Estimating concealment behavior via direct questioning often fail. One proposed and effective solution to tackle this challenge is the Randomized Response Technique (RRT). This study aims to present a new efficient and easily applicable randomized response model as a tool for measuring concealment behavior. Efficiency examination and privacy protection of the proposed model are analyzed. As a real-world implementation of the model, the case of COVID-19 non-disclosure among university students is investigated as an example of concealment behavior. The proposed model, with a rational choice of parameters, was tested on a sample of university students and proved to be practically reliable. Health status disclosure ratio was estimated. This estimate serves as a foundation for predicting the concealment behavior in different fields.

This paper introduces a new class of probability distributions, termed the generated log exponentiated polynomial (GLEP) family, designed to enhance flexibility in modeling complex real financial data. The proposed family is constructed through a novel cumulative distribution function that combines logarithmic and exponentiated polynomial structures, allowing for rich distributional shapes and tail behaviors. We present comprehensive mathematical properties, including useful series expansions for the density, cumulative, and quantile functions, which facilitate the derivation of moments, generating functions, and order statistics. Characterization results based on the reverse hazard function and conditional expectations are established. The model parameters are estimated using various frequentist methods, including Maximum Likelihood Estimation (MLE), Cramer–von Mises (CVM), Anderson–Darling (ADE), Right Tail Anderson–Darling (RTADE), and Left Tail Anderson–Darling (LEADE), with a comparative simulation study assessing their performance. Risk analysis is conducted using actuarial key risk indicators (KRIs) such as Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), Tail Variance (TV), Tail Mean Variance (TMV), and excess function (EL), demonstrating the model’s applicability in financial and insurance contexts. The practical utility of the GLEP family is illustrated through applications to real and simulated datasets, including house price dynamics and insurance claim sizes. Peaks Over Random Threshold Value-at-Risk (PORT-VaR) analysis is applied to U.K. motor insurance claims and U.S. house prices datasets. Some recommendations are provided. Finally, a comparative study is presented to prove the superiority of the new family.

This research mainly concerns obtaining an accurate and efficient numerical solution for fractional nonlinear Drinfeld-Sokolov-Wilson system (DSW). The Dou

The key challenge in advancing randomized response techniques lies in enhancing efficiency while ensuring simplicity and ease of implementation. This research presents a fresh randomized response framework that achieves comparable effectiveness with fewer randomization devices, simplifying its real-world deployment. Compared to Aboalkhair’s (2025) model that depend on two randomization tools, the suggested method delivers equivalent efficiency with only a single tool. The study evaluates the new model’s superiority over existing approaches and establishes a measure of privacy protection. Through theoretical analysis and numerical comparisons, the results demonstrate distinct efficiency benefit of the suggested model.

When addressing highly sensitive topics, respondents may provide incomplete or untruthful disclosures, compromising data accuracy. To mitigate this issue, this study introduces an innovative and efficient randomized response framework designed to enhance the estimation of highly sensitive attributes. The proposed model refines Aboalkhair’s (2025) framework, which has been established as an effective alternative to Warner’s and Mangat’s models. This study evaluates the conditions under which the new model achieves greater efficiency than existing approaches. Through theoretical analysis and numerical simulations—accounting for partial truthful reporting—the results demonstrate the model’s superior efficiency. Additionally, the paper quantifies the privacy protection level afforded by the new approach.

In this study, we examine the presence of bias in weighted least squares (WLS) estimation within the context of first-order bifurcating autoregressive (BAR(1)) models. These models are widely used in the analysis of binary tree-structured data, particularly in cell lineage research. Our findings suggest that WLS estimators may exhibit significant and problematic biases, especially in finite samples. The magnitude and direction of this bias are influenced by both the autoregressive parameter and the correlation structure of the model errors. To address this issue, we propose two bootstrap-based methods for bias correction of the WLS estimator. The paper further introduces shrinkage-based versions of both single and fast double bootstrap bias correction techniques, designed to mitigate the over-correction and under-correction issues that may arise with traditional bootstrap methods, particularly in larger samples. Comprehensive simulation studies were conducted to evaluate the performance of the proposed bias-corrected estimators. The results show that the proposed corrections substantially reduce bias, with the most notable improvements observed at extreme values of the autoregressive parameter. Moreover, the study provides practical guidance for practitioners on method selection under varying conditions.