Random Response Research Articles

Nonparametric model-assisted estimation from randomized response survey data

The randomized response technique offers an effective way for reducing potential bias resulting from nonresponse and untruthful responses when asking questions about sensitive behaviors or beliefs. The technique is also used for conducting statistical disclosure control of public use data files released by statistical agencies such as the U.S. Census Bureau. In both cases, the technique works by randomizing the actual survey responses using some known randomization model. In the case of asking sensitive survey questions, the randomization of responses is done by the survey respondents and only the randomized responses are collected, whereas in the case of disclosure control, the survey agency implements the randomization of responses after collecting the survey data and prior to releasing it for public use. This paper considers estimating the finite population mean from a survey where randomized responses are available for the study variable along with complete non-randomized auxiliary information. We define and study a class of nonparametric model-assisted estimators that make efficient use of the available auxiliary information and account for the complex survey design. The asymptotic properties of the proposed estimators are derived and a bootstrap variance estimator is presented. The finite sample performance of the proposed estimators is studied via extensive simulations accounting for a wide range of forms for the relationship between the study variable and auxiliary variable. The empirical results support the theoretical analyses and suggest that our proposed estimators are superior to existing estimators in most cases. Furthermore, the proposed methods are illustrated using real data from the 2015 U.S. consumer expenditure survey.

Customizable patterned membranes for cardiac tissue engineering: A model-assisted design method

Myocardial infarction can cause irreversible damage to the heart muscle, which can lead to heart failure. The difficulty of the treatment mainly arises from the anisotropic behavior of the myocardium fibrous structure. Patches or cardiac restraint devices appear to be a promising approach to post-infarction treatment. In this study, we propose a new model-assisted method to design patterned membranes. The proposed approach combines computer experiments and statistical models to optimize the design parameters and to meet the requirement for the post-infarction treatment. Finite element model, global sensitivity analysis, random forest model and response surface model are the key components of the strategy implemented in this study, which is applied to design a real membrane. The metamodel-based design method is able to estimate the equivalent Young’s modulus of the membrane in a few seconds and optimization results have been validated a posteriori by laboratory measurements. This solution opens up new prospects for the design of customized membranes with technical specifications tailored to each patient.

Analytical method for random seismic response of structures with tuned parallel inerter mass system and optimal parameters based on reliability

Tuned parallel inerter mass system (TPIMS) composed of tuned mass damper (TMD) and series parallel inerter-Ⅱ system has excellent damping performance. Earthquake action on structures has typical randomness, however the existing methods for outputs of structures with TPIMS only are numerical. In the work, an analytical method for calculating outputs and a globe method for optimizing TPIMS’s parameters was presented. Firstly, using dynamic finite element technique, the general dynamic equation of high-rise structures with TPIMS was established. Secondly, using quadratic decomposition method, the concise unified analytical solutions of zero-, first- and second-order spectral moments (ZFS-OSMs) of structural absolute and interlayer displacement, the inerter’s force and TMD’s displacement were deduced. Finally, three examples were carried out to verify the proposed method, to study influence of number of real vibration modes and to demonstrate the method for optimizing TPIMS’s parameters, respectively. The results show that the quantity of real vibration modes of uncontrolled structures with a participation weight of 100 % has the characteristics of both accuracy and efficiency in output analysis and the proposed optimal method is global.

Estimation of population variance using optional randomized response technique model in the presence of measurement errors

Measurement errors are an important consideration in sample surveys. Neglecting their influence can give misleading and inaccurate results. In this paper, we propose three variance estimators using Optional Randomized Response Technique (ORRT) in the presence of measurement error. Performance of the proposed estimators are evaluated through a simulation study and a numerical example.

Adjusting Self‐Protective and Non‐Response Behaviours in Sensitive Prevalence Estimation by a Two‐Stage Multilevel Randomised Response Technique

SummaryWhen using the customary direct questioning approach to collect sensitive information in a survey, some respondents may have no problem disclosing their true status or opinion, while others may be reluctant to reveal them. The problem of response bias is likely to arise as some responses tend to be more socially desirable than truthful. Hence, the indirect questioning approach can be used to guarantee privacy protection and reduce the influence of social desirability bias. This paper introduces a new technique which combines, both the direct and indirect questioning approaches and allows Bayesian estimates of the prevalence of multisensitive attributes to be obtained by taking into account the estimated proportion of honest respondents under direct questioning. Our proposal stems from a real survey in Taiwan and is illustrated with two motivating examples concerning voting behaviours and sexual identity. The empirical analysis reinforces that the proposed two‐stage multilevel method is satisfactory in mitigating the effects of respondents' self‐protective behaviour, and produces results that are more reliable than those based on the traditional direct questioning approach and better than a previous version of the multilevel randomised response method that ignores the presence of cheating behaviours in the direct questioning stage.

Improving a novel quantitative randomized response method using auxiliary variable information

In the majority of sample surveys, the variable of interest might have sensitive characteristics. Since we frequently get unrealistic responses from respondents during interviews as employing the direct method is not seen logical in these situations. To tackle this difficulty, the randomized response technique is good substitute for the direct method. The respondent is reassured that there would not be any issues will your response when using the randomized response technique as it protects privacy and secrets. In this paper, we propose a novel quantitative three-stage randomized response technique. Using simulation with R software, we prove that the results obtained from the proposed method are ideal. We suggest ratio and product type estimators by using the auxiliary information to enhance the efficiency of estimators. It is demonstrated that these estimators are superior for the conventional ones in special situations and in applied task, the use of auxiliary variable improves the estimation of the average cheating of Shahid Chamran university of Ahvaz students. The main objective is to increase the average sensitive reaction by utilizing the auxiliary variable.

Response and Reliability of Composite Post Insulators Under Random Earthquake

Abstract This article examines the stochastic response and seismic reliability of composite post-insulators under random seismic excitation. A random seismic motion model, tailored for power facility seismic research in China and compliant with the “Seismic Design Specification for Power Facilities” (GB50260-2013), is developed. This model accounts for both amplitude and frequency non-stationary. A nonlinear dynamic model for composite post insulators is then established, incorporating the nonlinearity at the multi-section insulator and flange connections. Numerical methods are employed for random seismic response analysis to address the non-stationary nature of seismic motion. The proposed model’s validity is confirmed by comparing it with the vibration table test results. Finally, the seismic reliability of composite post insulators, with root stress as the control criterion, is assessed using the probability density evolution method.

A forced optional randomized response model for estimating the proportion of sensitive attribute

The randomized response technique (RRT) is widely considered for estimating the sensitive attribute in sample surveys. Various randomized response methods have been formulated by the different researchers which provide suitable estimates and privacy protection to the respondents. In the present paper, a new randomized response technique is introduced for estimating a sensitive attribute by amalgamating forced answer strategy into optional RRT. The mean and variance of estimates of proportion of sensitive attribute and sensitivity level under proposed method are derived. The proposed procedure is found to be more efficient than the existing RRTs. The privacy of the respondents possessing the sensitive attribute is well protected under the proposed strategy. The superiority of the proposed procedure is established with the help of a numerical study.

An improved variant of quantitative randomized response techniques using forced responses

The randomized response technique has gained a wide popularity among survey researchers over the past few decades. Randomized response procedure is applicable in a variety of fields including sociology, psychology, business, and education, etc. It is a useful method which helps the researcher in dealing with the high non-response rates in sample surveys on sensitive issues such as monthly income, traffic rules violations, income tax amount, and expenditure on luxury items, etc. We introduce an optimal scrambling technique which uses forced responses. The algebraic properties of the estimator of population mean using the suggested technique have been derived. For the purpose of comparison with existing models, the different measures of model-evaluation have been obtained. Our analysis suggests that the new quantitative technique using forced responses is better than the available scrambling techniques not only in efficiency, but also in the combined metric of model’s efficiency and level of privacy, which make it more useful than the available models for practical problems. Using real-world example, we illustrate its practical implementation to sensitive sample surveys.

The Fusiongram: a periodic weak fault feature extraction strategy and its application in bearing fault diagnosis

Abstract The weak periodic transient impact responses caused by localized defects in rolling bearings are often obscured by complex interferences, such as white noise, random transient impact responses, and periodic responses from system operations. Meanwhile, the fault feature information contributing to damage detection may be distributed across different frequency bands in the vibration signal. Therefore, under the influence of complex interference, it is a challenging problem regarding how to 1) accurately select the frequency bands containing rich fault feature information, 2) effectively extract damage information from different frequency bands, and thereby, 3) successfully achieve fault diagnosis for machinery condition monitoring. To address these issues, this research introduces a novel signal processing strategy, termed as Fusiongram, for extracting weak periodic fault features amidst the influence of complex interferences. Firstly, the method of complementary hierarchical decomposition is proposed, in which the signal is decomposed into multiple components with overlapping frequency contents. Then, an index with interference resistance is constructed to select the components carrying rich damage feature information. Finally, the adaptive threshold denoising and multicomponent normalized averaging techniques are employed to fuse the information from the squared envelope spectra of the selected components, thus obtaining the reconstructed squared envelope spectra for fault diagnosis. The Fusiongram is able to achieve the goal of weak fault feature extraction from signals with complex interference. The analysis results of numerical simulation and experimental testing verify the effectiveness and advantages of the proposed strategy.

Deep deterministic policy gradient caching method for privacy protection in Internet of Vehicles

To address the problem of low cache hit ratio in edge nodes for privacy-preserving in the Internet of Vehicles (IoV), a deep deterministic policy gradient caching (DDPGC) method was proposed. First, a taxi certified by a trusted authority acted as a second-level caching edge node to acquire hotspot data and store it in the local cache. It then broadcasted this information to the neighboring service requesting vehicles (SRVs). SRVs cached the broadcasted data locally and search for service requests in the order of priority of local cache, taxi, and cloud server when such requests arise. Secondly, a neural network was deployed in taxis and SRVs to maximize the caching benefit through deep reinforcement learning for decision replacement of their cached data. Finally, when SRVs were located in vehicle sparsity and could not obtain request data from neighboring vehicles, a combination of k-anonymity and random response perturbation mechanisms generated anonymity sets to send requests to cloud servers in an anonymous manner to obtain services while protecting user location privacy. Simulation experimental results show that DDPGC can effectively improve the vehicle cache hit ratio, reduce the frequency of SRV interaction with the cloud server, and effectively protect user privacy security.

Fatigue Crack Propagation Life of Metallic Materials Under Random Loading: A Coupling Analysis Method in the Frequency Domain

ABSTRACTThis paper proposes an equivalent spectrum method to predict the fatigue crack propagation (FCP) life of metallic materials subjected to random loading. To adequately account for the coupling effects between crack propagation and the random response of structures, a coupling analysis model is introduced. The stress intensity factor (SIF) can be estimated based on the power spectral density (PSD) of an equivalent displacement. Random vibration fatigue tests were conducted to evaluate the proposed model on aluminum alloy specimens. Results indicate significant variations in natural frequency with crack length. The predicted results are compared with the experimental values, demonstrating satisfactory prediction accuracy of the proposed coupling analysis model. This model enables the assessment of coupling effects between crack length and random response, facilitating more precise predictions of FCP life in metallic materials and guiding the expanded application of damage tolerance criteria in structural engineering.

A perspective on conditional spectrum-based determination of response statistics of nonlinear systems

This work focuses on determining the stochastic response properties, in the frequency domain, of a general class of nonlinear systems with polynomial nonlinearities. Specifically, the results are presented in terms of the stationary power spectral densities of the system's displacement and velocity. This is pursued by revisiting the conditional power spectrum concept, with the assumption that the response process is both ergodic and pseudo-harmonic and characterized by an amplitude, and a phase. A theoretical elucidation of an existing formula for the conditional spectrum is attempted. In particular, this concept is interpreted in conjunction with the time averaging approximation made in the definition of the stationary probability density function of a response amplitude quantity, associated with the original nonlinear system. It is shown that a proper definition of the stationary probability density of the response amplitude, along with a reasonable treatment of the distribution over the frequency domain of the amplitude contribution, lead to an improved approximation of the stationary response power spectral density. The treatment involves the averaging of a population of surrogate spectral densities of stationary random responses conforming with the system responses associated with individual values of the amplitudes of the responses. The semi-analytical results have been quite favourably juxtaposed with a large suite of à propos Monte Carlo simulations, both in terms of the shape and of the range of the involved germane frequencies, even for strongly nonlinear systems.

A novel efficient randomized response model designed for attributes of utmost sensitivity

The existence of potential incomplete truthful reporting emerges when dealing with topics of great sensitivity. This paper introduces a novel, efficient randomized response model to address the challenges of untruthful reporting. It is meant to improve the accuracy of estimating highly sensitive attributes. The suggested model represents a modified version of Aboalkhair's model (2024) that was found to be an efficient substitute for the models developed by Warner and Mangat & Singh. This study examines the circumstances under which the suggested model outperforms alternative models in terms of efficiency. Theoretical and numerical comparisons of efficiency with alternative models, assuming incomplete truthful reporting, revealed the superior performance of the suggested model. Furthermore, a measure of privacy protection is computed.

Experimental Study of the Random Wave-Induced Hydrodynamics and Soil Response in a Porous Seabed Around Double Piles

The evaluation of the wave-induced pore pressures around the offshore piles has attracted great attentions among coastal engineers, because they have been commonly used as foundations of numerous marine infrastructures. This paper presents comparative studies of the random wave-induced transient seabed response around single and double piles in a sandy seabed through a series of wave flume experiments. The influences of relative spacing ratios, wave incidence angles, and front pile diameters under different random wave parameters on oscillatory pore pressures in the vicinity of double piles are examined. In addition, variations in wave profiles and dynamic wave pressures surrounding single and double piles are quantitatively analyzed. Based on the experimental results, the following conclusions can be drawn: (1) under the influence of random waves, the wave profiles around the double piles exhibit obvious irregularity and nonlinearity; (2) the shielding effect existing in the tandem piles results in lower dynamic wave pressures around the rear pile compared to the front pile; (3) the pore pressures on the front surface of the double piles decrease with increasing soil depth, with a decreasing attenuation rate at each layer; (4) when the relative spacing ratio G/D2=3, the group-pile effect weakens, leading to an increase in the pore pressures around the rear pile, approaching the results of a single pile under conditions of lower significant wave heights or periods; (5) the intense disturbance effect caused by large wave incidence angles exacerbates the pore pressure response around the double piles; (6) when the diameter of the front pile in the tandem piles increases, it enhances the shielding effect, thus suppressing the seabed response around the rear pile. In contrast, it causes an increase in the wave surface around the double piles, exacerbating the pore pressure response in the seabed. The latter effect becomes more pronounced when the significant wave height is larger.

Mean and sensitivity estimation using optional scrambled randomized response technique

In this paper, a new optional randomized response model has been proposed for estimating the population mean of a sensitive variable and sensitivity level in presence of scrambled response under simple random sampling. The properties of proposed model are described and verified its theoretical results with empirical analysis. Both efficiency and privacy measures are also explained and it is found that the proposed optional scrambled randomized response model outperform as compared to some existing additive and multiplicative models.

A class of infinite number of unbiased estimators using weighted squared distance for two-deck randomized response model

ABSTRACT We develop a collection of unbiased estimators for the proportion of a population bearing a sensitive characteristic using a randomized response technique with two decks of cards for any choice of weights. The efficiency of the estimator depends on the weights, and we demonstrate how to find an optimal choice. The coefficients of skewness and kurtosis are introduced. We support our findings with a simulation study that models a real survey dataset. We suggest that a careful choice of such weights can also lead to all estimates of proportion lying between [0, 1]. In addition, we illustrate the use of the estimators in a recent study that estimates the proportion of students, 18 years and over, who had returned to the campus and tested positive for COVID-19.

Optimization strategies for bifacial floating photovoltaic systems on different water bodies: A 10E performance evaluation

This study investigates the performance and optimization of standalone floating bifacial solar photovoltaic systems through a comprehensive 10E analysis, covering energy, exergy, economic, environmental, energo-economic, exergo-economic, enviro-economic, energo-environmental, exergo-environmental, energy payback time, and embodied energy factors. The research evaluates energy efficiency, economic feasibility, and environmental impact using various water coolants. Fresh water emerged as the best performer, delivering the highest output power (399 W), final yield (371.94 W), and performance ratio (59.082). Capacity utilization factors were comparable, with fresh water (0.517) and black water (0.516) leading. Fresh water also showed the lowest energy loss (−365.64) and highest exergy efficiency (32.10%). It achieved the lowest levelized cost of energy at 3.39 $/MWh and the highest enviro-economic parameter (243.497). The exergo-environmental analysis demonstrated consistent efficiency, with energy payback time ranging from 37.28 to 37.36, and the embodied energy for the panels was 2840.67 units. Optimization techniques such as random forest, gradient boosting, XGBoost gain, neural network, and response surface methodology were used to identify optimal panel heights, water depths, and azimuth angles. Fresh water was identified as the most effective coolant, balancing high energy efficiency, minimal energy loss, and environmental sustainability. Response surface methodology proved to be the most accurate method for predicting power output and bifacial gain. This study provides valuable insights into improving the efficiency and sustainability of floating bifacial photovoltaic systems under various environmental conditions.

Forced Response in Dichotomous Randomised Response Technique

In sample survey research when sensitive information such as illegal drug usage, rape, examination malpractices among students, induced abortion and the likes are to be obtained from individuals or respondents, false or no response is imminent. To correct this bias, this paper proposes a new randomized response model by incorporating the unrelated question in force-response technique. The unbiased estimate and variance of the proposed model were also obtained. The proposed randomized response model is then compared with the Alternative Unbiased Estimator in Unrelated Dichotomous Randomized Response Model, (AEUDRRT) and Alternative Unbiased Estimator in Dichotomous Randomized Response Model, (AEDRRT). The numerical investigation shows that the variance of the proposed model reduces while the V(AEUDRRT) and V(AEDRRT) increases as the sensitive issues increases. Hence, the proposed model has percentage relative efficiency (PRE) as high as 1195.45% and 1977.20% over AEUDRRT and AEDRRT, respectively. Also, the application of the proposed method revealed that it is practically more efficient in estimating the proportion of people involved in sensitive character (cyber threat) than the direct method of data collection. Therefore, the proposed technique is shown to be more efficient than conventional ones.

Green purchase behaviour of Arab millennials towards eco-friendly products: the moderating role of eco-labelling

Purpose This study aims to examine millennial consumers’ intentions towards purchasing eco-friendly products in the emerging Arab nation of Oman, focusing on the moderating role of eco-labelling (EL). Design/methodology/approach Environmental knowledge (EK) and environmental concern (EC) are integrated into the theory of planned behaviour (TPB) framework as independent variables, along with EL as a moderating variable. The data was collected through a self-administered survey and consisted of 705 random responses that was tabulated and analysed through the structural equation modelling technique. Findings ECs, subjective norms (SNs) and perceived behavioural control (PBC) significantly influence consumer attitudes towards green eco-friendly products. Consumers’ EK and attitudes positively influence the purchase intention (PI) to make green purchases. SNs and PBC substantially influence consumer attitudes towards adopting green products. However, the findings reveal a non-significant moderating influence of EL on the relationship between EC and PI for millennial Omani consumers. Research limitations/implications The findings are limited to only one Arab country, Oman, which limits the scope of the study. This study contributes to the TPB by using additional dimensions to examine the green purchasing behaviour of millennials. Practical implications Marketers can use these insights to develop strategies that emphasise the environmental benefits of products, leverage social influences and empower consumers to make environmentally conscious choices. Incorporating EL and green packaging can enhance the visibility and attractiveness of green products. Social implications Promoting environmental education, awareness campaigns and policy interventions that highlight the importance of eco-friendly product choices can foster more sustainable consumption patterns among Arab consumers. Originality/value To the best of the authors’ knowledge, this is the first study to examine the green PIs of millennials in an Arab context, specifically focusing on the moderating role of EL.