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Background: In global health crises, there is a heightened risk to patient and professional safety. Several studies have evaluated the safety climate, revealing different perceptions among healthcare professionals, often influenced by demographic characteristics. This study aimed to assess the percentage of problematic responses (PPR) for the patient safety climate dimensions and... [ABSTRACT FROM AUTHOR]

In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants. [ABSTRACT FROM AUTHOR]

Objectives The on-line sampling device for cracking gas in ethylene cracking furnace was developed, and an on-line sampling device for cracking gas was obtained with compact structure, sufficient gas-liquid separation, and small sampling and analysis error, which can improve the accuracy of cracking gas analysis. Methods After analyzing the shortcomings of the existing cracking gas sampling devices, it is determined that the newly developed sampling device should have the advantages of full- component sampling, sufficient gas-liquid separation, small sampling and analysis errors, compact structure, easy movement, and convenient assembly and maintenance. It is an on-line sampling device for cracking gas with two-step condensation, sufficient gas- liquid separation, and real and reliable analysis data. Results The volume of the condensing shell I and cryogenic shell II of the formed cracking gas on-line sampling device are not less than 140 L and 130 L, respectively, the produced cracked gas flow rate is 15-25 L/min, and the produced cracking gas outlet temperature is not more than 550 °C, under the above conditions, the temperature of the gas-phase material after gas-liquid separation is not higher than 25 °C, which can realize the full-component sampling of the cracking gas, and the gas-liquid separation is sufficient. Conclusions After sampling and analysis of the developed cracking gas on-line sampling device, the water-oil ratio of the cracking furnace deduced is 0. 498, which is very close to the actual operation water-oil ratio of 0. 50, indicating that the sampling method and data processing algorithm used by the cracking gas online sampling device have high performance scientific rationality. The industrial cracking furnace was calibrated by using the on-line sampling device of cracking gas, and the calibration data were used to guide the optimization and control of the outlet temperature (COT) of industrial cracking furnace. With the gradual increase of COT from 845 °C to 855 °C, the yield of propylene and butadiene did not change much, but the ethylene yield increased from 27. 78% to 30. 31%, with an increase of 2. 53 percentage points, and the effect was remarkable. In general, the yield of diene and triene decreased slightly at first and then increased with the increase of outlet temperature of cracking furnace, the increase range reached 5. 83% and 5. 42%, respectively, which effectively improved the efficiency of the plant. [ABSTRACT FROM AUTHOR]

Curiosity is one of the most important characters for elementary school students. However, the facts in the field show that the measurement model used by the teacher to identify the student's curiosity is not yet available in a standardized manner. This study aims to develop a model for measuring the curiosity of elementary school students using the graded response model (GRM) approach. This research uses quantitative method with descriptive type. The research sample used was 236 elementary school students who were randomly selected. Data were collected using a questionnaire of 16 statement items using a Likert scale approach. The data were analyzed using the response item theory approach with the GRM. The results showed that the model for measuring student curiosity in elementary schools had good location parameters, a good discriminant index, a fairly good information function with a small estimation error. The curiosity measurement model in this study can be used as an alternative for teachers to identify students' curiosity in elementary schools. [ABSTRACT FROM AUTHOR]

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The large quantity of movement data collected from various sources can be inherently uncertain and heterogeneous. In the movement data analysis and mining spectrum, computing the similarity of trajectories while considering the uncertainty and heterogeneity has been less addressed. Generally, two factors of sampling and positioning error cause uncertainty in trajectory databases. Therefore, in this research, a method based on the longest common subsequence (LCSS), named FLCSS, is proposed that uses fuzzy theory and the bead model to consider the uncertainty of trajectories originated from positioning and sampling errors. The performance of FLCSS is evaluated by implementations on real and synthetic datasets, and compared with six important and commonly used similarity measurement methods, namely, LCSS, edit distance on real sequence (EDR), dynamic time warping (DTW), edit distance with real penalty (ERP), Hausdorff distance (HD), and Fréchet distance (FD). The results show that FLCSS has a better performance compared to other methods, in terms of sensitivity to point displacement, noise, and different sampling rates. Furthermore, the high correlation between FLCSS and LCSS (ρ = 0.91) confirms the robustness of the proposed method in considering uncertainty in the trajectory databases. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is a medical imaging technique which is widely used in clinical routine. Standard imaging methods lead to so-called contrast-weighted images. The contrast arises from several tissue-related parameters such as the relaxation times T 1 and T 2. The aim in quantitative MRI (qMRI) is an estimation of these quantitative parameters. Magnetic resonance fingerprinting (MRF) is a promising technique in qMRI that allows for the simultaneous determination of multiple tissue-related parameters within a short acquisition time. The conventional MRF method utilizes an approximate inverse Fourier transformation for the highly undersampled data in the Fourier domain, leading to aliasing errors in the reconstructed magnetization courses. Computationally expensive statistical MRF modeling approaches overcome this issue by modeling the data directly in the Fourier domain. However, this leads to a non-convex and large-scale optimization task that is challenging to solve and requires expensive calculations. We compare two recent approaches, namely the conventional MRF method and a statistical MRF modeling approach based on Bayesian statistics in terms of their accuracy, reliability and computational costs. The comparison is carried out for simulated data with known ground truth for different signal-to-noise ratios, in the presence of errors in the physical model, and for several Fourier domain sampling schemes. It is demonstrated that a residual analysis can help to decide if the conventional MRF method is sufficient or if the complex Bayesian Fourier domain modeling approach can lead to a significant improvement. [ABSTRACT FROM AUTHOR]

The Python package mlreflect is demonstrated, which implements an optimized pipeline for the automated analysis of reflectometry data using machine learning. The package combines several training and data treatment techniques discussed in previous publications. The predictions made by the neural network are accurate and robust enough to serve as good starting parameters for an optional subsequent least‐mean‐squares (LMS) fit of the data. For a large data set of 242 reflectivity curves of various thin films on silicon substrates, the pipeline reliably finds an LMS minimum very close to a fit produced by a human researcher with the application of physical knowledge and carefully chosen boundary conditions. The differences between simulated and experimental data and their implications for the training and performance of neural networks are discussed. The experimental test set is used to determine the optimal noise level during training. The extremely fast prediction times of the neural network are leveraged to compensate for systematic errors by sampling slight variations in the data. [ABSTRACT FROM AUTHOR]

In this article, the authors consider some statistical concepts which are important to grasp in the field of dermatology research. The concepts of sampling, systematic errors, confidence intervals, and hypothesis testing are important when critically appraising a study and consolidate the concepts introduced in the previous article on critical appraisal of a trial by Williams et al. [ABSTRACT FROM AUTHOR]

When using discontinuous assay of reactions, initial rates are often estimated from a limited number of time points. There has been no detailed study of how best to do this. In this work, time courses were simulated by different theoretical equations (including strong product inhibition, first order, Michaelis–Menten and truly linear), but with random error addition to each data point. Various methods were tested to fit an initial rate to the data, and the result compared with the known "true" value. Fitting a simple quadratic generally gives initial rates as accurate as any other curve, and is better than a linear fit if there are about 8 or more time points. For fewer points a linear fit gives less variable and often more accurate rates. The absolute contribution to data point error has a major impact on rate accuracy, and often dominates that due to curvature, so that sampling to at least 10% conversion is preferred. The accuracy of a linear fit can be improved by methods that reject some later points based on curvature tests. Awareness of these effects can help avoid rate inaccuracies of 10% or more due to poor methods of data analysis. [ABSTRACT FROM AUTHOR]

The present study extended the p -optimality method to the multistage computerized adaptive test (MST) context in developing optimal item pools to support different MST panel designs under different test configurations. Using the Rasch model, simulated optimal item pools were generated with and without practical constraints of exposure control. A total number of 72 simulated optimal item pools were generated and evaluated by an overall sample and conditional sample using various statistical measures. Results showed that the optimal item pools built with the p -optimality method provide sufficient measurement accuracy under all simulated MST panel designs. Exposure control affected the item pool size, but not the item distributions and item pool characteristics. This study demonstrated that the p -optimality method can adapt to MST item pool design, facilitate the MST assembly process, and improve its scoring accuracy. [ABSTRACT FROM AUTHOR]

In the analysis of survey data, it is of interest to estimate and quantify uncertainty about means or totals for each of several nonoverlapping subpopulations or areas. When the sample size for a given area is small, standard confidence intervals based on data only from that area can be unacceptably wide. In order to reduce interval width, practitioners often utilize multilevel models in order to borrow information across areas, resulting in intervals centered around shrinkage estimators. However, such intervals only have the nominal coverage rate on average across areas under the assumed model for across-area heterogeneity. The coverage rate for a given area depends on the actual value of the area mean and can be nearly zero for areas with means that are far from the across-group average. As such, the use of uncertainty intervals centered around shrinkage estimators are inappropriate when area-specific coverage rates are desired. In this article, we propose an alternative confidence interval procedure for area means and totals under normally distributed sampling errors. This procedure not only has constant 1 − α frequentist coverage for all values of the target quantity but also uses auxiliary information to borrow information across areas. Because of this, the corresponding intervals have shorter expected lengths than standard confidence intervals centered on the unbiased direct estimator. Importantly, the coverage of the procedure does not depend on the assumed model for across-area heterogeneity. Rather, improvements to the model for across-area heterogeneity result in reduced expected interval width. [ABSTRACT FROM AUTHOR]

The p-value is often misunderstood and, for example, misinterpreted as a probability for the correctness of the null hypothesis. The aim of this article is to first explain the definition of the p-value. Determining the p-value requires knowledge of a probability function. Howan appropriate statistical model is selected and how the p-value is determined usingthis model, the null hypothesis and the empirical data is explained using the t-distribution. When interpreting the p-value obtained in this way, two incompatible statistical schools of thought are confronted: the orthodox Neyman-Pearson hypothesis test, which amounts to a decision between the null hypothesis and a complementary alternative hypothesis, and Fisher's significance test, in which no alternative hypothesis is formulated and in which the smaller the p-value, the greater the evidence against the null hypothesis. The amount ends with some critical remarks about the handling of p-values. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to establish mechanisms for process improvement so that production efficiency and product quality can be expected, and create a sustainable development in terms of circular economy. Design/methodology/approach: The authors obtain a critical value from statistical hypothesis testing, and thereby construct a process capability indices chart, which both lowers the chance of quality level misjudgment caused by sampling error and provides reference for the processes improvement in poor quality levels. The authors used the bottom bracket of bicycles as an example to demonstrate the model and methods proposed in this study. Findings: This approach enables us to plot multiple quality characteristics, despite varying attributes and specifications, onto the same process capability analysis chart. And it therefore increases accuracy and precision to reduce rework and scrap rates (reduce), increase product availability, reduce maintenance frequency and increase reuse (reuse), increase the recycle rates of components (recycle) and lengthen service life, which will delay recovery time (recovery). Originality/value: Parts manufacturers in the industry chain can upload their production data to the cloud platform. The quality control center of the bicycle manufacturer can utilized the production data analysis model to identify critical-to-quality characteristics. The platform also offers reference for improvement and adds the improvement achievements and experience to its knowledge management to provide the entire industry chain. Feedback is also given to the R&D department of the bicycle manufacturer as reference for more robust product designs, more reasonable tolerance designs, and selection criteria for better parts suppliers, thereby forming an intelligent manufacturing loop system. [ABSTRACT FROM AUTHOR]

Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational manual material handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. [ABSTRACT FROM AUTHOR]

This pilot study was conducted using a sample of 374 Portuguese occupational therapists to evaluate burnout levels and prevalence among occupational therapists as well as to relate those levels to practitioner age, gender, client age, years of professional activity, and area of practice. The Copenhagen Burnout Inventory questionnaire (Portuguese version) and a sociodemographic questionnaire was used. Results showed that ∼23%, 44%, and 45% of occupational therapists presented with client-related burnout, personal burnout (PB), and work-related burnout, respectively. Higher rates of burnout were observed across all dimensions as the age of the professionals increased, with more years of working increased the client-rated rate, and client age increased the PB rate. Outcomes suggest a need to implement intervention programs to promote personal and organizational strategies to prevent burnout. [ABSTRACT FROM AUTHOR]

This research explores the impact of missing rate of cycling count data on the accuracy of monthly and annual average daily bicycle volume estimates (MADB and AADB). The study made use of a full year of daily bicycle counts at six count stations in Vancouver, Canada. Two missing data patterns were simulated in this study: Completely at Random (MCR) and Not Missing at Random (NMR), also known as the systematic pattern. In the first pattern, repeated random samples of daily bicycle count of different missing rates were drawn from the full data set and used to calculate MADBs and AADB at each count station. In the second pattern, long period data gaps were assumed for periods of one week to four months and MADTs and AADBs were calculated. The estimates calculated from incomplete data were compared to the actual estimates and the errors for each scenario were determined. The results showed that the impact of missing counts on the estimation accuracy of the AADB is minimal where the errors did not exceed 5%, even for high missing rates. This is conditional on that the data is missing randomly and there are a few samples that cover each month of the year. On the other hand, the estimation errors of MADBs were found to be relatively high when the missing rates were high. These results indicated that even if half of the permanent counter data is missing at random, the maximum estimation error would not exceed 14%. The combined impact of AADB and MADB estimation was captured by comparing the MFs calculated using full data versus those calculated by incomplete data. The results showed maximum errors of 94% and 34% for missing rates of 90% and 70%. For the scenario of long period data gaps, the maximum estimation error of AADB ranged between 1.5% and 21.1% when data was missing for one week to four months. Disaggregate error analysis showed that missing data of July would have the most negative impact on the estimation accuracy of AADB. Finally, a Multiple Imputation (MI) method was applied to fill in data gaps for high missing rates. The method led to a maximum AADB estimation error of <3% even if four months of data were continuously missing at one count station. [ABSTRACT FROM AUTHOR]

National statistical organisations seek to publish seasonally adjusted time series in which measurement errors have been minimised and systematic and calendar-related effects are removed. Time series derived from survey data contain sample error, and for rotating panel surveys such errors are correlated over time. Standard seasonal adjustment processes do not account for this, leaving sample error spread across the trend, seasonal and irregular components of the time series. This paper proposes an improvement: modelling sample error as a component of a structural time series model, and removing modelled estimates of sample error before applying existing seasonal adjustment processes. This results in improved seasonally adjusted and trend estimates which better reflect underlying movements and real world phenomena. We discuss several practical considerations for this method: revision properties, estimating sample error for aggregate series, prior corrections, and model maintenance. We demonstrate the potential of this approach using the example of employment and unemployment series from the Australia Labour Force Survey. Simulations show that compared with the current seasonal adjustment method, the proposed method produces estimates of month-to-month movement of seasonally adjusted and trend series which are consistently closer to the series that would arise if there was no sample error. [ABSTRACT FROM AUTHOR]

National statistical organisations seek to publish seasonally adjusted time series in which measurement errors have been minimised and systematic and calendar-related effects are removed. Time series derived from survey data contain sample error, and for rotating panel surveys such errors are correlated over time. Standard seasonal adjustment processes do not account for this, leaving sample error spread across the trend, seasonal and irregular components of the time series. This paper proposes an improvement: modelling sample error as a component of a structural time series model, and removing modelled estimates of sample error before applying existing seasonal adjustment processes. This results in improved seasonally adjusted and trend estimates which better reflect underlying movements and real world phenomena. We discuss several practical considerations for this method: revision properties, estimating sample error for aggregate series, prior corrections, and model maintenance. We demonstrate the potential of this approach using the example of employment and unemployment series from the Australia Labour Force Survey. Simulations show that compared with the current seasonal adjustment method, the proposed method produces estimates of month-to-month movement of seasonally adjusted and trend series which are consistently closer to the series that would arise if there was no sample error. [ABSTRACT FROM AUTHOR]

Outputs from a data assimilation system may be used to diagnose observation- and background-error statistics, as has been demonstrated by previous researchers. In this study, that technique is extended to diagnose model error statistics using a weak-constraint data assimilation. It deals with a set of observations over a time window and uses the temporal distribution to separate model errors from errors in the background forecast. In a set of idealized tests, this method is shown to be able to distinguish successfully between model and background errors. The success of this method depends on the prior assumptions included in the weak-constraint data assimilation and how well these describe the true nature of the system being modelled. Other authors have demonstrated that it can be challenging to separate observation and background errors in diagnosis schemes. Some separation of these errors is possible within the context of four-dimensional assimilation, provided that observation errors are known to be uncorrelated in time. [ABSTRACT FROM AUTHOR]

As an alternative to the functional quadratic model due to Yao and Müller (2010), we consider a functional quadratic multiplicative model. This multiplicative model provides a useful alternative when the relative error is considered for analyzing data with positive responses. The existing work for functional models are mainly based on absolute errors. The commonly used least squares criterion is just such an example. In many practical applications, however, people concern on the size of relative error rather than that of error itself. Therefore, the estimation procedure based on least absolute relative errors, which is proposed by Chen et al. (2010) for the linear multiplicative model, is developed for functional quadratic multiplicative model. The asymptotic behaviors of the proposed estimators are established. Some simulation studies show that the estimation procedure has good prediction performance. Moreover, a real data set is analyzed for illustrating the proposed methods. [ABSTRACT FROM AUTHOR]

Background:The use of the borderline regression method (BRM) is a widely accepted standard setting method for OSCEs. However, it is unclear whether this method is appropriate for use with small cohorts (e.g. specialist post-graduate examinations). Aims and methods:This work uses an innovative application of resampling methods applied to four pre-existing OSCE data sets (number of stations between 17 and 21) from two institutions to investigate how the robustness of the BRM changes as the cohort size varies. Using a variety of metrics, the ‘quality’ of an OSCE is evaluated for cohorts of approximatelyn = 300 down ton = 15. Estimates of the standard error in station-level and overall pass marks,R2coefficient, and Cronbach’s alpha are all calculated as cohort size varies. Results and conclusion: For larger cohorts (n > 200), the standard error in the overall pass mark is small (less than 0.5%), and for individual stations is of the order of 1–2%. These errors grow as the sample size reduces, with cohorts of less than 50 candidates showing unacceptably large standard error. Alpha andR2also become unstable for small cohorts. The resampling methodology is shown to be robust and has the potential to be more widely applied in standard setting and medical assessment quality assurance and research. [ABSTRACT FROM PUBLISHER]

Pansharpening techniques aim at improving the spatial resolution of a multispectral data set (MS) by using a panchromatic image (Pan) acquired on the same scene with a greater spatial resolution and consequently a lower ground sample distance (GSD). Usually, a quantitative assessment of the fused products cannot be directly performed because of the lack of a reference MS data set with the same GSD of the Pan. A well-known solution is Wald's protocol: The original MS and Pan are spatially degraded, the reducing factor being the ratio between their GSDs. Pansharpening is then performed between the reduced MS and Pan data sets, and the fused products are compared with the original MS, which can be used as reference. In this protocol, fusion performances are assumed to be independent of the scale so that the results at the reduced scale are an estimation of those at the original resolution. This hypothesis can be more or less reliable, depending on the sensor and/or the scene content. The objective of this paper is to propose a new methodology to infer the unknown performances of a pansharpening method at full scale. For this purpose, multiple sets of fused images are computed at degraded scales by downsampling Pan and MS data sets by means of the sensor modulation transfer function. Multiscale quality/distortion measurements are fitted by linear and quadratic polynomials in order to extrapolate their full-scale values. Once the proposed protocol has been assessed in the presence of reference originals, the obtained results are extended to the case where the reference image is not available. [ABSTRACT FROM PUBLISHER]

RGB-D cameras like PrimeSense and Microsoft Kinect are popular sensors in the simultaneous localization and mapping researches on mobile robots because they can provide both vision and depth information. Most of the state-of-the-art RGB-D SLAM systems employ the Iterative Closest Point (ICP) algorithm to align point features, whose spatial positions are computed by the corresponding depth data of the sensors. However, the depth measurements of features are often disturbed by noise because visual features tend to lie at the margins of real objects. In order to reduce the estimation error, we propose a method that extracts and selects the features with reliable depth values, i.e. planar point features. The planar features can benefit the accuracy and robustness of traditional ICP, while holding a reasonable computation cost for real-time applications. An efficient RGB-D SLAM system based on planar features is also demonstrated, with trajectory and map results from open datasets and a physical robot in real-world experiments. [ABSTRACT FROM AUTHOR]

In this paper we present improved short-term load forecasting using bagged neural networks (BNNs). The BNNs consist of creating multiple sets of data by sampling randomly with replacement, training a neural network on each data set, and averaging the results obtained from each trained neural network. The bagging process reduces estimation errors and variation range of errors compared to using a single neural network for load forecasting. Examples with real data show the effectiveness of our proposed techniques by demonstrating that using BNNs can reduce load forecasting errors, compared to various existing techniques. [ABSTRACT FROM AUTHOR]

Multiple ratings are becoming increasingly popular for the assessment of a wide range of behaviors. Teaching evaluation designs and 360-degree feedback often use multiple raters alongside self-ratings. Eid and colleagues (2008) proposed a multilevel structural equation model for the analysis of data stemming from such designs that assumes that raters stem from independent populations of raters. However, it is quite common for raters to rate multiple targets, thus implying a cross-classified data structure. A simulation study was conducted to assess the effects of this rater nonindependence on parameter and standard error estimates in multilevel structural equation models. Results show parameter estimation biases to be small, whereas standard errors pertaining to the Level 1 covariance matrices as well as the mean structure on Level 2 are underestimated. [ABSTRACT FROM AUTHOR]

Background: Modern biotechnologies often result in high-dimensional data sets with many more variables than observations (n ⪡ p). These data sets pose new challenges to statistical analysis: Variable selection becomes one of the most important tasks in this setting. Similar challenges arise if in modern data sets from observational studies, e.g., in ecology, where flexible, non-linear models are fitted to high-dimensional data. We assess the recently proposed flexible framework for variable selection called stability selection. By the use of resampling procedures, stability selection adds a finite sample error control to high-dimensional variable selection procedures such as Lasso or boosting. We consider the combination of boosting and stability selection and present results from a detailed simulation study that provide insights into the usefulness of this combination. The interpretation of the used error bounds is elaborated and insights for practical data analysis are given. Results: Stability selection with boosting was able to detect influential predictors in high-dimensional settings while controlling the given error bound in various simulation scenarios. The dependence on various parameters such as the sample size, the number of truly influential variables or tuning parameters of the algorithm was investigated. The results were applied to investigate phenotype measurements in patients with autism spectrum disorders using a log-linear interaction model which was fitted by boosting. Stability selection identified five differentially expressed amino acid pathways. Conclusion: Stability selection is implemented in the freely available R package stabs (http://CRAN.R-project.org/ package=stabs). It proved to work well in high-dimensional settings with more predictors than observations for both, linear and additive models. The original version of stability selection, which controls the per-family error rate, is quite conservative, though, this is much less the case for its improvement, complementary pairs stability selection. Nevertheless, care should be taken to appropriately specify the error bound. [ABSTRACT FROM AUTHOR]

Optimisation of the number of required measurement points and their location is an important research topic in sensor networks. Finding the optimal positions increases spatial coverage and reduces deployment costs. This paper presents an approach for the case that two attributes have to be measured with a different number of available sensors. The proposed cokriging method performs cross-attribute fusion in sensor networks by being based on the analysis of multi-variable spatial correlations. To the best of our knowledge, this scientific work is the first one considering kriging and cokriging interpolations as IF methods. The single-variable ordinary kriging and bi-variable methods were applied to experimental data. The combination of humidity and temperature data in a refrigerated container is used as exemplary case, humidity measurements are considered to be the expensive attribute to measure. The average estimation error for intermediate points was estimated as a function of the number of humidity sensors. When variability is high, data fusion using the bi-variable method produced results as accurate as the single-variable one, without the necessity of deploying a large number of humidity measuring points, by complementing the estimation with temperature measurements. [ABSTRACT FROM AUTHOR]

In a previous study it was found that airborne and buoy-based measurements of wind stress made in the Gulf of Tehuantepc, México failed to agree. Here we revisit the issue and analyze data from both platforms in the context of flux-sampling strategies and find that there is now good agreement between wind-stress estimates from both experiments. The sampling strategies used for airborne and buoy-based sampling capture most of the contributing scales to the momentum flux and, correspondingly, the systematic errors for both stress estimates are low. On the other hand, the random error is much larger for the airborne measurements as compared with that for the buoy-based estimates. Increasing the averaging period for the aircraft-based estimates reduces the random error and brings the stress estimates into a better agreement with those from the buoy data. Since there is a good agreement between stress estimates, the apparent underestimation found earlier seems to be coincidental and caused by the interpolation method employed by the source paper. [ABSTRACT FROM AUTHOR]

We review the appropriateness of using SNIa observations to detect potential signatures of anisotropic expansion in the Universe. We focus on Union2 and SNLS3 SNIa datasets and use the hemispherical comparison method to detect possible anisotropic features. Unlike some previous works where non-diagonal elements of the covariance matrix were neglected, we use the full covariance matrix of the SNIa data, thus obtaining more realistic and not underestimated errors. As a matter of fact, the significance of previously claimed detections of a preferred direction in the Union2 dataset completely disappears once we include the effects of using the full covariance matrix. Moreover, we also find that such a preferred direction is aligned with the orthogonal direction of the SDSS observational plane and this suggests a clear indication that the SDSS subsample of the Union2 dataset introduces a significant bias, making the detected preferred direction unphysical. We thus find that current SNIa surveys are inappropriate to test anisotropic features due to their highly non-homogeneous angular distribution in the sky. In addition, after removal of the highest inhomogeneous sub-samples, the number of SNIa is too low. Finally, we take advantage of the particular distribution of SNLS SNIa sub-sample in the SNLS3 data set, in which the observations were taken along four different directions. We fit each direction independently and find consistent results at the 1 σ level. Although the likelihoods peak at relatively different values of Ω m , the low number of data along each direction gives rise to large errors so that the likelihoods are sufficiently broad as to overlap within 1 σ . [ABSTRACT FROM AUTHOR]

We propose composite quantile regression for dependent data, in which the errors are from short-range dependent and strictly stationary linear processes. Under some regularity conditions, we show that composite quantile estimator enjoys root- n consistency and asymptotic normality. We investigate the asymptotic relative efficiency of composite quantile estimator to both single-level quantile regression and least-squares regression. When the errors have finite variance, the relative efficiency of composite quantile estimator with respect to the least-squares estimator has a universal lower bound. Under some regularity conditions, the adaptive least absolute shrinkage and selection operator penalty leads to consistent variable selection, and the asymptotic distribution of the non-zero coefficient is the same as that of the counterparts obtained when the true model is known. We conduct a simulation study and a real data analysis to evaluate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

We consider transient convection-diffusion equations with a velocity vector field with multiscale character and rough data. We assume that the velocity field has two scales, a coarse scale with slow spatial variation, which is responsible for advective transport and a fine scale with small amplitude that contributes to the mixing. For this problem we consider the estimation of filtered error quantities for solutions computed using a finite element method with symmetric stabilization. A posteriori error estimates and a priori error estimates are derived using the multiscale decomposition of the advective velocity to improve stability. All estimates are independent both of the Péclet number and of the regularity of the exact solution. [ABSTRACT FROM AUTHOR]

Fast capacity estimation is a key enabling technique for second-life of lithium-ion batteries due to the hard work involved in determining the capacity of a large number of used electric vehicle (EV) batteries. This paper tries to make three contributions to the existing literature through a robust and advanced algorithm: (1) a three layer back propagation artificial neural network (BP ANN) model is developed to estimate the battery capacity. The model employs internal resistance expressing the battery's kinetics as the model input, which can realize fast capacity estimation; (2) an estimation error model is established to investigate the relationship between the robustness coefficient and regression coefficient. It is revealed that commonly used ANN capacity estimation algorithm is flawed in providing robustness of parameter measurement uncertainties; (3) the law of large numbers is used as the basis for a proposed robust estimation approach, which optimally balances the relationship between estimation accuracy and disturbance rejection. An optimal range of the threshold for robustness coefficient is also discussed and proposed. Experimental results demonstrate the efficacy and the robustness of the BP ANN model together with the proposed identification approach, which can provide an important basis for large scale applications of second-life of batteries. [ABSTRACT FROM AUTHOR]

This article validates the necessity of adjusting for the design effects in disproportionate stratified sampling designs through the use of sample weights. Using data from the 1958 Birth Cohort study, we demonstrate that complex sampling designs introduce sampling error and even sampling bias into sample data. Such sample data are a poor representation of population parameters. These design effects can be addressed through the application of sample weights. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, an energy estimation methodology based on performance monitor counters (PMC) is proposed to estimate the energy consumption of RVC-CAL video codec specifications. The proposed PMC-driven methodology is able to automatically identify the most appropriate events and training data to cover the main application characteristics. In addition, knowledge of the hardware platform employed is not required. Therefore, this methodology can be easily implemented on other PMC-available systems while keeping the estimation accuracy. It is worth noting that this is an attractive asset to analyze the energy consumption of RVC-CAL codec specifications. Besides, the methodology reduces the PMC redundancy and, thus, the overhead introduced when applied to on-line power management. Experimenting on two RVC-CAL decoders, H.264 and MPEG4 Part2 SP, a coarse estimation model based on instructions per cycle (IPC) and the proposed PMC-driven model are compared. The results show that the PMC-driven model can achieve for the H.264 and MPEG4 Part2 SP decoders average estimation errors of 5.95% and 5.01%, respectively, in comparison to the 17.11% and 13.65% average errors obtained with the IPC-based model. As a consequence, this methodology is suggested to be combined into the RVC framework to help the designer to have an overview of the energy consumption of the specification actors at earlier design stages. [Copyright &y& Elsevier]

In this paper, we consider the use of auxiliary and paradata for dealing with non-response and measurement errors in household surveys. Three over-arching purposes are distinguished: response enhancement, statistical adjustment, and bias exploration. Attention is given to the varying focus at the different phases of statistical production from collection, processing to analysis, and how to select and utilize the useful auxiliary and paradata. Administrative register data provide the richest source of relevant auxiliary information, in addition to data collected in previous surveys and censuses. Due to their importance in effective dealings with non-sampling errors, one should make every effort to increase their availability in the statistical system and, at the same time, to develop efficient statistical methods that capitalize on the combined data sources. [ABSTRACT FROM AUTHOR]

An algorithm for the fusion of data used for target search and tracking originated by a bidimensional radar and infrared search and track is proposed and described. To permit a fusion process between two systems whose measurements are not completely comparable, a new strategy for mixing the system states is introduced, thus obtaining a set of homogeneous measurements. We describe the rationale behind the method and develop mathematical aspects necessary to obtain the equation for the fusion of tracking data. An analysis of the estimation errors associated with the proposed model is also described. Some simulation results demonstrate the capabilities of the presented technique. [ABSTRACT FROM AUTHOR]

By writing scientific articles we communicate science among colleagues and peers. By doing this, it is our responsibility to adhere to some basic principles like transparency and accuracy. Authors, journal editors and reviewers need to be concerned about the quality of the work submitted for publication and ensure that only studies which have been designed, conducted and reported in a transparent way, honestly and without any deviation from the truth get to be published. Any such trend or deviation from the truth in data collection, analysis, interpretation and publication is called bias. Bias in research can occur either intentionally or unintentionally. Bias causes false conclusions and is potentially misleading. Therefore, it is immoral and unethical to conduct biased research. Every scientist should thus be aware of all potential sources of bias and undertake all possible actions to reduce or minimize the deviation from the truth. This article describes some basic issues related to bias in research. [ABSTRACT FROM AUTHOR]

In present work for numerical solving Dirichlet's problem for Lap-lace's equations, there is applied difference scheme of higher accuracy order, allowing getting estimate of the error of order O(h4), only known data take part in this estimate. And this, in its turn, allows applying computational techniques to estimating the error and getting concrete error values. Representation of difference problem (five-point scheme) solution for Lap-lace's equation on rectangle with aid of discrete analog of Fourier method, suggested and grounded in [2], is the main tool for creating its solutions both on whole net domain [4], [5], and on net segments [6], and what is more, not only rectangular, but and on multistep domain as well. Besides, error estimates (of second order regarding net step h), inferred in [2], are effective, i.e. depend only on known quantities. In works [4], [5], though representation of nine-point scheme solution is used, but its convergence to solution of the differential problem is not grounded. Effective estimates for nine-point scheme, not representing the solution by Fourier method, are obtained in [7]. [ABSTRACT FROM AUTHOR]

The impact of uncertainty about item parameters on test information functions is investigated. The information function of a test is one of the most important tools in item response theory (IRT). Inaccuracy in the estimation of test information can have substantial consequences on data analyses based on IRT. In this article, the major part (called the adjusted term) of the deviation of an estimated test information function from the true test information function due to the uncertainty of item parameters was approximated asymptotically, and a simulation study shows that this approximation captures the difference between the estimated and the true information functions rather well. A real data example shows that the magnitude of an estimated adjusted term can be substantially large when a sample size is relatively small. [ABSTRACT FROM AUTHOR]

In this paper, power allocation between pilots and data symbols is investigated to maximize energy efficiency (EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function considering channel estimation error, which depends on large-scale channel gains of multiple users, allocated power to pilots and data symbols, and circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. Exploiting the quasiconcavity property of the EE function, we propose an alternating optimization method in the low transmit power region and reformulate a joint quasiconcave problem in the high transmit power region. Analysis and simulation results show that the power ratio for pilots decreases with the circuit power. When the circuit power is small, the optimal overall transmit power increases with the circuit power. Otherwise, the optimal transmit power does not depend on it. Transmitting more data symbols to the users with higher channel gains improves the EE but at a cost of sacrificing the fairness among multiple users. Simulation results also demonstrate that compared with spectral efficiency (SE)-oriented design, the EE-oriented design can improve the EE performance significantly with a relatively small SE loss. [ABSTRACT FROM PUBLISHER]

Widely recognized in many fields including economics, engineering, epidemiology, health sciences, technology and wildlife management, length-biased sampling generates biased and right-censored data but often provide the best information available for statistical inference. Different from traditional right-censored data, length-biased data have unique aspects resulting from their sampling procedures. We exploit these unique aspects and propose a general imputation-based estimation method for analyzing length-biased data under a class of flexible semiparametric transformation models. We present new computational algorithms that can jointly estimate the regression coefficients and the baseline function semiparametrically. The imputation-based method under the transformation model provides an unbiased estimator regardless whether the censoring is independent or not on the covariates. We establish large-sample properties using the empirical processes method. Simulation studies show that under small to moderate sample sizes, the proposed procedure has smaller mean square errors than two existing estimation procedures. Finally, we demonstrate the estimation procedure by a real data example. [ABSTRACT FROM AUTHOR]

Generalised variance function (GVF) models are data analysis techniques often used in large-scale sample surveys to approximate the design variance of point estimators for population means and proportions. Some potential advantages of the GVF approach include operational simplicity, more stable sampling errors estimates and providing a convenient method of summarising results when a high number of survey variables is considered. In this paper, several parametric and nonparametric methods for GVF estimation with binary variables are proposed and compared. The behavior of these estimators is analysed under heteroscedasticity and in the presence of outliers and influential observations. An empirical study based on the annual survey of living conditions in Galicia (a region in the northwest of Spain) illustrates the behaviour of the proposed estimators. [ABSTRACT FROM AUTHOR]

Abstract: Motivated by the needs of Mars data assimilation, particularly quantification of measurement errors and generation of averaging kernels, we have evaluated atmospheric temperature retrievals from Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) radiances. Multiple sets of retrievals have been considered in this study: (1) retrievals available from the Planetary Data System (PDS), (2) retrievals based on variants of the retrieval algorithm used to generate the PDS retrievals, and (3) retrievals produced using the Mars 1-Dimensional Retrieval (M1R) algorithm based on the Optimal Spectral Sampling (OSS) forward model. The retrieved temperature profiles are compared to the MGS Radio Science (RS) temperature profiles. For the samples tested, the M1R temperature profiles can be made to agree within 2K (in the zonal mean) with the RS temperature profiles, but only after tuning the prior and error statistics. Use of a global prior that does not take into account the seasonal dependence leads errors of up 6K. In polar samples, errors relative to the RS temperature profiles are even larger. In these samples, the PDS temperature profiles also exhibit a poor fit with RS temperatures. This fit is worse than reported in previous studies, indicating that the lack of fit is due to a bias correction to TES radiances implemented after 2004. To explain the differences between the PDS and M1R temperatures, the algorithms are compared directly, with the OSS forward model inserted into the PDS algorithm. Factors such as the filtering parameter, the use of linear versus nonlinear constrained inversion, and the choice of the forward model, are found to contribute heavily to the differences in the temperature profiles retrieved in the polar regions, resulting in uncertainties of up to 6K. Even outside the poles, changes in the a priori statistics result in different profile shapes which all fit the radiances within the specified error. The importance of the a priori statistics prevents reliable global retrievals based a single a priori and strongly implies that a robust science analysis must instead rely on retrievals employing localized a priori information, for example from an ensemble based data assimilation system such as the Local Ensemble Transform Kalman Filter (LETKF). [Copyright &y& Elsevier]

Abstract: Soil sampling is a critical step in environmental site assessment studies. The representativeness of soil samples has a direct influence on financial, liability, environmental and public health issues associated with the outcome of remediation activities. Representativeness must be quantified for assessing and designing soil sampling procedures. Gy''s Sampling Theory and Practice (STP) was used to analyze the reproducibility of two soil sampling procedures, namely a procedure based on grab sampling (GSP) and an alternative procedure (ASP) developed from STP principles. Sampling reproducibility, a component of sampling representativeness, was determined by theoretical calculations and experimental measurement of relative variances in trace metals concentrations at each stage of both sampling procedures. The ASP significantly increased the reproducibility of soil sampling compared to the GSP. Larger relative variances occurred during field sampling for the ASP and during laboratory sampling for the GSP. They were due to subsample mass reduction without control over particle size. Relative theoretical and experimental variances were in agreement. However, large discrepancies were observed for all sampling stages of both procedures between absolute theoretical and experimental relative variances. In the case of Pb, theoretical calculations were closer to experimental measurements when using a calculated value of the liberation factor (l) based on mineralogical data rather than l =1. It was shown that the b-exponent had a large influence on theoretical variances. Increasing the estimate of b from 0.5 to 1 largely improved the agreement between theory and experiment. Finally, 99% of experimental relative variance was explained by sampling errors compared to analytical errors. [Copyright &y& Elsevier]

BayesicFitting is a comprehensive, general-purpose toolbox for simple and standardized model fitting. Its fitting options range from simple least-squares methods, via maximum likelihood to fully Bayesian inference, working on a multitude of available models. BayesicFitting is open source and has been in development and use since the 1990s. It has been applied to a variety of science applications, chiefly in astronomy. BayesicFitting consists of a collection of PYTHON classes that can be combined to solve quite complicated inference problems. Amongst the classes are models, fitters, priors, error distributions, engines, samples, and of course NestedSampler, our general-purpose implementation of the nested sampling algorithm. Nested sampling is a novel way to perform Bayesian calculations. It can be applied to inference problems, that consist of a parameterized model to fit measured data to. NestedSampler calculates the Bayesian evidence as the numeric integral over the posterior probability of (hyper)parameters of the problem. The solution in terms of the parameters is obtained as a set of weighted samples drawn from the posterior. In this paper, we emphasize nested sampling and all classes that are directly connected to it. Additionally, we present the fitters, which fit the data by the least-squares method or the maximum likelihood method. They can also calculate the Bayesian evidence as a Gaussian approximation. We will discuss the architecture of the toolbox. Which classes are present, what is their function, how they are related and implementational details where it gets complicated. • Python toolbox for generic model fitting. • Easy usage and open source. • Ready-made combinable models. • Least squares, maximum likelihood and Nested Sampling. • Evidence calculation. [ABSTRACT FROM AUTHOR]

The article discusses a research on the ways of how to deal with fear of statistics in researchers. It highlights the Consolidated Standards of Reporting Trials (CONSORT) Guidelines and Improved Consort guidelines to supply information about minimally important difference (MID). It also emphasizes the establishment of causality between the intervention in the relationship between the cause and the outcome.

The second article in this series on biostatistics covers the concepts of sample, population, research hypotheses and statistical errors. [ABSTRACT FROM AUTHOR]

Background Data errors, including sample swapping and mis-labeling, are inevitable in the process of large-scale omics data generation. Data errors need to be identified and corrected before integrative data analyses where different types of data are merged on the basis of the annotated labels. Data with labeling errors dampen true biological signals. More importantly, data analysis with sample errors could lead to wrong scientific conclusions. We developed a robust probabilistic multi-omics data matching procedure, proMODMatcher, to curate data and identify and correct data annotation and errors in large databases. Results Application to simulated datasets suggests that proMODMatcher achieved robust statistical power even when the number of cis -associations was small and/or the number of samples was large. Application of our proMODMatcher to multi-omics datasets in The Cancer Genome Atlas and International Cancer Genome Consortium identified sample errors in multiple cancer datasets. Our procedure was not only able to identify sample-labeling errors but also to unambiguously identify the source of the errors. Our results demonstrate that these errors should be identified and corrected before integrative analysis. Conclusions Our results indicate that sample-labeling errors were common in large multi-omics datasets. These errors should be corrected before integrative analysis. [ABSTRACT FROM AUTHOR]