the accurate estimation of the direction-of-arrival (DOA) remains a challenging problem, especially under low signal-to-noise ratio (SNR) conditions. In this paper, a novel convolution (COV)-based single-vector acoustic preprocessing method is proposed on the basis of the single-vector acoustic preprocessing model. In view of the theoretical analysis of the classical single-vector acoustic DOA estimation method, the principle of preprocessing can be described as “to achieve an improved denoising performance in the constraint of equivalent amplitude gain and phase response.” This can be naturally guaranteed by our proposed COV method. In addition, the upper bound with matched filtering (MF) preprocessing is provided in the consideration of the optimal linear signal processing for weak signal detection under Gaussian noise. Numerical analyses demonstrate the effectiveness of our proposed preprocessing method with both vector array signal processing-based and intensity-based methods. Experimental verification conducted in South China Sea further verifies the effectiveness of our approach for practical applications. This work can lay a solid foundation in improving underwater remote vector acoustic DOA estimation under low SNR, and can provide important guidance for future research work.

An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation

This paper proposes an innovative global solution which is a pioneering work applying automated machine learning algorithms to remarkable precision sparse underwater direction-of-arrival (DOA) estimation that views the subaquatic sparse-sampling DOA estimation problem as a classification prediction task. The proposed solution, termed automated multi-layer perceptron discriminative neural network (AutoMPDNN), is built upon a Bayesian optimization framework. AutoMPDNN transforms sparsely sampled time-domain signals into the complex domain, preserving essential components in a one-source single-snapshot scenario. Leveraging Bayesian optimization principles, the algorithm embeds necessary hyperparameters into the loss function, effectively defining it as a maximum likelihood problem using the upper confidence bound function and incorporating sparse signal features. We also explore the model space architecture and introduce variants of AutoMPDNN, denoted as AutoMPDNNs_ln (n = 2,3,4). Through a series of plane wave simulation experiments, it is demonstrated that AutoMPDNN achieves the highest prediction performance for one-source single-snapshot scenarios compared to classical DOA estimation algorithms that incorporate sparse representation approaches, as well as contemporary deep learning DOA methods under varying conditions.

Abstract PO5-21-04: Leveraging Digital Twins for Patient Stratification and Treatment Optimization in geriatric oncology: A Breast Cancer Multivariate Clustering Analysis

Abstract Context: Geriatric oncology necessitates an accurate evaluation of a patient's health status and cancer characteristics. Digital twins, virtual replicas of a patient and their cancer, can aid in predicting real-world behaviors and support therapeutic decision-making. Objectives: This study aims to demonstrate how digital twins can optimize treatments by simulating various scenarios based on the individual characteristics of patients and their cancers. Methods: Creation of a digital twin relies on the use of advanced manifold learning technologies. A model is built to represent the variability present in the reference cohort, capturing complex relationships and inherent data structures. A reference digital twin is generated by drawing samples from this model, thus reflecting the characteristics distribution of the reference population. Stratification (MeanShift algorithm) enables cohort structure visualization and classical statistics estimation for each stratum. Multivariate analysis was performed on a French monocentric population of 1345 patients aged over 70 years who underwent surgery for HER2-negative early breast cancer. Results: 8 cluster, created by manifold learning based on biological, demographic, and tumoral variables, exhibits unique five-year survival rates and influential factors. The clusters represent specific patient subgroups, each with its distinct set of characteristics and prognostic indicators. Table 1 provides a summary of these variables' averages within each cluster. we can thus see that the variables of clusters 3 and 6 (including only HR+ cancers) do not show statistically significant differences, unlike their 5-year survival rates (cluster 3 : 80,9% (CI95 :70-88.5) and 63.9% (CI95 :47.6-77.5) for cluster 6, p= 0,04). Provided with model results, the relative importance of these variables varies from one cluster to another, underscoring the heterogeneity among patient subgroups. Interestingly, Hormonal receptor status, nodal involvement and tumoral grade were not the predominant variables in these clusters, which challenges traditional perspectives. Table 1 : Mean value of variables per cluster (SBR grade 0=grade 1 and 2 ; 1= grade 3) Discussion: The findings underscore the vast variability of individual variable importance across clusters. Digital twins enabled the modeling of these complex interactions, providing valuable insights for clinical decision-making. Conclusion: Digital twins offer a valuable tool for personalizing care in oncogeriatrics, facilitating the identification of patient subgroups, yielding more accurate treatment outcomes, and guiding future research directions. The results affirm that incorporating digital twins into oncogeriatric care could potentially lead to the enhanced personalization of therapeutic approaches, ultimately improving patient outcomes. Table. Citation Format: Pierre Heudel, Mashal Ahmed, Felix Renard. Leveraging Digital Twins for Patient Stratification and Treatment Optimization in geriatric oncology: A Breast Cancer Multivariate Clustering Analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-21-04.

Self-error learning framework-based algorithm for parameter recovery of extended Wiener–Hammerstein systems subject to quantised measurements

In this study, a novel estimation scheme is proposed for identifying extended Wiener–Hammerstein systems with hysteresis nonlinearity subject to quantised measurements. The proposed scheme is established in a self-error learning framework to achieve high-performance parameter estimation compared with classic error feedback learning estimation algorithms. Initially, the useful identification data can be extracted from contaminated system data by introducing an adaptive filter. Then, with the help of the filtered data, the identification error expression used to establish the estimator is derived. Subsequently, an online compensation estimation error variable is proposed to eliminate the effect of the regression vector on the convergence performance. A new adaptive law is designed with adaptive recursive gain, considering the compensation estimation error data and parameter initial error data. Under general persistent excitation (PE) condition, the PE condition of the regressor information is verified online, and the estimator convergence is strictly proven. Finally, the statistical results of two illustrated examples and a real-word example are provided to validate the positive features and effectiveness of the proposed estimation scheme.

Adaptive Kalman filter with LSTM network assistance for abnormal measurements

A classic state estimation method, the Kalman filter integrates prior information, system dynamics models, and measurement data to achieve posterior state estimations. However, measurements often encounter various unknown disturbances, leading to abnormal or inaccurate measurements and subsequently impacting the performance of Kalman filtering. In response to this challenge, this paper introduces a novel adaptive Kalman filter approach aided by posterior state estimations using the Long short-term memory (LSTM) networks. The proposed method begins by utilizing prior residuals to construct a chi-square distribution model, which facilitates the detection of abnormal measurements within data. Upon identifying abnormal measurements, an LSTM network is employed to generate alternative predictive measurements, replacing the original inaccurate measurement. This approach enhances the model’s capability to handle complex relationship and measurement uncertainties and improves filtering estimation accuracy. A noise covariance adjustment method is introduced in extreme cases where alternative predictive measurements alone are insufficient for filter requirements. This method mitigates the adverse effects of abnormal measurements on posterior estimation. Throughout the entire adaptive assistance process involving the LSTM network, deep learning maintains a stable structure of the filter while enhancing adaptability. This strategy ensures the filter’s resilience in dynamic environments with unknown factors by providing predicted measurements conforming to the chi-square distribution and corresponding measurement noise covariance. Ultimately, the efficacy of the proposed algorithm is validated through simulations and experiments involving vehicle positioning with inertial sensors.

Velocity-based rack storage location assignment for the unidirectional robotic mobile fulfillment system

Nowadays, the robotic mobile fulfillment system (RMFS) has been increasingly used by online retailers. Compared with traditional picker-to-parts warehouses, the racks of RMFS do not have to return to the same location after picking, thus we can dynamically change their locations, which brings great potential to efficiently fulfill orders. Aiming at minimizing the sum of rack travel distances, the key question is how to reassign a rack to an unoccupied storage location after picking items from the rack. However, the issue involves two challenges for unidirectional RMFS, one is that huge differences may exist between the classical Manhattan distance estimation and the actual distance for the unidirectional aisles in RMFS, and the other one is that we need to account for the frequency of rack moving for multi-item orders. We thus first propose closed-form formulas to optimally estimate the cycle travel distance for each rack. Then, by overcoming the repeated counting issue for multi-item orders, we propose a novel SKU (Stock Keeping Units)-correlation-based algorithm to choose high-velocity racks, which can better fulfill multi-item orders. Finally, embedding the cycle travel distance and SKU-correlation-based velocity, we propose a Velocity-based Rack Storage Location Assignment method (VRSLA) to solve the rack storage location assignment problem by assigning high-velocity racks to the nearest storage locations. Collaborating with a large online retailer in China, we demonstrate the performance of VRSLA by using both small-scale and large-scale datasets. The computational results show that VRSLA not only can achieve near-best solutions compared with an integer programming model solved by Gurobi, but also outperforms four state-of-the-art assignment methods in literature (random, velocity-based class, shortest path, and sale-based) by reducing the rack travel distance up to 43.32%. We also found that the stronger the correlation between SKUs on the racks or the larger the size of the RMFS, the shorter the rack travel distance by the proposed VRSLA method.

Learning‐based monocular visual‐inertial odometry with SE2(3) $S{E}_{2}(3)$‐EKF

AbstractLearning‐based visual odometry (VO) becomes popular as it achieves a remarkable performance without manually crafted image processing and burdensome calibration. Meanwhile, the inertial navigation can provide a localization solution to assist VO when the VO produces poor state estimation under challenging visual conditions. Therefore, the combination of learning‐based technique and classical state estimation method can further improve the performance of pose estimation. In this paper, we propose a learning‐based visual‐inertial odometry (VIO) algorithm, which consists of an end‐to‐end VO network and an ‐Extended Kalman Filter (EKF). The VO network mainly combines a convolutional neural network with a recurrent neural network, taking advantage of two consecutive monocular images to produce relative pose estimation with associated uncertainties. The ‐EKF, which is proved to overcome the inconsistency issues of VIO, propagates inertial measurement unit kinematics‐based states, and fuses relative measurements and uncertainties from the VO network in its update step. The extensive experimental results on the KITTI data set and the EuRoC data set demonstrate the superior performance of the proposed method compared to other related methods.

Quasi‐Likelihood Estimation in Volatility Models for Semi‐Continuous Time Series

Time series containing non‐negligible portion of possibly dependent zeros, whereas the remaining observations are positive, are considered. They are regarded as GARCH processes consisting of non‐negative values. Our first aim lies in estimation of the omnibus model parameters taking into account the semi‐continuous distribution. The hurdle distribution together with dependent zeros cause that the classical GARCH estimation techniques fail. Two different quasi‐likelihood approaches are employed. Both estimators are proved to be strongly consistent and asymptotically normal. The second goal consists in the proposed predictions with bootstrap add‐ons. The considered class of models can be reformulated as multiplicative error models. The empirical properties are illustrated in a simulation study, which demonstrates computational efficiency of the employed methods. The developed techniques are presented through an actuarial problem concerning insurance claims.

Statistical mirroring: A robust method for statistical dispersion estimation

This study introduces statistical mirroring as an innovative approach to statistical dispersion estimation, drawing inspiration from the Kabirian-based isomorphic optinalysis model, aimed at enhancing robustness and mitigating biases in estimation methods. Beyond scale-invariant characteristics, the proposed estimators emphasize scaloc-invariant robustness, thereby addressing a critical gap in dispersion estimation. By highlighting statistical meanic mirroring, alongside other forms of proposed statistical mirroring, the study underscores the adaptability and customization potential. Through extensive Monte Carlo simulations and real-life applications, in comparison with classical estimators, the results of the performance evaluation of the proposed estimators demonstrate robustness, efficiency, and transformations-invariance. The research offers a paradigm shift in addressing longstanding challenges in dispersion estimation, offering a new category of dispersion estimation and increased resistance to outliers. Notable limitations include selecting and evaluating the proposed statistical meanic mirroring under Gaussian and Gaussian mixture model distributions. This research paper significantly contributes to statistical methodologies, offering avenues for expanding knowledge in dispersion estimation. It recommends further exploration of proposed estimators across various statistical mirroring types and encourages comparative studies to establish their effectiveness, thereby advancing statistical knowledge and tools for precise data analysis.•The proposed methodology involves preprocessing transformations, statistical mirror design, and optimization to transform a univariate set into a bivariate one, facilitating the fitting of an isomorphic optinalysis model.•Estimators rely on a foundational bijective mapping of isoreflective pairs, deducing the probability of proximity or deviation from any defined center. This contrasts with classical estimators that utilize average or median deviations from a mean or median center.

Estimation in shape mixtures of skew-normal linear regression models via ECM coupled with Gibbs sampling

Abstract In this paper, we study linear regression models in which the error term has shape mixtures of skew-normal distribution. This type of distribution belongs to the skew-normal (SN) distribution class that can be used for heavy tails and asymmetry data. For the first time, for the classical (non-Bayesian) estimation of the parameters of the SN family, we apply the Markov chains Monte Carlo ECM (MCMC-ECM) algorithm where the samples are generated by Gibbs sampling, denoted by Gibbs-ECM, and also, we extend two other types of the EM algorithm for the above model. Finally, the proposed method is evaluated through a simulation and compared with the Numerical Math-ECM algorithm and Monte Carlo ECM (MC-ECM) using a real data set.

Probabilistic modeling of COVID-19 events: Exploring new alpha generated family for enhanced analysis capabilities

This study explores a new family of distributions to enhance the analysis capabilities of COVID-19 events, providing valuable insights for informed decision-making and effective public health management. Accurate understanding and prediction of COVID-19 transmission patterns and their impacts are crucial in reducing the mortality rate, especially in proactive control and mitigation efforts. The proposed approach focuses on a best-fit probabilistic model that incorporates a comprehensive assessment of various statistical measures. Additionally, seven well-recognized classical point estimation methods are employed to identify the most suitable approach for assisting epidemiologists in their analysis. The study analyzes COVID-19 data from multiple countries, including the Netherlands, Mexico, the United Kingdom, China, Canada, Saudi Arabia, and Italy, considering different aspects such as mortality rates and the number of deaths. By evaluating the performance of the new alpha generated family of distributions in modeling COVID-19 events. This research contributes to the advancement of our understanding of the disease's probabilistic nature. The findings have practical implications, guiding the development of public health policies, resource allocation strategies, and intervention plans, ultimately facilitating more effective control and mitigation of COVID-19 outbreaks.

Safe Abductive Learning in the Presence of Inaccurate Rules

Integrating complementary strengths of raw data and logical rules to improve the learning generalization has been recently shown promising and effective, e.g., abductive learning is one generic framework that can learn the perception model from data and reason between rules simultaneously. However, the performance would be seriously decreased when inaccurate logical rules appear, which may be even worse than baselines using only raw data. Efforts on this issue are highly desired while remain to be limited. This paper proposes a simple and effective safe abductive learning method to alleviate the harm caused by inaccurate rules. Unlike the existing methods which directly use all rules without correctness checks, it utilizes them selectively by constructing a graphical model with an adaptive reasoning process to prevent performance hazards. Theoretically, we show that induction and abduction are mutually beneficial, and can be rigorously justified from a classical maximum likelihood estimation perspective. Experiments on diverse tasks show that our method can tolerate at least twice as many inaccurate rules as accurate ones and achieve highly competitive performance while other methods can't. Moreover, the proposal can refine inaccurate rules and works well in extended weakly supervised scenarios.

Abstract 3565: The Cox proportional hazards mixed model enables accurate estimation of the total effect somatic SNVs have on treatment outcomes

Abstract Heritability is the variation in disease susceptibility attributed to genetic variation and is a fundamental parameter for interpreting disease architecture and making predictions. In the context of germline genetics, variance component models are commonly used to estimate heritability for continuous or case-control phenotypes (under the liability threshold model, LTM). However, models for time-to-event (TTE) outcomes have been largely unstudied. This has limited the characterization of age-of-onset and treatment response phenotypes, the latter particularly relevant for clinical studies. Here, we propose and evaluate a Cox proportional hazard Mixed Model (COXMM) to enable accurate estimation for TTE phenotypes with censoring. We apply these methods to Real World data from the Profile cohort from Dana-Farber Cancer Institute, a large prospectively collected tumor sequencing cohort with detailed clinical annotation. We implemented an efficient COXMM with a random effect modeled by the genetic relatedness across individuals, and benchmarked its performance in extensive simulations. We simulated phenotypes according to generative models reflecting either LTM or TTE models. For TTE phenotypes, data followed various Weibull distributions with independent censoring. Across TTE simulations, COXMM produced unbiased estimates while the classic case-control heritability estimator showed significant downward bias. Likewise, restricting only to cases and estimating the heritability of age-of-onset as a normalized continuous phenotype produced biased estimates, due to the artificial exclusion of controls. For example, for an age-of-onset with heritability of 0.8 and 40% of individuals in the study being cases, COXMM correctly inferred the heritability to be 0.80 +/- 0.007, compared to 0.32 +/- 0.004 for a conventional case-control model, and 0.09 +/- 0.02 for a conventional case-only age-of-onset model. In the Profile cohort, we analyzed the impact of somatic SNVs on progression free survival (PFS) and overall survival (OS) in patients with non-small cell lung cancer (N=954 and N=1796, respectively). We restricted to somatic SNVs present in at least 5% of patients. For OS, we observed that the COXMM heritability estimate was significantly larger than those computed by case-control status and using a normalized case-only age/duration (COXMM: 0.19 +/- 0.003; Case-Control: 0.07 +/- 0.2; Duration: 0.04 +/- 0.01). We observed a similar phenomenon for PFS as well (COXMM: 0.11 +/- 0.01; Case-Control: 0.03 +/- 0.4; Duration: 0.03 +/- 0.02). These results demonstrate that somatic features are most strongly associated with TTE and can be accurately estimated with COXMM. COXMM enables the efficient quantification of TTE heritability for diverse clinical phenotypes and outperforms existing methods. Citation Format: Kodi Taraszka, Alexander Gusev. The Cox proportional hazards mixed model enables accurate estimation of the total effect somatic SNVs have on treatment outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3565.

Removability conditions for anisotropic parabolic equations in a computational validation

The article investigates removability conditions for singularities of anisotropic parabolic equations and in particular for the anisotropic porous medium equation and it aims in the numerical validation of the analytical results. The preconditions on the strength of the anisotropy are analyzed, and the analytical estimates for the growth behavior of the solutions near the singularities are compared with the observed growth in numerical simulations. Despite classical estimates used in the proof, we find that the analytical estimates are surprisingly close to the numerically observed solution behavior.

Estimating the survival function Maximum Likelihood and Cramér-Von Mises Method use of amixture distribution weibull and ailamujia with apractical application

Tow failure distributions were mixed ,weibull distribution and ailamujia distribution it's called weibull –ailamujia distribution which consists of four features, the two distributions were mixed by adding a mixing parameter ,the mixture of two distributions would be more efficient ,and representative of the data than the single distributions. A group of statistical properties of the mixture distribution were studied , such as the probability function ,the survival function and the failure rate function .classical estimation method were used to estimate the parameters of the mixture distribution, which are maximum likelihood of method ,the cramer –von mises method ,The method were compared using monte carlo simulations real data were taken regarding drug –resistant tuberculosis (TB) Multi Drug Resistance(MDR) methods were compared using the MSE statistical criterion ,and the result should a preference for the method cramer-von mises for having the lowest MSE

Reliability, validity and acceptability of an online clinical reasoning simulator for medical students: An international pilot

Introduction Clinical reasoning skills are essential for decision-making. Current assessment methods are limited when testing clinical reasoning and management of uncertainty. This study evaluates the reliability, validity and acceptability of Practicum Script, an online simulation-based programme, for developing medical students’ clinical reasoning skills using real-life cases. Methods In 2020, we conducted an international, multicentre pilot study using 20 clinical cases with 2457 final-year medical students from 21 schools worldwide. Psychometric analysis was performed (n = 1502 students completing at least 80% of cases). Classical estimates of reliability for three test domains (hypothesis generation, hypothesis argumentation and knowledge application) were calculated using Cronbach’s alpha and McDonald’s omega coefficients. Validity evidence was obtained by confirmatory factor analysis (CFA) and measurement alignment (MA). Items from the knowledge application domain were analysed using cognitive diagnostic modelling (CDM). Acceptability was evaluated by an anonymous student survey. Results Reliability estimates were high with narrow confidence intervals. CFA revealed acceptable goodness-of-fit indices for the proposed three-factor model. CDM analysis demonstrated good absolute test fit and high classification accuracy estimates. Student survey responses showed high levels of acceptability. Conclusion Our findings suggest that Practicum Script is a useful resource for strengthening students’ clinical reasoning skills and ability to manage uncertainty.

Comparing some estimation methods for Frechet Distribution (Simulation)

The Frechet distribution, widely utilized in various fields such as hydrology, finance, and environmental sciences, poses challenges in parameter estimation, This study aims to compare several estimation methods for the Frechet distribution under scenarios with outlier observations. We consider both classical and robust estimation techniques, including ( Maximum Likelihood Estimation (MLE), Method of Moments (MOM), Least Squares Estimation (LSE), and Robust Regression methods(RRE)). To evaluate the performance of these methods, extensive simulation studies are conducted under different sample sizes and intial parameter values .mean squared error, are employed to assess the accuracy and robustness of each method under varying conditions. Our findings provide insights into the effectiveness and limitations of different estimation approaches for the Frechet distribution when simulation parameters are present, aiding practitioners in selecting suitable methods for robust parameter estimation in practical applications. Simulation results show that estimation method effected with (sample size, initial parameter values and evaluation criteria) , Bayesian estimation methods can be compared with Non-Bayesian estimation methods for Frechet distribution.

An efficient and easy-to-implement recovery-based a posteriori error estimator for isogeometric analysis of the Stokes equation

In this article, we present an efficient recovery-based a posteriori error estimator for adaptive isogeometric analysis (IGA) of the Stokes equation, which is straightforward to implement. The increased regularity of splines in IGA versus Lagrange polynomials used in the classical finite element method plays a significant role for the success of the presented recovery technique. We consider LR B-splines (Johannessen et al., 2014) for the adaptive mesh refinement, and the isogeometric Taylor–Hood element, Sub-Grid element (Buffa et al., 2011), and Raviart–Thomas element (divergence-conforming) (Buffa et al., 2010; Evans and Hughes, 2013; Johannessen et al., 2015) for the mixed discretization of the Stokes equation. Three benchmark problems with analytical solutions are tested, and they demonstrate clearly the great performance of the recovery-based error estimator compared to classical residual-based error estimator.

Improvements in the estimation of the Weibull tail coefficient: A comparative study

The Weibull tail‐coefficient (WTC) plays a crucial role in extreme value statistics when dealing with Weibull‐type tails. Several distributions, such as normal, Gamma, Weibull, and logistic distributions, exhibit this type of tail behavior. The WTC, denoted by , is a parameter in a right‐tail function of the form , where represents a regularly varying cumulative hazard function with an index of regular variation equal to 1/ . The commonly used WTC‐estimators in literature are often defined as functions of the log‐excesses, making them closely related to estimators of the extreme value index (EVI) for Pareto‐type tails. For a positive EVI, the classical estimator is the Hill estimator. Generalized means have been successfully employed in estimating the EVI, leading to reduction of bias and of root mean square error for appropriate threshold values. In this study, we propose and investigate new classes of WTC‐estimators based on power of the log‐excesses within a second‐order framework. The performance of these new estimators is evaluated through a large‐scale Monte Carlo simulation study, comparing them with existing WTC‐estimators available in the literature.