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Mediation analysis empirically investigates the process underlying the effect of an experimental manipulation on a dependent variable of interest. In the simplest mediation setting, the experimental treatment can affect the dependent variable through the mediator (indirect effect) and/or directly (direct effect). However, what appears to be an indirect effect in standard mediation analysis may reflect a data-generating process without mediation, including the possibility of a reversed causal ordering of measured variables, regardless of the statistical properties of the estimate. To overcome this indeterminacy where possible, the authors develop the insight that a statistically reliable total effect, combined with strong evidence for conditional independence of the treatment and the outcome given the mediator, is unequivocal evidence for mediation as the underlying causal model into an operational procedure. This is particularly helpful when theory is insufficient to definitely causally order measured variables, or when the dependent variable is measured before what is believed to be the mediator. The procedure combines Bayes factors as principled measures of the degree of support for conditional independence, with latent variable modeling to account for measurement error and discretization in a fully Bayesian framework. The authors reanalyze a set of published mediation studies to illustrate how their approach facilitates stronger conclusions. [ABSTRACT FROM AUTHOR]

The cluster-weighted model (CWM) is a model-based clustering approach that utilizes a mixture of regression models to cluster data points based on both a response variable Y and covariates , where the covariates are assumed to be random. The Gaussian CWM (GCWM) is the most commonly used member of the CWM family, where the Gaussian distribution is adopted for both the covariates and the response given the covariates. In mixture of regression, assignment of data points to the clusters is based on the conditional distribution of the response variable given covariates and is independent of the covariates' distribution. In CWM, to increase clustering performance, the covariates' distribution is also used to assign data points to the clusters. Existing researches on CWMs are limited to the directly observed covariates, which may not reflect real-world scenarios where measurement errors (MEs) occur. The measurement error can lead to inconsistent estimates, consequently, produce spurious or obscure clusters. In this article, we assume that random covariates are latent, observed with an independent ME that has the Gaussian distribution. A new generalized expectation maximization algorithm is defined for estimating model parameters. The performance of the proposal is illustrated and compared with the GCWM using both simulated and real data. [ABSTRACT FROM AUTHOR]

In this paper, we study the complete convergence and complete moment convergence for randomly weighted sums of arrays of rowwise widely negative dependent random variables in sub-linear expectation space under some appropriate conditions, which extend and improve the corresponding ones in classical probability space to the case of sub-linear expectation space. And we obtain a strong law of large numbers for the randomly weighted sums of arrays of rowwise widely negative dependent random variables. As applications of our main results, we not only present a result on the complete consistency for the weighted estimator in a nonparametric regression model, but also obtain the complete consistency for the least squares estimators in errors-in-variables regression models based on widely negative dependent errors under sub-linear expectations. We perform some numerical simulations to verify the validity of the theoretical results and a real example is analysed for illustration. [ABSTRACT FROM AUTHOR]

In this article, the complete f-moment convergence for m-asymptotic negatively associated random variables is investigated. As applications, we establish the strong consistency of the least square estimator in the simple linear errors-in-variables models and the complete consistency for estimator in the semiparametric regression model based on m-asymptotic negatively associated errors. We also give some simulations to assess the finite sample performance of the theoretical results. [ABSTRACT FROM AUTHOR]

Order‐restricted hypothesis testing problems frequently arise in practice, including studies involving regression models for longitudinal data. These tests are known to be more powerful than tests that ignore such restrictions. In this article, we consider order‐restricted tests for nonlinear mixed‐effects models with measurement errors in time‐dependent covariates. We propose to use a multiple imputation method to address measurement errors, since this approach allows us to use existing complete‐data methods for order‐restricted tests. Some theoretical results are presented. We evaluate our proposed methods via simulation studies that demonstrate they are more powerful than either a competing naive method or a two‐step approach to testing hypotheses. We illustrate the use of our proposed approach by analyzing data from an HIV/AIDS study. [ABSTRACT FROM AUTHOR]

The precision of process monitoring often encounters challenges in determining the exact shift size. Therefore, combined control charts have gained considerable attention because of their excellent speed to detect simultaneously small-to-moderate and large-size shifts. The effectiveness of the applied quality control methods strongly depends on the performance of the measurement system. Measurement error presence contributes significantly negatively toward the performance of the usual control charting schemes. This article proposes novel two-sided combined Shewhart-Cumulative EWMA-sum (Shewhart-CUESUM) control charts designed to efficiently monitor the mean of normally distributed processes. In addition, to address measurement errors, the M-Shewhart-CUESUM chart is proposed, incorporating an additive measurement error model. Evaluation of the charts through Monte-Carlo simulations, considering metrics such as average run length (ARL), extra quadratic loss, relative ARL, and performance comparison index. It is found that the combined Shewhart-CUESUM outperforms than CUESUM chart. The results show that the presence of measurement errors can significantly diminish the charts' performance, which can be mitigated by utilizing a multiple measurements scheme. Among the different well-established combined charts examined, the M-Shewhart-CUESUM chart shows considerably more sensitive to detecting simultaneously detect small and large size shifts. To employ simulated datasets to illustrate the impact of measurement errors and demonstrate the implications of the proposed charts on process mean shifts. [ABSTRACT FROM AUTHOR]

Consider a situation where one is interested in estimating the density of a survival time that is subject to random right censoring and measurement errors. This happens often in practice, like in public health (pregnancy length), medicine (duration of infection), ecology (duration of forest fire), among others. We assume a classical additive measurement error model with Gaussian noise and unknown error variance and a random right censoring scheme. Under this setup, we develop minimal conditions under which the assumed model is identifiable when no auxiliary variables or validation data are available, and we offer a flexible estimation strategy using Laguerre polynomials for the estimation of the error variance and the density of the survival time. The asymptotic normality of the proposed estimators is established, and the numerical performance of the methodology is investigated on both simulated and real data on gestational age. [ABSTRACT FROM AUTHOR]

AbstractMeasurement 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. [ABSTRACT FROM AUTHOR]

This note discusses the radical technical inflation in error variance and the related standard error of test scores from both conceptual and empirical viewpoints. This technical inflation arises as a direct consequence of the technical underestimation of item-score correlation by the product–moment coefficient of correlation (PMC), which is embedded in the traditional estimators of reliability such as coefficients alpha, theta, omega, or rho (maximal reliability). Specifically, in educational settings where compilations usually include both easy and difficult items, the estimate by PMC may substantially deviate from the true association between an item and the score. Consequently, the use of traditional estimators of reliability leads to technically inflated estimates of standard errors, as the error variance related to these traditional measurement models is significantly inflated, resulting in deflated reliability estimates. In educational testing, employing deflation-corrected standard errors, calculated using deflation-corrected reliability estimators, would provide a more accurate measure of the test score's true precision. [ABSTRACT FROM AUTHOR]

In this article, some general results on the rates of strong consistency for the least squares estimators and weighted least squares estimators in heteroscedastic partially linear errors-in-variables model based on widely orthant dependent random errors are presented. The results can deduce strong consistency and convergence rates under some mild conditions. Some numerical simulations are also provided to support the theoretical results. [ABSTRACT FROM AUTHOR]

Household surveys on income might suffer from quality limitations mainly due to the difficulty of enrolling households (unit nonresponse) and retrieving correct information during the interview (measurement error [ME]). These errors are likely to be correlated because of latent factors, such as the threat of disclosing personal information, the perceived sensitivity of the topic, or social desirability. For survey organizations, assessing the interplay of these errors and their impact on the accuracy and precision of inferences derived from their data is crucial. In this article, we propose to use a standard sample selection model within a total survey error framework to deal with the case of correlated nonresponse error (NR) and ME in estimating average household income. We use it to study the correlation between the two errors, quantify the ME component due to this correlation, and evaluate ME among nonrespondents. Using the Italian Survey on Income and Wealth linked with administrative income data from tax returns, we find a positive correlation between the two errors and that households at the extremes of the income distribution mainly cause this association. Our results show that ME contributes more to the total error than unit nonresponse and that it would be larger in absence of the correlation between the two errors. Finally, efforts to reduce nonresponse rates are worthwhile only for nonrespondents in the lowest estimated response propensity group. If these households participate, the bias decreases because of the reduction in NR that offsets the increase in ME. [ABSTRACT FROM AUTHOR]

The problem of measurement error in predictors has received a vast amount of attention for both linear and non-linear models. The major focus has generally been on inference for regression coefficients, while residual analysis, variance estimation and heteroscedasticity assessment have received limited attention. In this paper we propose and evaluate new methods, based on reconstructed predictors, to address these problems in the context of linear regression. Methods using moment reconstructed predictors are found to perform well, including a new model-based bootstrapping technique for testing heteroscedasticity. Motivated by an example where the predictor is methylation rate and the measurement error model is based on the binomial distribution, particular attention is given to being able to handle the situation where both the measurement error variances and the variance of the error in the equation may be changing as a quadratic function of the unobserved predictor. [ABSTRACT FROM AUTHOR]

Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and without measurement error): IV with post-instrument covariates, IV without post-instrument covariates, and ordinary least squares. In large samples and when the model provides a reasonable approximation, these formulas sometimes allow the analyst to bracket the parameter of interest with two estimators and allow the analyst to choose the estimator with the least asymptotic bias. We illustrate these points with a discussion of the settler mortality IV used by Acemoglu, Johnson, and Robinson. [ABSTRACT FROM AUTHOR]

As an adaptive finite impulse response filtering algorithm, the maximum total correntropy (MTC) algorithm plays an important role in parameter estimation of the errors-in-variables model where both input and output signals are contaminated with impulsive noises. However, the MTC algorithm is difficult to obtain a sufficiently high estimation accuracy under impulsive noises because the MTC cost function contains second-order moments of the error signal and its first-order gradient is susceptible to large outliers in the input noise. In this paper, a maximum total fractional-order correntropy (MTFOC) cost function is proposed and then a fractional-order gradient based MTFOC adaptive filtering algorithm is developed to improve the estimation accuracy of MTC. Moreover, the local stability and computational complexity of the proposed algorithm are analyzed. Simulation results indicate that the estimation accuracy and robustness of the MTFOC algorithm are superior to previous algorithms in both Gaussian mixture noise environments and α -stable distribution noise environments. [ABSTRACT FROM AUTHOR]

In psychological studies, multivariate outcomes measured on the same individuals are often encountered. Effects originating from these outcomes are consequently dependent. Multivariate meta‐analysis examines the relationships of multivariate outcomes by estimating the mean effects and their variance–covariance matrices from series of primary studies. In this paper we discuss a unified modelling framework for multivariate meta‐analysis that also incorporates measurement error corrections. We focus on two types of effect sizes, standardized mean differences (d) and correlations (r), that are common in psychological studies. Using generalized least squares estimation, we outline estimated mean vectors and variance–covariance matrices for d and r that are corrected for measurement error. Given the burgeoning research involving multivariate outcomes, and the largely overlooked ramifications of measurement error, we advocate addressing measurement error while conducting multivariate meta‐analysis to enhance the replicability of psychological research. [ABSTRACT FROM AUTHOR]

In this study we introduce an innovative mathematical and statistical framework for the analysis of motion energy dynamics in psychotherapy sessions. Our method combines motion energy dynamics with coupled linear ordinary differential equations and a measurement error model, contributing new clinical parameters to enhance psychotherapy research. Our approach transforms raw motion energy data into an interpretable account of therapist–patient interactions, providing novel insights into the dynamics of these interactions. A key aspect of our framework is the development of a new measure of synchrony between the motion energies of therapists and patients, which holds significant clinical and theoretical value in psychotherapy. The practical applicability and effectiveness of our modelling and estimation framework are demonstrated through the analysis of real session data. This work advances the quantitative analysis of motion dynamics in psychotherapy, offering important implications for future research and therapeutic practice. [ABSTRACT FROM AUTHOR]

Based on the Bayesian framework, a time domain method is proposed for the uncertain quantification of structural modal identification. First, a theoretical prediction model is constructed from the state space model in modal space and then transformed into physical space using the modal basis. Second, taking into account the uncertainty of the identification results caused by measurement noise and modeling errors, the negative log-likelihood function is constructed using time domain measurement data and a theoretical prediction model based on the Bayesian system identification framework. Finally, an unconstrained quadratic function for the identification parameters is derived through matrix vectorization, and, by mathematically transforming the optimization problem, only the dynamic spectral parameters (the natural frequencies and damping ratios) need to be identified, while the spatial parameters (the mode shapes and modal contribution factors) can be analytically calculated from the spectral parameters, which greatly reduces the dimensionality of the identification parameters. In numerical examples, the identification of the modal parameters for a spring–mass system and high-speed pantograph was studied, and the identified modal parameters based on the simulation response's data were in good agreement with the theoretical values. Moreover, the modal parameters of the actual structure of the pantograph were identified based on the experimental data, and the identifying uncertainties were quantified by the coefficient of variation. [ABSTRACT FROM AUTHOR]

There are several advantages to the MIG (Metal Inert Gas) process, which explains its increased use in various welding sectors, such as automotive, marine, and construction. A variant of the MIG process, where the same equipment is employed except for the deposition of a thin layer of flux before the welding operation, is the AMIG (Activated Metal Inert Gas) technique. This study focuses on investigating the impact of physical properties of individual metallic oxide fluxes for 304L stainless steel welding joint morphology and to what extent it can help determine a relationship among weld depth penetration, the aspect ratio, and the input physical properties of the oxides. Five types of oxides, TiO2, SiO2, Fe2O3, Cr2O3, and Mn2O3, are tested on butt joint design without preparation of the edges. A robust algorithm based on the particle swarm optimization (PSO) technique is applied to optimally tune the models' parameters, such as the quadratic error between the actual outputs (depth and aspect ratio), and the error estimated by the models' outputs is minimized. The results showed that the proposed PSO model is first and foremost robust against uncertainties in measurement devices and modeling errors, and second, that it is capable of accurately representing and quantifying the weld depth penetration and the weld aspect ratio to the oxides' thermal properties. [ABSTRACT FROM AUTHOR]

Errors-in-variables (EIV) biases plague asset pricing tests. We offer a new perspective on addressing the EIV issue: instead of viewing EIV biases as estimation errors that potentially contaminate next stage risk premium estimates, we consider them to be return innovations that follow a particular correlation structure. We factor this structure into our test design, yielding a new regression model that generates the most accurate risk premium estimates. We demonstrate the theoretical appeal as well as the empirical relevance of our new estimator. This paper was accepted by Victoria Ivashina, finance. Supplemental Material: The supplemental appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4966. [ABSTRACT FROM AUTHOR]

The economics of obesity literature implicitly assumes that measured anthropometrics are error-free and they are often treated as a gold standard when compared to self-reported data. We use factor mixture models to analyse measurement error in both self-reported and measured anthropometrics with nationally representative data from the 2013 National Health Survey in Brazil. A small but statistically significant fraction of measured anthropometrics are attributed to recording errors, while, as they are imprecisely recorded and due to reporting behaviour, only between 10 and 23% of our self-reported anthropometrics are free from any measurement error. Post-estimation analysis allows us to calculate hybrid anthropometric predictions that best approximate the true body weight and height distribution. BMI distributions based on the hybrid measures do not differ between our factor mixture models, with and without covariates, and are generally close to those based on measured data, while BMI based on self-reported data under-estimates the true BMI distribution. "Corrected self-reported BMI" measures, based on common methods to mitigate reporting error in self-reports using predictions from corrective equations, do not seem to be a good alternative to our "hybrid" BMI measures. Analysis of regression models for the association between BMI and health care utilization shows only small differences, concentrated at the far-right tails of the BMI distribution, when they are based on our hybrid measure as opposed to measured BMI. However, more pronounced differences are observed, at the lower and higher tails of BMI, when these are compared to self-reported or "corrected self-reported" BMI. [ABSTRACT FROM AUTHOR]

In this article, we have chosen to explore the performance of the piezoresistive accelerometer, favoured for its comparative advantages over other accelerometers. Our approach includes modelling this accelerometer according to the law of motion and validating the model through simulation tests and experimental trials. With this approach, we can determine the optimal damping rate and frequency range for the piezoresistive accelerometer, the optimisation of these parameters is crucial to maximise the performance of the accelerometer in various operating conditions, ensuring high sensitivity and fast response while minimising distortions or errors in the collected data. These optimal values of damping rate and frequency range guarantee precise and reliable accelerometer measurements. This paper provides a comprehensive overview of sensor design principles and signal processing methods from a theoretical standpoint. Beginning with sensor design, we delve into the foundational aspects of transduction principles, emphasising the conversion of physical quantities into electrical signals. The selection and optimisation of sensing elements are explored, highlighting material properties, geometrical considerations, and packaging strategies to ensure sensitivity, linearity, and robustness. [ABSTRACT FROM AUTHOR]

The conventional practices in handling cross-sectional data treated the explanatory variables as fixed variables without measurement errors. This article proposed a novel autocorrelated unreplicated linear functional relationship model (AULFR) model to accommodate the autocorrelated errors in the measurement errors model. Some basic properties of the model have been derived. A modified backshift operator is used in transforming the autocorrelated error into uncorrelated error. Simulation studies show that AULFR outperforms other benchmarking models even with a relatively small training data percentage or a sample size of 100 training observations. The application of the AULFR model on an actual economic case shows consistent results with simulation studies. The advantages of the proposed model are (i) it models the relationship between a time-based dependent variable and a set of time-based explanatory variables which subjected to measurement errors, and (ii) it can predict the current or future values of both dependent and explanatory variables using historical data. [ABSTRACT FROM AUTHOR]

The Model for Prediction Across Scales-Atmosphere (MPAS) was used to simulate meteorological conditions for a two-week winter episode during 10–23 January 2013, and a two-week summer episode during 18–31 July 2016, using both as a global model and a regional model with a focus on California. The results of both global and regional applications of MPAS were compared against the surface and upper air rawinsonde observations while the variations of characteristic meteorological variables and modeling errors were evaluated in space, time, and statistical sense. The results of the Advanced Weather Research and Forecast (WRF-ARW, hereafter WRF) model simulations for the same episodes were also used to evaluate the results of both applications of MPAS. The temporal analyses performed at surface stations indicate that both global and regional applications of MPAS and WRF model predict the diurnal evolution of characteristic meteorological parameters reasonably well in both winter and summer episodes studied here. The average diurnal bias in predicting 2 m temperature by MPAS and WRF are about the same with a maximum of 2 °C in winter and 1 °C in summer while that of 2 m mixing ratio is within 1 g/kg for all three modeling applications. The rawinsonde profiles of temperature, dew point temperature, and wind direction agree reasonably well with observations while wind speed is underestimated by all three applications. The comparisons of the spatial distribution of anomaly correlation and mean bias errors calculated from each model results for 2 m temperature, 2 m water vapor mixing ratio, 10 m wind speed and wind direction indicate that all three models have similar magnitudes of agreement with observations as well as errors away from observations throughout California. [ABSTRACT FROM AUTHOR]

CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present considerable statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens—"thresholded regression"—exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV ("GLM-based errors-in-variables"), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g. Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several new insights. [ABSTRACT FROM AUTHOR]

In undeclared work research, the estimation of the magnitude of the phenomenon (i.e., the amount of income and/or the percentage of workers involved) is of major interest. This has been done either using indirect methods or by means of ad hoc surveys such as the Eurobarometer special survey on undeclared work, our motivating study. The extent of undeclared work can be measured by means of two different outcomes: the event of working off-the-book (binary variable) and, when the event occurs, the amount of earnings deriving from the undeclared activity (continuous variable). This setup has been typically modeled via the so called two-part model: a binary choice model for the probability of observing a positive-versus-zero outcome and then, conditional on a positive outcome, a regression model for the positive outcome. We propose an extension of the two-part model that goes in two directions. The first regards the measurement error that, given the very nature of undeclared activities, is most likely to affect both the outcomes of interest. The second is that we generalize the linear regression part of the model to allow individual-level means. We also conduct an extensive simulation study to investigate the performance of the proposed model and compare it with traditional approaches. [ABSTRACT FROM AUTHOR]

Cognitive decline represents a severe non‐motor symptom of Parkinson's disease (PD) that can significantly reduce the benefits of subthalamic deep brain stimulation (STN DBS). Here, we aimed to describe post‐surgery cognitive decline and identify pre‐surgery cognitive profile associated with faster decline in STN DBS‐treated PD patients. A retrospective observational study of 126 PD patients treated by STN DBS combined with oral dopaminergic therapy followed for 3.54 years on average (SD = 2.32) with repeated assessments of cognition was conducted. Pre‐surgery cognitive profile was obtained via a comprehensive neuropsychological examination and data analysed using exploratory factor analysis and Bayesian generalized linear mixed models. On the whole, we observed a mild annual cognitive decline of 0.90 points from a total of 144 points in the Mattis Dementia Rating Scale (95% posterior probability interval [−1.19, −0.62]) with high inter‐individual variability. However, true score changes did not reach previously reported reliable change cut‐offs. Executive deficit was the only pre‐surgery cognitive variable to reliably predict the rate of post‐surgery cognitive decline. On the other hand, exploratory analysis of electrode localization did not yield any statistically clear results. Overall, our data and models imply mild gradual average annual post‐surgery cognitive decline with high inter‐individual variability in STN DBS‐treated PD patients. Nonetheless, patients with worse long‐term cognitive prognosis can be reliably identified via pre‐surgery examination of executive functions. To further increase the utility of our results, we demonstrate how our models can help with disentangling true score changes from measurement error in future studies of post‐surgery cognitive changes. [ABSTRACT FROM AUTHOR]

While the practical application of adaptive filters has indeed garnered substantial attention, two pressing issues persist that have a profound impact on their performance—system sparsity and the presence of contaminated Gaussian impulsive noise. In this research paper, we propose a novel approach to tackle both of these issues simultaneously by introducing the concept of a proportionate matrix. Specifically, we present a proportionate maximum total complex correntropy algorithm based on the errors-in-variables model. The paper presents a theoretical analysis of the steady-state weight error power under the influence of impulsive noise. Furthermore, it discusses the performance comparison in system identification and highlights the robustness of the proposed algorithm. To validate its effectiveness, a simulation involving stereophonic acoustic echo cancellation is conducted, and the results confirm the clear advantages of the proposed Proportionate Maximum Total Complex Correntropy algorithm. [ABSTRACT FROM AUTHOR]

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167–90, 2004a, Journal of Econometrics, 128(2), 301–23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78–103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena. [ABSTRACT FROM AUTHOR]

In the auction literature, it is well established that bidders' asymmetry plays an important role in determining auction revenues. In this article, we propose a nonparametric methodology to analyze first-price auctions with two popularly adopted asymmetries: heterogeneous risk preferences and asymmetric value distributions. We find that the two competing models provide distinct implications for the bid distributions conditional on heterogeneity. By modeling bidders' asymmetry as unobserved heterogeneity, we show that the conditional bid distributions are identified nonparametrically. These results enable researchers to test the two competing models. In an application using the US Forest Service timber auction data, we find that the data supports the model with heterogeneity in risk preference. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, we investigate the functional partially linear single index models with contaminated functional and real covariates when the functional sample grid is dense. To eliminate the influence of measurement error on the estimations of the proposed models, a two-stage estimation procedure is introduced. In the first stage, we utilize a kernel presmoothing regression to denoise the corrupted functional curves. In the second stage, the fitted functional curves are applied to replace the original ones to obtain the predictor of unknown parameter in parametric component with the attenuation correction method and then we derive the predictor of unknown link function in nonparametric part with kernel method. Meanwhile, in order to improve computational efficiency, functional slice inverse regression method is employed to estimate the functional single index parameter. The asymptotic properties are also stated in this process at the situation of independent and identical distribution sample under some mild conditions. A simulation study and real data analysis are further explored to illustrate the good performance of our methodology. [ABSTRACT FROM AUTHOR]

To study the symmetry and asymmetry of the model error under multiplicative distortion measurement errors setting, we propose a correlation coefficient-based measure between the distribution function and the root of density function. The unknown distribution function and density function are estimated from four kinds of residuals: the conditional mean calibration-based residuals, the conditional absolute mean calibration-based residuals, the conditional variance calibration-based residuals, and the conditional absolute logarithmic calibration-based residuals. We study the asymptotic results of the estimators of correlation coefficient-based measure under four calibrations. Next, we consider statistical inference of the correlation coefficient-based measure by using the empirical likelihood method. The empirical likelihood statistics are shown to be an asymptotically standard chi-squared distribution. Simulation studies demonstrate the performance of the proposed estimators and test statistics. A real example is analyzed to illustrate its practical usage. [ABSTRACT FROM AUTHOR]

The analysis of the subsidence cone at Salina Ocna Dej involved modern measurement techniques, including drones, to evaluate terrain changes and generate a detailed 3D model. Data collection occurred in two stages, in 2021 and 2022, utilizing drones to capture a large number of high-resolution images (5472x3648 pixels) [1], resulting in a significant volume of data. These images were processed using specialized software to create a 3D model, employing advanced alignment, data fusion, and interpolation techniques. The results demonstrated that using drones offers considerable benefits over traditional methods, including increased accuracy and reduced time and resource consumption [2], with minimal errors recorded at just 0.7 mm. The project emphasized the importance of adapting initial plans to field conditions and considering weather forecasts to prevent accidents. Post-processing the data enabled clear delineation of the subsidence contour and slope angles, facilitating their integration into the analysis of other mining activities in the region. The generated 3D model serves as a reference for monitoring subsidence evolution and assessing risks to nearby residences. Continuous measurement and constant monitoring of the subsidence cone are essential to prevent potential future damage caused by slope instability and erosion. [ABSTRACT FROM AUTHOR]

This paper derives a general Bartlett correction formula to improve the inference based on the likelihood ratio test in a multivariate model under a quite general parameterization, where the mean vector and the variance-covariance matrix can share the same vector of parameters. This approach includes a number of models as special cases such as non-linear regression models, errors-in-variables models, mixed-effects models with non-linear fixed effects, and mixtures of the previous models. We also employ the Skovgaard adjustment to the likelihood ratio statistic in this class of multivariate models and derive a general expression of the correction factor based on Skovgaard approach. Monte Carlo simulation experiments are carried out to verify the performance of the improved tests, and the numerical results confirm that the modified tests are more reliable than the usual likelihood ratio test. Applications to real data are also presented for illustrative purposes. [ABSTRACT FROM AUTHOR]

The presence of measurement errors in data and multicollinearity among the explanatory variables have negative effects on the estimation of regression coefficients. Within this respect, the motivation of this article is to examine measurement errors and multicollinearity problems simultaneously. In this paper, by utilizing three different forms of corrected score functions three consistent ridge regression estimators are proposed. Theoretical comparisons of these new estimators are examined by implementing the mean squared error criterion. Large sample properties of these estimators are investigated without assuming any distributional assumption. Two numerical examples are presented using real data sets and also a simulation study is performed. The findings indicate that the newly proposed three estimators outperform the existing estimators by the criterion of mean squared error. [ABSTRACT FROM AUTHOR]

Partially linear models are useful tools to analyze data from economic, genetic, and other fields. Similar to other data analyses, the identification of influential observations that may be potential outliers is an important step beyond estimation in such models. The objective of this article is to develop some diagnostic measures for identifying influential observations in partially linear models when some of the covariates are measured with errors. Deletion measures are developed based on case deletion, mean shift outlier models, and the corrected likelihood of Nakamura (1990). The performance of the methods is illustrated by an artificial example and a real example. [ABSTRACT FROM AUTHOR]

Item response theory (IRT) analyses are often used to evaluate measurement error in educational and psychological test instruments. In such contexts, the latent traits/proficiencies are typically assumed normally distributed and a cumulative normal/logistic measurement link function is applied. Such choices are consistent with constructs that are viewed as bipolar in nature and play a critical role in defining the latent proficiency metric against which the measurement error in the test is evaluated. Recently, alternative models that portray the construct as unipolar have been highlighted as being more appropriate for certain psychopathology and personality constructs. In this paper we extend consideration of unipolar IRT models for a recognition task measure, using several example datasets from various versions of the Author Recognition Test (ART), a measure of print exposure. We show how the decision between unipolar versus bipolar IRT modeling has substantial implications for the quantification and interpretation of measurement error in the ART. In sharp contrast to prior bipolar IRT analyses of the ART, under unipolar IRT measurement error in the ART is minimized at low levels of latent print exposure, and increases as latent print exposure increases. Implications for consideration of unipolar IRT with other constructs and measures (e.g., vocabulary, specialized forms of knowledge) that reflect a similar type of response process are considered in the discussion. [ABSTRACT FROM AUTHOR]

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Regularized regression methods have attracted much attention in the literature, mainly due to its application in high-dimensional variable selection problems. Most existing regularization methods assume that the predictors are directly observed and precisely measured. It is well known that in a low-dimensional regression model if some covariates are measured with error, then the naive estimators that ignore the measurement error are biased and inconsistent. However, the impact of measurement error in regularized estimation procedures is not clear. For example, it is known that the ordinary least squares estimate of the regression coefficient in a linear model is attenuated towards zero and, on the other hand, the variance of the observed surrogate predictor is inflated. Therefore, it is unclear how the interaction of these two factors affects the selection outcome. To correct for the measurement error effects, some researchers assume that the measurement error covariance matrix is known or can be estimated using external data. In this paper, we propose the regularized instrumental variable method for generalized linear measurement error models. We show that the proposed approach yields a consistent variable selection procedure and root-n consistent parameter estimators. Extensive finite sample simulation studies show that the proposed method performs satisfactorily in both linear and generalized linear models. A real data example is provided to further demonstrate the usage of the method. [ABSTRACT FROM AUTHOR]

The topography measurement accuracy of coherence scanning interferometry (CSI) suffers from the local characteristic of micro-structured surfaces, such as local surface slopes. A cylindrical reference artefact made of single-mode fiber with high roundness and low roughness has been proposed in this manuscript to traceably investigate the surface tilting induced measurement deviations using coherence scanning interferometry with high NA objectives. A feed-forward neural network (FF-NN) is designed and trained to model and thereafter compensate the systematic measurement deviations due to local surface tilting. Experimental results have verified that the FF-NN approach can well enhance the accuracy of the CSI for radius measurement of cylindrical samples up to 0.3%. Further development of the FF-NN for modelling of the measurement errors in CSI due to the optical properties of surfaces including areal roughness is outlined. [ABSTRACT FROM AUTHOR]

This paper explores the estimators of parameters for a partially linear single-index spatial model which has measurement errors in all variables. We propose an efficient methodology to estimate our model by combining a local-linear smoother based Pseudo- θ algorithm, simulation-extrapolation (SIMEX) algorithm, the estimation equation and the estimation method for profile maximum likelihood. Under some regular conditions, we derive the asymptotic properties of the link function and unknown estimators. Some simulations indicate our estimation method performs well. Finally, we apply our method to a real data set of Boston Housing Price. The result shows that our model fits the data set well. [ABSTRACT FROM AUTHOR]

Small-modulus gears, which are essential for motion transmission in precision instruments, present a measurement challenge due to their minuscule gear gaps. A high-precision measurement method under the influence of positioning errors is proposed, enabling precise evaluation of the machining quality of small-modulus gears. Firstly, a compound measurement platform for small-modulus gears is developed. Using a 3D model of the measurement system, the mathematical relationships governing motion transmission between various components are analyzed. Secondly, the formation mechanism of gear positioning error is revealed and its important influence on measurement accuracy is discussed. An optimization method for spatial coordinate transformation matrices under positioning errors of gears is proposed. Thirdly, the study focuses on small-sized gears with a modulus of 0.1 mm and a six-level accuracy. Based on the aforementioned measurement system, the tooth profile measurement points are collected in the actual workpiece coordinate system. Then, gear error parameters are extracted based on the established models for tooth profile deviation and pitch deviation. Finally, the accuracy and effectiveness of the proposed measurement method are verified by comparing the measurement results of the P26 gear measuring center. [ABSTRACT FROM AUTHOR]

This paper presents a new method for correction of dynamic errors occurring when measuring flat surfaces in the presence of mechanical effects. Mechanical effects cause inertial forces and moments that affect the moving components of measuring instruments, thereby causing dynamic errors. The study proposes a mathematical model, on the basis of which algorithms for correction of dynamic errors can be developed. The basic concept of the model is based on determining the optimal estimate in the current coordinate point on the basis of the theoretical model of the measured surface and the information from the measurement that contains errors caused by internal and external factors. Based on this model, an algorithm for real-time data processing has been developed. The algorithm works in "predictor-corrector" mode at each step of which the best estimate is obtained. The estimate is based on minimizing the variance of a random component in which the main values are formed from the accumulated statistical data of the error of the model and the measurement error. This paper presents the results of experimental studies, carried out with simulations of mechanical effects in four modes. The results confirm the high efficiency of the algorithm for high-accuracy measurement of flat surfaces in the presence of mechanical effects. [ABSTRACT FROM AUTHOR]

In order to improve the measurement accuracy of mutual inductance parameters of transmission lines in the environment of double circuit power lines without power outage, this paper establishes simulation models for measuring mutual inductance parameters of transmission lines using the different frequency method in two modes: equal and unequal zero sequence self‐parameters of two circuit lines, using a parallel zero sequence coupling model of double circuit power lines. In the article, simulation analysis is conducted on the line parameters with a coupling coefficient between 0.4 and 0.6 and a line length of 20–50 km. In order to further verify the correctness of the simulation model and measurement methods, a test bench was established based on the principle of line mutual inductance parameter testing in a laboratory dual circuit line without a power outage environment for experimental testing. By comparing the test values under experimental conditions with the standard values, it has been proven that the model and method can meet the actual measurement requirements of engineering. [ABSTRACT FROM AUTHOR]

AbstractNumerous biased estimators are known to circumvent the multicollinearity problem in linear measurement error models. This article proposes a general biased estimator with the ridge regression and the Liu estimators as special cases. The efficiency of the suggested estimator is compared with ridge regression and Liu estimators under the mean squared error matrix criterion. In addition, a Monte Carlo simulation study and a numerical evaluation have been conducted to elucidate the superiority of the new general biased estimator over other estimators. [ABSTRACT FROM AUTHOR]

A particle swarm algorithm-based identification method for the optimal measurement area of large coordinate measuring machines (CMMs) is proposed in this study to realize the intelligent identification of measurement objects and optimize the measurement position and measurement space using laser tracer multi-station technology. The volumetric error distribution of the planned measurement points in the CMM measurement space is obtained using laser tracer multi-station measurement technology. The volumetric error of the specified step distance measurement points is obtained using the inverse distance weighting (IDW) interpolation algorithm. The quasi-rigid body model of the CMM is solved using the LASSO algorithm to obtain the geometric error of the measurement points in a specified step. A model of individual geometric errors is fitted with least squares. An error optimization model for the measurement points in the CMM space is established. The particle swarm optimization algorithm is employed to optimize the model, and the optimal measurement area of the CMM airspace is determined. The experimental results indicate that, when the measurement space is optimized based on the volume of the object being measured, with dimensions of (35 × 35 × 35) mm3, the optimal measurement area for the CMM, as identified by the particle swarm algorithm, lies within the range of 150 mm < X < 500 mm, 350 mm < Y < 700 mm, and −430 mm < Z < −220 mm. In particular, the optimal measurement area is defined as 280 mm < X < 315 mm, 540 mm < Y < 575 mm, and −400 mm < Z < −365 mm. Comparative experiments utilizing a high-precision standard sphere with a diameter of 19.0049 mm and a sphericity of 50 nm demonstrate that the identified optimal measurement area is consistent with the results obtained through the particle swarm algorithm, thereby validating the correctness of the method proposed in this study. [ABSTRACT FROM AUTHOR]

In this article, we define mixed predictor and stochastic restricted ridge predictor of partially linear mixed measurement error models by taking advantage of Kernel approximation. Under matrix mean square error criterion, we make the comparison of the superiorities the linear combinations of the new defined predictors. Then we investigate the asymptotic normality characteristics and the situation of the unknown covariance matrix of measurement errors. Finally, the study is ended with a Monte Carlo simulation study and COVID-19 data application. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the groundbreaking work by Cleveland and McGill in 1984, studies have revealed the difficulties humans have extracting quantitative data from visualizations as simple as bar graphs. As a first step toward understanding this situation, this paper proposes a mathematical model of the interpretation effort of a bar graph using concepts drawn from eye tracking. First, three key areas of interest (AOIs) are identified, and fixations are modeled as random point clouds within the AOIs. Stochastic geometry is introduced via random triangles connecting fixations within the adjacent key visual regions. The so-called landmark methodology provides the basis for the probabilistic analysis of the constructed system. It is found that the random length of interest in a stochastic triangle has a noncentral chi distribution with a known mean. Unique to this model, in terms of previous landmark applications, is the inclusion of a correlation between fixations, which is justified by physiological studies of the eyes. This approach introduces several model parameters, such as the noncentrality parameter, variance of the fixation cloud, correlation between fixations, and a visualization scale. A detailed parametric analysis examining the dependence of the mean on these parameters is conducted. The paper ties this work to the visualization via a definition of the expected visual measurement error. An asymptotic analysis of the visual error is performed, and a simple expression is found to relate the expected visual measurement error to the key model parameters. From this expression, the influence these parameters have on a visualization's interpretation is considered. [ABSTRACT FROM AUTHOR]

Matrix-variate generalized linear model (mvGLM) has been investigated successfully under the framework of tensor generalized linear model, because matrix-form data can be regarded as a specific tensor (2-dimension). But there are few works focusing on matrix-form data with measurement error (ME), since tensor in conjunction with ME is relatively complex in structure. In this paper we introduce a mvGLM to primarily explore the influence of ME in the model with matrix-form data. We calculate the asymptotic bias based on error-prone mvGLM, and then develop bias-correction methods to tackle the affect of ME. Statistical properties for all methods are established, and the practical performance of all methods is further evaluated in analysis on synthetic and real data sets. [ABSTRACT FROM AUTHOR]

Consistent estimators of the baseline hazard rate and the regression parameter are constructed in the Cox proportional hazards model with heteroscedastic measurement errors, assuming that the baseline hazard function belongs to a certain class of functions with bounded Lipschitz constants. [ABSTRACT FROM AUTHOR]