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County level estimates of mean sheet and rill erosion from the Conservation Effects Assessment Project (CEAP) are useful for program development and evaluation. Since county sample sizes in the CEAP survey are insufficient to support reliable direct estimators, small area estimation procedures are needed. The quantity of water runoff is a useful covariate but is unavailable for the full population. We use an estimate of mean runoff from the CEAP survey as a covariate in a small area model with sheet and rill erosion as the response. As the runoff and sheet and rill erosion are estimators from the same survey, the measurement error in the covariate is important as is the correlation between the measurement error and the sampling error. We conduct a detailed investigation of small area estimation in the presence of a correlation between the measurement error in the covariate and the sampling error in the response. In simulations, the proposed predictor is superior to small area predictors that assume the response and covariate are uncorrelated or that ignore the measurement error entirely. [ABSTRACT FROM AUTHOR]

Objective: To assess the cost-effectiveness of using cheaper-but-noisier outcome measures, such as a short questionnaire, for large simple clinical trials. Background: To detect associations reliably, trials must avoid bias and random error. To reduce random error, we can increase the size of the trial and increase the accuracy of the outcome measurement process. However, with fixed resources, there is a trade-off between the number of participants a trial can enrol and the amount of information that can be collected on each participant during data collection. Methods: To consider the effect on measurement error of using outcome scales with varying numbers of categories, we define and calculate the variance from categorisation that would be expected from using a category midpoint; define the analytic conditions under which such a measure is cost-effective; use meta-regression to estimate the impact of participant burden, defined as questionnaire length, on response rates; and develop an interactive web-app to allow researchers to explore the cost-effectiveness of using such a measure under plausible assumptions. Results: An outcome scale with only a few categories greatly reduced the variance of non-measurement. For example, a scale with five categories reduced the variance of non-measurement by 96% for a uniform distribution. We show that a simple measure will be more cost-effective than a gold-standard measure if the relative increase in variance due to using it is less than the relative increase in cost from the gold standard, assuming it does not introduce bias in the measurement. We found an inverse power law relationship between participant burden and response rates such that a doubling the burden on participants reduces the response rate by around one third. Finally, we created an interactive web-app (https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/) to allow exploration of when using a cheap-but-noisy measure will be more cost-effective using realistic parameters. Conclusion: Cheaper-but-noisier questionnaires containing just a few questions can be a cost-effective way of maximising power. However, their use requires a judgement on the trade-off between the potential increase in risk of information bias and the reduction in the potential of selection bias due to the expected higher response rates. Key messages: A cheaper-but-noisier outcome measure, like a short form questionnaire, is a more cost-effective method of maximising power in large simple clinical trials than an error free gold standard measure when the percentage increase in noise from using the cheaper-but-noisier measure is less than the relative difference in the cost of administering the two measures. We have created an R-shiny app to facilitate the exploration of when this condition is met at https://benjiwoolf.shinyapps.io/cheapbutnoisymeasures/ Cheaper-but-noisier outcome measures are more likely to introduce information bias than a gold standard but may reduce selection bias because they reduce loss-to-follow-up. Researchers therefore need to form a judgement about the relative increase or decrease in bias before using a cheap-but-noisy measure. We encourage the development and validation of short form questionnaires to enable the use of high quality cheaper-but-noisier outcome measures in randomised controlled trials. [ABSTRACT FROM AUTHOR]

Egocentric networks represent a popular research design for network research. However, to what extent and under what conditions egocentric network centrality can serve as reasonable substitutes for their sociocentric counterparts are important questions to study. The answers to these questions are uncertain simply because of the large variety of networks. Hence, this paper aims to provide exploratory answers to these questions by analyzing both empirical and simulated data. Through analyses of various empirical networks (including some classic albeit small ones), this paper shows that egocentric betweenness approximates sociocentric betweenness quite well (the correlation is high across almost all the networks being examined) while egocentric closeness approximates sociocentric closeness only reasonably well (the correlation is a bit lower on average with a larger variance across networks). Simulations also confirm this finding. Analyses further show that egocentric approximations of betweenness and closeness seem to work well in different types of networks (as featured by network size, density, centralization, reciprocity, transitivity, and geodistance). Lastly, the paper briefly presents three ideas to help improve egocentric approximations of centrality measures. [ABSTRACT FROM AUTHOR]

Significant errors often arise when measuring streamflow during high flows and flood events. Such errors conflated by short records of observations may induce bias in the flood frequency estimates, leading to costly engineering design mistakes. This work illustrates how observational (measurement) errors affect the uncertainty of flood frequency estimation. The study used the Bulletin 17 C (US standard) method to estimate flood frequencies of historical peak flows modified to represent the measurement limitations. To perform the modifications, we explored, via Monte Carlo simulation, four hypothetical scenarios that mimic measurement errors, sample size limitations, and their combination. We used a multiplicative noise from a log-normal distribution to simulate the measurement errors and implemented a bootstrap approach to represent the sampling error. Then, we randomly selected M samples from the total N records of the observed peak flows of four gauging stations in Iowa in central USA. The observed data record ranges between 76 and 119 years for watersheds with drainage areas between 500 and 16,000 km2. According to the results, measurement errors lead to more significant differences than sampling limitations. The scenarios exhibited differences with median magnitudes of up to 50%, with some cases reaching differences up to 100% for return periods above 50 years. The results raise a red flag regarding flood frequency estimation that warrants looking for further research on observational errors. [ABSTRACT FROM AUTHOR]

Ranked set sampling is widely acknowledged for its superior efficiency compared with simple random sampling. Only a small amount of work has been conducted using ranked set sampling when measurement errors are present. This study introduces innovative estimators utilizing ranked set sampling to assess the population mean when faced with both correlated and uncorrelated measurement errors. The expressions for the bias and mean squared error of the proposed estimators are derived up to the first-order approximation, revealing their superior performance compared to the other examined estimators. The efficacy of the suggested estimators in handling measurement errors was demonstrated through numerical illustration and simulation study investigations. The recommended estimators are further compared to the existing ones using the percentage relative efficiency and mean squared error, and the impact of measurement errors on the results is highlighted through the percentage computation of measurement errors. The innovative estimators suggested were formulated by judiciously incorporating ratio, exponential, and log estimators. Numerical examples involving expenditure and income, as well as simulated data generated from a normal population using R software, affirm the superior performance of the proposed estimators over existing ones such as the usual mean estimator and those proposed by Vishwakarma and Singh (2022), as evidenced by the higher percent relative efficiency and lower mean squared error. The evaluation of the percentage contribution of measurement error values confirms the impact of measurement errors on the properties of the estimators. [ABSTRACT FROM AUTHOR]

This paper presents the creation and realization of a precision time base (PTB) for mitigating jitter and measuring time interval error (TIE) in digital sampling oscilloscopes (DSO), achieved through the utilization of a field programmable gate array (FPGA). Our proposed method focuses on mitigating jitter through PTB, which involves the sampling of two reference channels having a phase shift of approximately 90∘ (at quadrature) for an accurate timebase correction. Through extensive experimentation and analysis, we were able to achieve a reduction in root mean square (RMS) jitter, minimizing it to around 260 fs. This outcome demonstrates the effectiveness of our approach in enhancing the accuracy and reliability of DSO measurements. Expanding on the application of our previously proposed technique, we demonstrate that the same sampling error correction approach can be utilized for TIE measurement. By sampling a large amount of data at either the rising or falling edge of a sinusoidal signal, TIE can be accurately evaluated. This extension enhances the versatility of our FPGA-based implementation by enabling comprehensive TIE analysis alongside PTB compensation in DSOs. [ABSTRACT FROM AUTHOR]

Google Trends is a popular data source that has been utilized in hundreds of studies across various fields, including information technology, business, economics, healthcare, and political science. While several previous research has addressed sampling error issues, this article focuses on the measurement errors resulting from changes in Google Trends' data collection method. By examining key phrases such as 'covid-19,' 'decoupling China,' and 'debt trap diplomacy,' this article reveals that, after 2022 when the data collection method was updated, Google Trends generates search results for terms that were not yet coined or yields too few results. These 'strange' search results indicate the presence of measurement errors in Google Trends data. Scholars are advised to validate the contents of Google Trends data through additional steps. In particular, authors in fields like business, economics, and healthcare are encouraged to replicate their research conducted before 2022 to assess the forecasting errors arising from the new data sets retrieved after that period. [ABSTRACT FROM AUTHOR]

We consider the development of optoelectronic instruments used for measuring linear and angular quantities based on focal-plane arrays (photomatrices) with the help of a digital phase analyzer (digital raster). It is shown that, unlike the phase raster with mechanical drive, the digital raster guarantees the possibility of automation, increasing the accuracy of measurements, and decreasing the weight and size characteristics of the measuring instruments. We propose a method for constructing digital raster based on the use of photomatrices in combination with computer technologies. To guarantee the required accuracy of the developed optoelectronic devices, we study the sources and components of measurement errors. We analyze the influence of the discrete structure of digital raster on the measurement errors of the coordinates of image. We obtain mathematical expressions for the evaluation of the limit values of the errors of coordinate measurements caused by the parasitic phase modulation formed in the case of application of the digital raster (sampling error). The dependences of these errors on the parameters of images and pixel size are determined. It is shown that, by using the permissible maximum sampling error, it is possible to compute the required sizes of the photomatrix pixel. In this case, the size of the sampling pixel can be larger than the size of the photomatrix pixel, which is very important from the viewpoint of energy relations. The obtained results prove to be useful for the development of optoelectronic means of angular and linear measurements with digital rasters. [ABSTRACT FROM AUTHOR]

This study mainly concerns correction for measurement error using the meta‐analysis of Fisher's z‐transformed correlations. The disattenuation formula of Spearman (American Journal of Psychology, 15, 1904, 72) is used to correct for individual raw correlations in primary studies. The corrected raw correlations are then used to obtain the corrected z‐transformed correlations. What remains little studied, however, is how to best correct for within‐study sampling error variances of corrected z‐transformed correlations. We focused on three within‐study sampling error variance estimators corrected for measurement error that were proposed in earlier studies and is proposed in the current study: (1) the formula given by Hedges (Test validity, Lawrence Erlbaum, 1988) assuming a linear relationship between corrected and uncorrected z‐transformed correlations (linear correction), (2) one derived by the first‐order delta method based on the average of corrected z‐transformed correlations (stabilized first‐order correction), and (3) one derived by the second‐order delta method based on the average of corrected z‐transformed correlations (stabilized second‐order correction). Via a simulation study, we compared performance of these estimators and the sampling error variance estimator uncorrected for measurement error in terms of estimation and inference accuracy of the mean correlation as well as the homogeneity test of effect sizes. In obtaining the corrected z‐transformed correlations and within‐study sampling error variances, coefficient alpha was used as a common reliability coefficient estimate. The results showed that in terms of the estimated mean correlation, sampling error variances with linear correction, the stabilized first‐order and second‐order corrections, and no correction performed similarly in general. Furthermore, in terms of the homogeneity test, given a relatively large average sample size and normal true scores, the stabilized first‐order and second‐order corrections had type I error rates that were generally controlled as well as or better than the other estimators. Overall, stabilized first‐order and second‐order corrections are recommended when true scores are normal, reliabilities are acceptable, the number of items per psychological scale is relatively large, and the average sample size is relatively large. [ABSTRACT FROM AUTHOR]

We propose a method to reconstruct sea surface height anomalies (SSHA) from vector surface currents and winds. This analysis is motivated by the proposed satellite ODYSEA, which is a Doppler scatterometer that measures coincident surface vector winds and currents. If it is feasible to estimate SSHA from these measurements, then ODYSEA could provide collocated fields of SSHA, currents, and winds over a projected wide swath of ∼1700 km. The reconstruction also yields estimates of the low-frequency surface geostrophic, Ekman, irrotational, and nondivergent current components and a framework for separation of balanced and unbalanced motions. The reconstruction is based on a steady-state surface momentum budget including the Ekman drift, Coriolis acceleration, and horizontal advection. The horizontal SSHA gradient is obtained as a residual of these terms, and the unknown SSHA is solved for using a Helmholtz–Hodge decomposition given an imposed SSHA boundary condition. We develop the reconstruction using surface currents, winds, and SSHA off the U.S. West Coast from a 43-day coupled ROMS–WRF simulation. We also consider how simulated ODYSEA measurement and sampling errors and boundary condition uncertainties impact reconstruction accuracy. We find that temporal smoothing of the currents for periods of 150 h is necessary to mitigate large reconstruction errors associated with unbalanced near-inertial motions. For the most realistic case of projected ODYSEA measurement noise and temporal sampling, the reconstructed SSHA fields have an RMS error of 2.1 cm and a model skill (squared correlation) of 0.958 with 150-h resolution. We conclude that an accurate SSHA reconstruction is feasible using information measured by ODYSEA and external SSHA boundary conditions. [ABSTRACT FROM AUTHOR]

Field Oriented Control (FOC) effectively realizes independent control of flux linkage and torque, and is widely used in application of Permanent Magnet Synchronous Motor (PMSM). However, it is necessary to detect the phase current information of the motor to realize the current closed-loop control. The phase current detection method based on a sampling resistor will cause a measurement error due to the influence of parasitic parameters of the sampling resistor, which will lead to the decrease in PMSM control performance. This paper reveals the formation mechanism of the current sampling error caused by parasitic inductance and capacitance of the sampling resistor, and further confirms that the above error will lead to the fluctuation of the electromagnetic torque output by simulation. Moreover, we propose an approach for online observation and compensation of the current sampling error based on PI-type observer to suppresses the torque pulsation of PMSM. The phase current sampling error is estimated by the proportional and integral (PI) observer, and the deviation value of current sampling is obtained by low-pass filter (LPF). The above deviation value is further injected into the phase current close-loop for error compensation. The PI observer continues to work to keep the current sampling error close to zero. The simulation platform of Matlab/Simulink (Version: R2021b) is established to verify the effectiveness of online error observation and compensation. Further experiments also prove that the proposed method can effectively improve the torque fluctuation of the PMSM and enhance its control accuracy performance of rotation speed. [ABSTRACT FROM AUTHOR]

This year (2023) marks 140 years since the first publication of a facial soft tissue thickness (FSTT) study. Since 1883, a total of 139 studies have been published, collectively tallying > 220,000 tissue thickness measurements of > 19,500 adults. In just the last 5-years, 33 FSTT studies have been conducted. Herein, we add these data (plus an additional 20 studies) to the 2018 T-Table to provide an update of > 81,000 new datapoints to the global tallied facial soft tissue depths table. In contrast to the original 2008 T-Table, some notable changes are as follows: increased FSTTs by 3 mm at infra second molar (ecm2–iM2ʹ), 2.5 mm at gonion (go–goʹ), 2 mm at mid-ramus (mr–mrʹ), and 1.5 mm at zygion (zy–zyʹ). Rolling grand means indicate that stable values have been attained for all nine median FSTT landmarks, while six out of nine bilateral landmarks continue to show ongoing fluctuations, indicating further data collection at these landmarks holds value. When used as point estimators for individuals with known values across 24 landmarks (i.e., C-Table data), the updated grand means produce slightly less estimation error than the 2018 T-Table means (3.5 mm versus 3.6 mm, respectively). Future efforts to produce less noisy datasets (i.e., reduce measurement and sampling errors as much as possible between studies) would be useful. [ABSTRACT FROM AUTHOR]

The issue of the actually achieved measurement temperature accuracy of samples of materials used for fabricating printed circuit board bases using the contact method is considered. The purpose of the work is to estimate the error in measuring the temperature of the sample surface caused by the temperature field distortion when a thermocouple probe is brought into contact with the surface. Numerical simulation of the temperature distribution in the sample before and after its contact with the thermocouple probe is carried out. The effect of the thermal conductivity of a sample and the contact area of a thermocouple with the sample on the temperature measurement error is studied. The possibility of increasing the accuracy of temperature measurements by the contact method by introducing a thermal interface in the form of a heat-conducting paste into the contact area is discussed. [ABSTRACT FROM AUTHOR]

This study proposes a closed-form direction-of-arrival (DoA) solution derived from multilateration equations for microphone arrays of co-centered and orthogonal pairs. The generalized cross-correlation phase transform (GCC-Phat) algorithm is used to obtain the time-difference-of-arrival (TDoA) values. Simulation studies have shown the success of our proposed method compared to existing DoA methods in the literature by varying the sampling frequency of the sound signal, inter-microphone distances, and the source distance. The results from the simulation are validated by the measurements from our experiments. Our proposed solution gives better results than the far-field solution against the angle error, which is more pronounced at incidence angles smaller than 15°. These angle errors, which approach 3° using the far-field method, are reduced to less than 0.5 degrees using our proposed solution. Our solution also gives more stable results against TDoA measurement errors. Our proposed solution achieves a 66% improvement for azimuth angle and 5.88% improvement for elevation angle compared to the simulation results in the absence of TDoA measurement error, outperforming the far-field approach. When normally distributed sampling error is added to TDoA measurements, with a standard deviation of three samples, our proposed solution achieves a 41% improvement for azimuth angle and a 5.44% improvement for elevation angle. In our field measurements, an absolute mean error of 0.94 degrees was observed with our proposed method for azimuth angle. It is shown to be a more stable and faster solution method for real-time applications. [ABSTRACT FROM AUTHOR]

When comparison among groups is of major importance, it is necessary to ensure that the measuring tool exhibits measurement invariance. This means that it measures the same construct in the same way for all groups. In the opposite case, the test results in measurement error and bias toward a particular group of respondents. In this study, a new approach to examine measurement invariance was applied, which was appropriately designed when a large number of group comparisons are involved: the alignment approach. We used this approach to examine whether the factor structure of a cognitive ability test exhibited measurement invariance across the 26 universities of the Kingdom of Saudi Arabia. The results indicated that the P-GAT subscales were invariant across the 26 universities. Moreover, the aligned factor mean values were estimated, and factor mean comparisons of every group's mean with all the other group means were conducted. The findings from this study showed that the alignment procedure is a valuable method to assess measurement invariance and latent mean differences when a large number of groups are involved. This technique provides an unbiased statistical estimation of group means, with significance tests between group pairs that adjust for sampling errors and missing data. [ABSTRACT FROM AUTHOR]

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Higher derivatives are important to interpret the physical process. However, higher derivatives calculated from measured data often deviate from the real ones because of measurement errors. A novel method for data fitting without higher derivatives violating the real physical process is developed in this paper. Firstly, the research on errors' influence on higher derivatives and the typical functions' extreme points distribution were conducted, which demonstrates the necessity and feasibility of adopting extreme points distribution features in neural networks. Then, we proposed a new neural network considering the extreme points distribution features, namely, the extreme-points-distribution-based neural network (EDNN), which contains a sample error calculator (SEC) and extreme points distribution error calculator (EDEC). With recursive automatic differentiation, a model calculating the higher derivatives of the EDNN was established. Additionally, a loss function, embedded with the extreme points distribution features, was introduced. Finally, the EDNN was applied to two specific cases to reduce the noise in a second-order damped free oscillation signal and an internal combustion engine cylinder pressure trace signal. It was found that the EDNN could obtain higher derivatives that are more compatible with physical trends without detailed differentiation equations. The standard deviation of derivatives' error of the EDNN is less than 62.5 percent of that of traditional neural networks. The EDNN provides a novel method for the analysis of physical processes with higher derivatives compatible with real physical trends. [ABSTRACT FROM AUTHOR]

Magnetic resonance fingerprinting (MRF) is a novel quantitative MR technique that simultaneously provides multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions for direct measurement of quantitative errors will make the optimization results more practical and robust. However, optimizing such cost function is computationally expensive and impractical for MRF optimization with tens of thousands of iterations. Here, we introduce a fast MRF simulator to simulate aliased images from actual scan scenarios including undersampling and system imperfections, which substantially reduces computational time and allows for direct error estimation of the quantitative maps and efficient sequence optimization. We evaluate the performance and computational speed of the proposed approach by simulations and in vivo experiments. The simulations from the proposed method closely approximate the signals and MRF maps from in vivo scans, with 158 times shorter processing time than the conventional simulation method using Non-uniform Fourier transform. We also demonstrate the power of applying the fast MRF simulator in MRF sequence optimization. The optimized sequences are validated with in vivo scans to assess the image quality and accuracy. The optimized sequences produce artifact-free T1 and T2 maps in 2D and 3D scans with equivalent mapping accuracy as the human-designed sequence but at shorter scan times. Incorporating the proposed simulator in the MRF optimization framework makes direct estimation of undersampling errors during the optimization process feasible, and provide optimized MRF sequences that are robust against undersampling artifacts and field inhomogeneity. [ABSTRACT FROM AUTHOR]

Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualise associated measurement and modelling errors. In this paper, we study source localisation uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localisation model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualisation techniques, including confidence maps, level-sets, and topology-based visualisations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline. [ABSTRACT FROM AUTHOR]

In recent years, the impact of measurement errors on the performance of various control charts have been well evaluated. However, as far as we know, the multiple sampling control charts in the literature have been proposed based on the assumption that the measurements are precise. In this paper, we investigate the effect of measurement errors with both constant and linearly increasing variance on the performance of double sampling (DS)- X ¯ chart. We study the capability of multiple measurements procedure to reduce the undesired effect of imprecise measurements on detection capability of DS- X ¯ chart. Using simulation experiments, we compare the performance of DS- X ¯ and classical X ¯ charts in terms of average run length (ARL) and standard deviation of run length (SDRL) metrics. The results show that the measurement errors negatively affect the performance of both DS- X ¯ and classical X ¯ charts. The comparison results also confirm that under measurement errors, the DS- X ¯ control chart outperforms the classical X ¯ chart in all out-of-control scenarios. Moreover, the results indicate that taking several measurements on each item can effectively reduce the undesired impact of error on chart's performance. Finally, the undesired impact of measurement errors on detection capability of DS- X ¯ control chart is illustrated using a real-life example. [ABSTRACT FROM AUTHOR]

The conventional z-test statistic, which is one of the most popular statistics, is based on the mean of a sample and the standard error of the mean. Consequently, in case of a violation of the normality of the data, the traditional z-test may lead to incorrect test conclusions. The main aim of this article is to present two robust parametric modifications of the traditional z-test statistic. In order to minimize the effect of non-normality due to the presence of the outliers and some potential contaminated observations in a sample we use either the center or the C_2 statistic and their standard errors, respectively, instead of the mean and the standard error of the mean. The statistical power of one-sample z-tests based on the mean, center and C_2 statistics were compared by generating of random number samples with different sizes and known expectations. These samples were derived from some popular distributions. The comparison shows that the z-tests based on the center and the C_2 statistics are more powerful and efficient than the traditional one. In addition, real-data illustrations by implementing both one-sample z-test and two-sample z-test are also provided. [ABSTRACT FROM AUTHOR]

Modelling the movement uncertainty of human indoor trajectory consist of an essential part in promoting the performance of smart city related applications. At this stage, the existing uncertainty modelling algorithms usually take the constant sampling error or measurement error into consideration and cannot adapt well to the changeable human motion modes and complex handheld modes of smartphones. To fill this gap, this paper applied the Long Short-Term Memory (LSTM) network for continuous prediction of uncertainty error of human indoor trajectory with complex motion modes and detected indoor landmark points. The human motion information including handheld modes, walking speed, and heading information in extracted and fused with detected landmark points for reconstruction of human indoor trajectory under large-scale areas using Gradient Descent (GD) algorithm. In addition, the hybrid LSTM and Multilayer Perceptron (MLP) network is adopted for uncertainty error prediction, by considering both sampling error and measurement error in a specific time period, and the reconstructed trajectory with human motion features are modelled as the input vector for model training with the ground-truth uncertainty error as reference. Comprehensive experiments on real-world collected dataset indicate that the proposed LSTM-assisted uncertainty modelling algorithm has robust outperformance in uncertainty error prediction and uncertainty region definition compared with traditional uncertainty modelling approaches. [ABSTRACT FROM AUTHOR]

In the context of a sample survey, the collection of information on a sensitive variable is difficult, which may cause nonresponse and measurement errors. Due to this, the estimates can be biased and the variation may increase. To overcome this difficulty, we propose an estimator for the estimation of a sensitive variable by using auxiliary information in the presence of nonresponse and measurement errors simultaneously. The properties of the proposed estimators have been studied, and the results have been compared with those of the usual complete response estimator. Theoretical results have been verified through a simulation study using an artificial population and two real-life applications. With the outcomes of the proposed estimator, a suitable recommendation has been made to the survey statisticians for their real-life application. [ABSTRACT FROM AUTHOR]

Evaluating and validating satellite sea surface salinity (SSS) measurements is fundamental. There are two types of errors in satellite SSS: measurement error due to the instrument's inaccuracy and problems in retrieval, and sampling error due to unrepresentativeness in the way that the sea surface is sampled in time and space by the instrument. In this study, we focus on sampling errors, which impact both satellite and in situ products. We estimate the sampling errors of Level 3 satellite SSS products from Aquarius, SMOS and SMAP, and in situ gridded products. To do that, we use simulated L2 and L3 Aquarius, SMAP and SMOS SSS data, individual Argo observations and gridded Argo products derived from a 12-month high-resolution 1/48° ocean model. The use of the simulated data allows us to quantify the sampling error and eliminate the measurement error. We found that the sampling errors are high in regions of high SSS variability and are globally about 0.02/0.03 psu at weekly time scales and 0.01/0.02 psu at monthly time scales for satellite products. The in situ-based product sampling error is significantly higher than that of the three satellite products at monthly scales (0.085 psu) indicating the need to be cautious when using in situ-based gridded products to validate satellite products. Similar results are found using a Correlated Triple Collocation method that quantifies the standard deviation of products' errors acquired with different instruments. By improving our understanding and quantifying the effect of sampling errors on satellite-in situ SSS consistency over various spatial and temporal scales, this study will help to improve the validation of SSS, the robustness of scientific applications and the design of future salinity missions. [ABSTRACT FROM AUTHOR]

Good estimates of weight-at-age for fishery catches and the stock are necessary for more reliable stock assessment and projections. I use a simple model to filter out "noise" in the 3Ps cod weight-at-age estimates, and to fill in missing values, especially for older ages and the age 14+ group that will be used in assessment models for the stock. The weight model is applied to estimated weight-at-age from fishery monitoring activities and also weight-at-age from the Fisheries and Oceans Canada (DFO) Spring research vessel (RV) survey which are assumed to represent weight-at-age in the stock. The other important model inputs are information about the precision of sampling estimates of weight-at-age which the model uses to help distinguish between population variability and measurement error. The model fits the fishery weights-at-age closely for those ages with low measurement error coefficients of variation (CVs). The survey weights-at-age have more between-year variability and presumably higher CVs and therefore the model did not fit these data as well but did capture the overall trends in the weights-at-age over time. [ABSTRACT FROM AUTHOR]

In the present study, the problem of estimation of the finite population mean of a sensitive study variable using the three-stage optional Randomized Response Technique (RRT) model under measurement errors is addressed. A generalized class of estimators is proposed using a mixture of auxiliary attribute and variable. Some members of the proposed generalized class of estimators are identified and studied. The bias and mean square error expressions for the proposed estimators are correctly derived up to first order Taylor's series of approximation. The proposed estimator's efficiency is investigated theoretically and numerically using both real data and simulated data. From the numerical study, the proposed estimators outperforms other existing estimators of the finite population mean. Furthermore, efficiencies of the proposed estimators of the finite population mean decreases as sensitivity level of the survey question increases. [ABSTRACT FROM AUTHOR]

Small area estimation is essential in making reliable inferences for areas where the sample is relatively small or no sample is available. In the case of developing a small area estimation model, if the covariates measured with errors were ignored, the estimation results may be worse than the direct estimate. Therefore, the problem of measurement error in covariates imposes challenges to data analytics in small area estimation. This paper studies a model with multiple covariates subject to structural measurement error and multiple error-free covariates. This study explores different scenarios in a simulation study by creating a finite super population spread across the area, investigating the sensitivity to sample size, and determining the variance of measurement error that was lower than the variance of the sampling error and the variance of the random effect. Jackknife method was employed to obtain a nearly unbiased estimator of the mean squared prediction error of empirical best predictors of the predictors of small area means. The research shows that the performance of the empirical best predictors is better in the case of a large sample size within the area and small variances of the measurement errors. The weighted version is recommended because it is more stable in terms of variability and bias. [ABSTRACT FROM AUTHOR]

In the academic world, students who have a high level of resilience, are mentally healthier, optimistic, dynamic, enthusiastic about various things in life, and more resistant to various things including stress conditions. Many studies show that high resilience will have a positive effect on student achievement. To prove these studies, we conducted a meta-analysis of a number of articles discussing the correlation between resilience and academic stress. This article presents a meta-analysis of the correlation studies that have examined the correlation between resilience on academic stress. The result of a meta-analysis on 17 studies from 4194 subjects shows that resilience is negatively correlated to academic stress (r = -.503). The difference in the variance of correlation can, among others, be caused by sampling error (6.122%) and error in measurement of independent and dependent variables (1.449%). The results indicated that resilience negatively influences academic stress. [ABSTRACT FROM AUTHOR]

An agent in a nonstationary contextual bandit problem should balance between exploration and the exploitation of (periodic or structured) patterns present in its previous experiences. Handcrafting an appropriate historical context is an attractive alternative to transform a nonstationary problem into a stationary problem that can be solved efficiently. However, even a carefully designed historical context may introduce spurious relationships or lack a convenient representation of crucial information. In order to address these issues, we propose an approach that learns to represent the relevant context for a decision based solely on the raw history of interactions between the agent and the environment. This approach relies on a combination of features extracted by recurrent neural networks with a contextual linear bandit algorithm based on posterior sampling. Our experiments on a diverse selection of contextual and noncontextual nonstationary problems show that our recurrent approach consistently outperforms its feedforward counterpart, which requires handcrafted historical contexts, while being more widely applicable than conventional nonstationary bandit algorithms. Although it is very difficult to provide theoretical performance guarantees for our new approach, we also prove a novel regret bound for linear posterior sampling with measurement error that may serve as a foundation for future theoretical work. [ABSTRACT FROM AUTHOR]

The ratio, product, difference estimators, and unbiased estimator under systematic sampling scheme has been studied in the presence of measurement error. The methods of estimation have been proposed for the estimation of the finite population mean. To exhibit the effect of measurement error, the study variable and auxiliary variable are supposed to be observed with measurement error. The properties of estimators' viz. bias and MSE have been obtained. The simulation study has been conducted to compute the effect of measurement error on the MSE for the different levels of correlation coefficient and different levels of error variance. [ABSTRACT FROM AUTHOR]

By measuring the temporal consistency, or repeatability, in the diets of predators, we can gain a better understanding of the degree of individual specialization in resource utilization and implications for predator–prey interactions, population dynamics, and food web structure. To measure repeatability, we require repeated diet estimates of individuals over time, such as those derived from quantitative fatty acid signature analysis (QFASA), a popular diet estimation technique. However, diet estimates are often lengthy compositional vectors with many zeros, as some prey will not be consumed by all individuals, precluding the use of previously proposed measures of repeatability. In this paper, we propose a novel approach for inferring repeatability for multivariate data and, in particular, compositional diet estimates. We extend the commonly used measure of repeatability for univariate data to the multivariate compositional setting by utilizing the mean squares obtained from a nonparametric multivariate analysis of variance, and an appropriate choice of statistical distance. Our measure and its extension are compatible with both balanced and unbalanced data sets. Associated confidence intervals via nonparametric bootstrapping are also developed for the case of QFASA diet estimates that incorporate both sampling error and measurement error, where the latter error arises because the diets of predators are estimated. Simulation study results suggest that for practical levels of repeatability, our methods yield confidence intervals with the desired coverage probability even when the sample size relative to the dimension of the data (i.e., number of prey species eaten) is small. We tested our methods using QFASA diet estimates for free‐ranging Northwest Atlantic grey seals. Given the importance of understanding how predator diets vary over time and space, our method may find broad application to other compositional diet estimates, including those derived from the stomach or fecal contents, and stable isotope analyses. [ABSTRACT FROM AUTHOR]

[Display omitted] • The front surface, rear surface, thickness variation, and refractive index inhomogeneity information of the transparent multi-surface optics to be tested can be reconstructed simultaneously from a set of interferograms; • Two convolutional fitted windows have been redesigned which have explicit function expressions and better window performance, they can be stretched to free lengths; • The factors which affect the performance of multi-surface reconstruction are fully analyzed, which provides a solid foundation for the analysis of sampling validity during multi-surface interferometry; • A phase-shift matching method without strict limitation of cavity lengths is developed, and four-surface detection in flexible positions is realized. Consisting of the front and rear surfaces of the measured optics and two reference surfaces, the four-surface interferometry system can obtain parameters of the measured surface profiles and refractive index inhomogeneity by acquiring and decoding the fringe patterns of the measured plate and the rear reference plate respectively. However, the difficulty of such measurement comes from the complex superposition of at least six interference sub-signals, and the setting of the phase-shift interval determines whether the harmonics can be reconstructed or not. To improve the measurement accuracy, two kinds of convolutional fitting window functions are redesigned based on the Hanning window to enhance the function performance. Then, by analyzing the reconstruction errors of each wavefront under different combinations of sampling intervals and cavity lengths during four-surface measurements, an error quantization matrix obtained by triple iteration is constructed. By setting a threshold condition, the optimal phase-shifts under different cavity lengths can be matched adaptively. The multi-surface measurement accuracy and reliability of adaptive phase-shift matching are verified in the simulation. The interferogram acquisition and phase demodulation of two transparent flats were performed using a Fizeau wavelength-tuning interferometer, and the reconstruction errors of the surfaces in the repetitive measurements further verified the reliability of the proposed method. [ABSTRACT FROM AUTHOR]

Quantitative analysis of error sources in wave measurement intercomparison under real-world conditions remains challenging due to various factors such as spatial and temporal offsets in the field, diverse configurations of wave sensors and platforms, and distinct measurement algorithms. This study introduces a modified error analysis method based on sampling variability. Measurements from two co-deployed Datawell Waverider buoys at the National Marine Test Site (Weihai) in China offer an opportunity to assess the spatial variability error. The results reveal that for non-directional wave parameters, like significant wave height and mean zero-crossing period, the wave conditions in the field can be considered stationary, with random errors primarily arising from inherent sampling variability. Spatial offsets significantly impact directional wave parameters at peak frequencies, with large deviations (greater than 20°) in peak wave direction primarily attributed to bimodal distribution. For data with significant wave heights exceeding 0.5 m, random errors in the mean wave direction at the same frequency can be mainly attributed to sampling variability. The bulk directional wave parameters weighted by the energy spectrum demonstrate lower sensitivity to spatial offsets, and there is excellent agreement between the bulk mean directions when considering only the unimodal distribution data. • Wave intercomparison of two co-deployed Datawell Waverider buoys was analyzed. • Random error of non-directional parameters mainly arises from sampling variability. • The directional wave parameters at peak frequencies are sensitive to spatial offsets. • Excellent agreement between the bulk mean direction of unimodal distribution data. [ABSTRACT FROM AUTHOR]

The quality of historical US census data is critical to the performance of linking algorithms. We use genealogical profiles to correct measurement error in census names and ages. Our findings suggest that one in every two records has an error in name or age, and human capital is correlated with lower error rates. While errors in age decline across subsequent census rounds from 1850 to 1930, errors in names do not exhibit such trends. Fixing all transcription errors, hence leaving only those errors made at the time of enumeration, would reduce error rates in names by 41 percent. Correcting all names and ages using genealogical profiles leads to 20%–36% more links and fewer false positives. Reassuringly, we find that reducing such errors has a negligible effect on estimates of intergenerational mobility. [ABSTRACT FROM AUTHOR]

Khalil et al. (2018, 2019a) presented mean estimators of a sensitive study variable in the presence of measurement errors (ME) in single-phase sampling. We extend that work forward in two-phase sampling under both simple and stratified random sampling and present generalized mean estimators. Efficiency of the proposed estimators is validated through a simulation study. [ABSTRACT FROM AUTHOR]

Measurement error of sparkle contrast was investigated by applying image subtraction method. The sparkle contrast was measured by changing the region of interest (ROI) and the imaging F‐number, while the measurement distance was fixed. The statistical error was dependent on the size of the ROI. It was suppressed when the ROI was over 100 × 100 LMD pixels under the condition which the intensity modulation of the image of the display matrix elements optically disappeared on the LMD sensor plane by choosing the specific combination of the effective F‐number of the imaging lens, the pitch of the display matrix element, and the optical magnification. The ratio of the intensity standard deviation of the subtracted image to that of the original image could be used as an error indicator of the statistical error by insufficient sampling points. It could also be used for analyzing the error caused by the low‐frequency intensity variations within the ROI. In case that the low‐frequency intensity variations were not eliminated from the measured sparkle pattern, the sparkle contrast kept changing with increasing the ROI. The error indicator was not converged to the certain value even if the size of the ROI was over 100 × 100 LMD pixels. [ABSTRACT FROM AUTHOR]

Byers‐Heinlein et al. (2021) argue that a greater focus on reliability in infant research is much needed and desirable for the field. Moreover, they offer a number of possible solutions to increase reliability in infant research. Here, I do not discuss the suitability of the offered solutions. Instead, I further develop the authors' argument by considering the worryingly downstream consequences of low reliability for the very possibility of drawing meaningful inferences from the results of replication studies. I argue that the sources of error related to sampling and measurement greatly affect the estimates, resulting in confidence intervals being often very wide. Consequently, interpreting the differences between small‐scale original and replication studies becomes a meaningless task, which, I maintain, could be replaced by a meta‐analytic mindset and the habit of fully incorporating uncertainty in the reasoning about research findings. [ABSTRACT FROM AUTHOR]

This research paper deals with the problem of estimating population mean of study variable using information of auxiliary variable under varying probability and measurement error. Motivated by Kadilar and Cingi, App Math Lett, 2006, 19, 75 and Zaman, Stat Trans‐New Ser, 2020, 21, 159, generalized ratio and product enhanced regressed exponential (ERE) estimators are proposed to estimate the population mean using different auxiliary parameters under the sampling of probability proportional to size (pps) and extended for the case of measurement error. Further, following the strategies of Naik and Gupta, J Ind Soc Agr Stat, 1996, 48, 151; Jhajj et al., Pak J Stat, 2006, 23, 1; Singh et al., Auxiliary Information and A Priori Values in Construction of Improved Estimators, Renaissance High Press, 2007; and Solanki and Singh, Chil J Stat, 2013, 4, 3, conventional ratio, product, regression, generalized and many other types of estimators have been adopted under pps sampling with and without measurement error. Up to the first order of large sample approximation, the bias and mean square error (MSE) of the suggested ERE and adopted estimators are derived and their properties have been studied. Comparisons have been made theoretically and empirically to comprehend the merits of recommended estimators over the conventional and adopted estimators. [ABSTRACT FROM AUTHOR]

Low-rank tensor recovery (LRTR) is a natural extension of low-rank matrix recovery (LRMR) to high-dimensional arrays, which aims to reconstruct an underlying tensor $\boldsymbol{\mathcal {X}}$ X from incomplete linear measurements $\mathfrak {M}(\boldsymbol{\mathcal {X}})$ M (X) . However, LRTR ignores the error caused by quantization, limiting its application when the quantization is low-level. In this work, we take into account the impact of extreme quantization and suppose the quantizer degrades into a comparator that only acquires the signs of $\mathfrak {M}(\boldsymbol{\mathcal {X}})$ M (X) . We still hope to recover $\boldsymbol{\mathcal {X}}$ X from these binary measurements. Under the tensor Singular Value Decomposition (t-SVD) framework, two recovery methods are proposed—the first is a tensor hard singular tube thresholding method; the second is a constrained tensor nuclear norm minimization method. These methods can recover a real $n_1\times n_2\times n_3$ n 1 × n 2 × n 3 tensor $\boldsymbol{\mathcal {X}}$ X with tubal rank $r$ r from $m$ m random Gaussian binary measurements with errors decaying at a polynomial speed of the oversampling factor $\lambda :=m/((n_1+n_2)n_3r)$ λ : = m / ((n 1 + n 2) n 3 r) . To improve the convergence rate, we develop a new quantization scheme under which the convergence rate can be accelerated to an exponential function of $\lambda$ λ . Numerical experiments verify our results, and the applications to real-world data demonstrate the promising performance of the proposed methods. [ABSTRACT FROM AUTHOR]

It is important to estimate the sample data when inspecting the quality of products. Therefore, sampling error and uncertainty in the measurement are inevitable, which may lead to misjudgment in product performance evaluation. Since the important quality characteristics of gasoline belong to one-sided specifications, a one-sided specification capability index was proposed to evaluate whether the process capabilities of various quality characteristics of gasoline reach the required quality levels. The 100(1−α)% upper confidence limits of the index were obtained to ensure low producer's risk and reduce sampling errors. To deal with fuzzy data and limited sample sizes, a fuzzy testing model based on the 100(1−α)% upper confidence limits of the index was developed. A practice example of 95 unleaded gasoline was used to illustrate the effectiveness and usefulness of the proposed method. The result shows that two quality characteristics—Reid vapor pressure and oxygen content—of the nine quality characteristics of the 95 unleaded gasoline should be considered for improvements. This study provided an evaluation procedure to facilitate quality managers to take the opportunity to improve product quality, promoting the improvement of air quality, and the sustainability of industrial processes or products. [ABSTRACT FROM AUTHOR]

The present investigation deals with the estimation of population variance in successive sampling in presence and absence of measurement error. We have suggested a chain type estimator of population variance and compared its performances with natural sample variance estimator in presence and absence of measurement errors. The dominance of the proposed strategies over conventional ones has been demonstrated through numerical investigation carried over the dataset of natural and artificially generated population. Suitable recommendations have been made for the application of proposed strategy in real life. [ABSTRACT FROM AUTHOR]

The performance of the triple sampling (TS) X¯$\bar{X}$ chart is investigated by using a linear covariate error model that considers the presence of measurement errors (ME), called the TS‐ME chart. The sensitivity of the TS‐ME chart is evaluated by means of the average run length (ARL) and standard deviation of the run length (SDRL) performance measures. Additionally, the false alarm rate (FAR) metric is also considered in analyzing the TS‐ME chart's performance. The findings reveal that, as expected, ME significantly affect the TS‐ME chart's performance but taking multiple measurements for each item may compensate for this setback. An example based on real‐life data is given to illustrate the application of the TS‐ME chart. [ABSTRACT FROM AUTHOR]

Multilevel structural equation modeling (MSEM) allows researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This article explores how variation in the sampling ratio in MSEM affects the measurement of Level 2 (L2) latent constructs. Specifically, we investigated whether the sampling ratio is related to bias and variability in aggregated L2 construct measurement and estimation in the context of doubly latent MSEM models utilizing a two-step Monte Carlo simulation study. Findings suggest that while lower sampling ratios were related to increased bias, standard errors, and root mean square error, the overall size of these errors was negligible, making the doubly latent model an appealing choice for researchers. An applied example using empirical survey data is further provided to illustrate the application and interpretation of the model. We conclude by considering the implications of various sampling ratios on the design of MSEM studies, with a particular focus on educational research. [ABSTRACT FROM AUTHOR]

Spectrophotometric pH measurements of seawater (pHspec) are routinely made by the oceanographic community for a wide variety of studies. However, obtaining consistent measurements between laboratories that meet stringent thresholds such as the "weather" (± 0.02) and "climate" (± 0.003) standards, has been a challenge. One of the main sources of error for pHspec measurements is gas exchange of carbon dioxide during sample handling. Here, we present a simple method where pHspec measurements on the total scale are made directly in the sampling bottle, which minimizes sample handling errors because the solution is not transferred during analysis. We compared the performance of this method to a standard automated benchtop system on a hydrography cruise, and the two methods were consistent to 0.003 ± 0.0033 (1σ). This demonstrates that this simple method can produce pHspec that approaches climate quality, and comfortably meets weather quality standards. Additional benefits include high sample throughput, and the ability to rapidly quantify dye perturbation effects for each sample. The latter should be particularly useful for low salinity samples such as those taken from estuaries, insofar as modifications specific to the pHspec measurements of estuarine waters are employed. [ABSTRACT FROM AUTHOR]

In order to improve the predictive ability of the error model of the five-axis machine tool, an indirect error measurement (IEM) method of the five-axis machine tool based on the sample test method was proposed, and the optimized design of the stepped axis sample was completed. By inversely calculating the spatial error model of the machine tool, the function analysis relationship between the sample machining error and different error terms of the machine tool was established, and then the dynamic errors of the machine tool in different machining states were obtained. The reliability and practical effects of the method was verified through experiments. The results showed that the proposed IEM method was in good agreement with the laser measurement method. The predictive ability of the error model of the five-axis machine tool can be improved by using the IEM method, thereby more machining accuracy of the machine tool will be ensured. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimation of population mean of the study character in two-occasion successive sampling in presence of non-response and measurement error. A logarithmic type imputation technique has been developed to reduce the nuisance effect of non-response in sample surveys. Utilizing information available on a highly positively or highly negatively correlated auxiliary variable, a logarithmic type estimator is proposed and its properties, including bias and mean square error, are discussed. An optimal replacement policy has been derived, and effect of measurement errors on the mean square error of the estimator has been studied. Empirical studies have been carried out using both real and simulated data, and suitable recommendations have been put forward to the survey statisticians for applications in real life problems. [ABSTRACT FROM AUTHOR]

In this paper, simple compensation methods for the current measurement errors due to the scaling gain error of the current sensor and non-simultaneous sampling error are proposed for three-phase inverter applications. The scaling error can be compensated offline simply by turning off one phase of the inverter. The error compensation methods for the non-simultaneous sampling error are proposed according to the number of sample and hold (S/H) circuits in the current sensor and analog-to-digital converters (ADCs). When a low-cost ADC with a few S/H circuits is used, it can be divided into three cases and compensated through a simple calculation formula. The effectiveness of the proposed compensation methods is verified through simulations and experiments. [ABSTRACT FROM AUTHOR]