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In this paper, the problems of time-delay, packet loss and quantisation for network control with global Lipschitz nonlinearity are investigated. The packet losses between the forward and backward channels are considered, and two independent Bernoulli distributions are modelled respectively. An appropriate compensation strategy is proposed to reduce the buffeting and response time of the system. A controller based on differential proportional observer is proposed. The information of estimation errors is used to optimise state estimations. Finally, a simulation example illustrates the feasibility of the method, and the comparisons with existing discrete time-delay system and distributed time-delay system prove the superiority of the method. [ABSTRACT FROM AUTHOR]

Modelling movement uncertainty is of profound significance in promoting effective trajectory analysis and mining. However, classic uncertainty models are limited by rigid assumptions on moving speed and distance, which ignores the stochastic nature of individual's travel behaviour. This study introduces a novel method using adaptive ellipses to represent the movement uncertainty in a planar space under the framework of time geography. Two models are established by considering different error sources in trajectory data. The first model captures the uncertainty caused by sampling error, and the second one further, takes the measurement error into account. The Minkowski distance metric is adopted to determine the size of uncertainty ellipses, in which the Minkowski parameter is optimized for each segment in the raw trajectory on the basis of the geometric characteristics extracted. Compared with state-of-the-art methods on five real-life trajectory datasets, the proposed method is proved to produce more effective uncertain regions, which significantly reduce the redundant uncertain area, while retaining at a comparative level of actual movement coverage. Given the heterogeneity of human mobility patterns, this study provides a robust and applicable solution for adaptively modelling individual's movement uncertainty, which is expected to benefit trajectory-related applications in various scenarios. [ABSTRACT FROM AUTHOR]

The Northern Northwest Coast of North America has consistently been an epicenter of hunter-gatherer research, but attention has focused on the period after 5000 cal BP. Using data largely produced within the past 12 years, we present the first synthesis and analysis of this region's 14C record between ∼11000 and 5000 cal BP. Our analysis produced two primary findings (i) that the number of dated sites does not increase or decrease significantly during this period and (ii) there is an unexplained gap in dated sites across the entire region between ∼9000 and 8300 cal BP. The existence of this gap has critical implications regarding hypotheses of technological change, migration, population continuity, and cultural evolution for the region. However, available explanations for this gap are problematic. Possible causes investigated include: demographic abandonment of the region, distorting effects of the calibration curve, sea-level change, taphonomy, and sampling error. All are found to be inadequate. [ABSTRACT FROM AUTHOR]

Traditional observed variable multilevel models for evaluating indirect effects are limited by their inability to quantify measurement and sampling error. They are further restricted by being unable to fully separate within- and between-level effects without bias. Doubly latent models reduce these biases by decomposing the observed within-level indicators into within- and between-level latent components. This decomposition simultaneously controls for both measurement error at each level, and sampling error in the aggregation of individual values that serve as indicators of higher-level constructs. We illustrate the usefulness of this approach in investigating the mediating role of aggressive attitudes in associations between reports of school climate and prevalence of teasing and bullying. Teacher perceived justness and support in schools were both found to be indirectly associated with the prevalence of teasing and bullying through aggressive attitudes, and teacher perceived support was found to be directly associated with teasing and bullying. [ABSTRACT FROM AUTHOR]

None of the polls predicted the winner of the 2019 Australian election, the first such failure since 1993 when all the polls started reporting a two-party preferred (2PP) vote estimate of the vote share as well as the parties' first preferences. But the idea that the polls had enjoyed a very good run until 2019 is misleading: from 1993 to 2016, a fifth had predicted the wrong winner. This paper examines the performance of the polls against several measures: the outcome; margins of error; size of the errors; and estimates of the gap between the Liberal-National Party (LNP) and Labor. It shows that about a third of the estimates of the 2PP vote, Labor's first preferences, and the LNP's first preferences, involved errors greater than those attributable to sampling error. [ABSTRACT FROM AUTHOR]

One of the main sources of non-sampling errors is missing data due to non-response. Various methods are adopted to deal with this problem, one of which is the method of imputation. In this paper, a few logarithmic and sine type imputation techniques have been proposed for estimating the population mean and the effect of measurement errors on the resultant estimators has been examined. Their properties in terms of bias and mean square errors have been studied. Empirical studies have been carried out on real and simulated data sets to show the efficiency of the proposed estimators over contemporary estimators. Suitable recommendations have been put forward to the survey statisticians for applications in real-life problems. [ABSTRACT FROM AUTHOR]

The Poisson INAR(1) one-sided exponentially weighted moving average (EWMA) chart has been proposed to monitor integer-valued autoregressive processes of order 1 with a Poisson marginal distribution. It is common to assume that the INAR(1) process parameters are known or can be accurately estimated. However, in practice, the in-control process mean and autocorrelation coefficient are typically unknown and must be estimated. In this article, we investigate the effect of parameter estimation on the run length properties of the Poisson INAR(1) one-sided EWMA chart with the use of bivariate Markov chain approach. It is shown from the conditional in-control and out-of-control average run length values with different design parameters under various shift magnitudes that the effect of parameter estimation error can be significant. The effect due to the process mean estimation error is stronger than that due to the autocorrelation coefficient. Moreover, practitioners should rarely expect the in-control performance to be close to that obtained under the assumption that process parameters are known. We also provide sample size-recommendations regarding marginal performance. This sample size depends on the different shift magnitudes. [ABSTRACT FROM PUBLISHER]

The paper proposes the use of data cloning (DC) to the estimation of general univariate asymmetric stochastic volatility (ASV) models with flexible distributions for the standardized returns. These models are able to capture the asymmetric volatility, the leptokurtosis and the skewness of the distribution of returns. Data cloning is a general technique to compute maximum likelihood estimators, along with their asymptotic variances, by means of a Markov chain Monte Carlo (MCMC) methodology. The main aim of this paper is to illustrate how easily general univariate ASV models can be estimated and consequently studied via data cloning. Changes of specifications, priors and sampling error distributions are done with minor modifications of the code. Using an intensive simulation study, the finite sample properties of the estimators of the parameters are evaluated and compared to those of a benchmark estimator that is also user-friendly. The results show that the proposed estimator is computationally efficient, and can be an effective alternative to the existing estimation methods applied to ASV models. Finally, we use data cloning to estimate the parameters of general ASV models and forecast the one-step-ahead volatility of S&P 500 and FTSE-100 daily returns. [ABSTRACT FROM AUTHOR]

Choropleth mapping provides a simple but effective visual presentation of geographical data. Traditional choropleth mapping methods assume that data to be displayed are certain. This may not be true for many real-world problems. For example, attributes generated based on surveys may contain sampling and non-sampling error, and results generated using statistical inferences often come with a certain level of uncertainty. In recent years, several studies have incorporated uncertain geographical attributes into choropleth mapping with a primary focus on identifying the most homogeneous classes. However, no studies have yet accounted for the possibility that an areal unit might be placed in a wrong class due to data uncertainty. This paper addresses this issue by proposing a robustness measure and incorporating it into the optimal design of choropleth maps. In particular, this study proposes a discretization method to solve the new optimization problem along with a novel theoretical bound to evaluate solution quality. The new approach is applied to map the American Community Survey data. Test results suggest a tradeoff between within-class homogeneity and robustness. The study provides an important perspective on addressing data uncertainty in choropleth map design and offers a new approach for spatial analysts and decision-makers to incorporate robustness into the mapmaking process. [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]

This paper provides a finite sample bound for the error term in the Edgeworth expansion for a sum of independent, potentially discrete, non-lattice random vectors, using a uniform-in-P version of the weaker Cramér condition in [Angst J, Poly G. A Weak Cramèr Condition and Application to Edgeworth Expansions. Electronic J Prob. 2017;22:1–24]. This finite sample bound can be used to derive an Edgeworth expansion that is uniform over the distributions of the random vectors. Using this result, we derive a uniform-in-P higher-order expansion of resampling-based distributions. [ABSTRACT FROM AUTHOR]

A novel identification algorithm is proposed to deal with the modelling issues of linear switched systems under the switching modes being unknown. The main contributions of this paper embody the following aspects: (1) for simplifying the identification problems, the label functions are proposed to replace the switching states of the modes; (2) The recursive least square algorithm is used to identify the system parameters, and the convergence performance of the proposed algorithm is studied. By using convergence analysis, the parameter estimation error can converge to zero point when the input signals satisfy persistent excited conditions and the total time of mode mismatch is limited. Finally, the effectiveness of the proposed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

The use of small and reliable samples in a microscopic analysis can decrease the time to estimate the particle size and mineral grade distributions in a population of particles. This paper attempts to assess the minimum reliable sample size for the above-mentioned distributions, by implementing non-parametric tests on the subsamples taken from a specific population of 2800 particles. The Kolmogorov-Smirnov tests show that the subsamples which contain more than 800 particles (29% of the population) cannot give cumulative distributions of particle size and mineral grade different from their equivalents in the population. On the other hand, Chi-square tests show that 1500–1700 particles (54–61% of the population) may be sufficient to estimate the discrete distributions in the population. The minimum reliable sample size increases with an enlarging population because the sampling errors should increase while taking samples of equal size from larger populations. Nevertheless, the minimum reliable size should be 29–32% of the population if the distributions of mineral grade and particle size in the population are fitted to bimodal and normal distributions, respectively. [ABSTRACT FROM AUTHOR]

In point-based sensing systems such as coordinate measuring machines and laser ultrasonics where complete sensing is impractical due to the high sensing time and cost, adaptive sensing through a systematic exploration is vital for online inspection and anomaly quantification. Most of the existing sequential sampling methodologies focus on reducing the overall fitting error for the entire sampling space. However, in many anomaly quantification applications, the main goal is to estimate sparse anomalous regions at pixel-level accurately. In this article, we develop a novel framework named Adaptive Kernelized Maximum-Minimum Distance ( AKM 2 D) to speed up the inspection and anomaly detection process through an intelligent sequential sampling scheme integrated with fast estimation and detection. The proposed method balances the sampling efforts between the space-filling sampling (exploration) and focused sampling near the anomalous region (exploitation). The proposed methodology is validated by conducting simulations and a case study of anomaly detection in composite sheets using a guided wave test. [ABSTRACT FROM AUTHOR]

We consider the regression learning based on the moving least-squares framework for the polynomially β-mixing samples. The rigorous error analysis is carried out by using the independent-blocks technique in the sample error estimates. We derive the satisfactory learning rate that can be arbitrarily close to the best rate O (m − 1) , when the sequence of marginal distributions converges exponentially to some marginal distribution in the dual of a Hölder space. [ABSTRACT FROM AUTHOR]

Intelligence tests and adaptive behavior scales measure vital aspects of human functioning. Assessment of each is a required component in the diagnosis or identification of intellectual disability. The present study investigated the population correlation between intelligence and adaptive behavior using psychometric meta-analysis. The main analysis included 148 samples with 16,468 participants. Following correction for sampling error, measurement error, and range departure, analysis resulted in a population correlation of ρ =.51. The most pertinent moderator analysis indicated that the relation between intelligence and adaptive behavior tended to decrease as IQ increased. The theoretical prevalence of intellectual disability is affected not only by IQ and adaptive behavior cut scores but also by the correlation between the two; thus, these findings inform practice and policy related to eligibility criteria and prevalence of intellectual disability. [ABSTRACT FROM AUTHOR]

Estimating an optimal individualized treatment rule (ITR) based on patients' information is an important problem in precision medicine. An optimal ITR is a decision function that optimizes patients' expected clinical outcomes. Many existing methods in the literature are designed for binary treatment settings with the interest of a continuous outcome. Much less work has been done on estimating optimal ITRs in multiple treatment settings with good interpretations. In this article, we propose angle-based direct learning (AD-learning) to efficiently estimate optimal ITRs with multiple treatments. Our proposed method can be applied to various types of outcomes, such as continuous, survival, or binary outcomes. Moreover, it has an interesting geometric interpretation on the effect of different treatments for each individual patient, which can help doctors and patients make better decisions. Finite sample error bounds have been established to provide a theoretical guarantee for AD-learning. Finally, we demonstrate the superior performance of our method via an extensive simulation study and real data applications. for this article are available online. [ABSTRACT FROM AUTHOR]

Multivariate skewness and kurtosis were defined by Mardia. However, the distribution of multivariate normality test statistics based on skewness and kurtosis is only obtainable for large samples. In this paper, we propose a normalizing transformation statistic for Mardia's multivariate kurtosis. The accuracy of the normal approximation of this statistic, i.e. its expectation, variance, skewness, kurtosis, sample error Type I, and power for chosen alternative distributions, was investigated by Monte Carlo simulations for given sizes and dimensions. [ABSTRACT FROM AUTHOR]

The optimal predictive rule is the conditional response function Graph HT ht . Start with a set of I n i training samples, and divide them into I K i evenly sized and non-overlapping folds Graph HT ht . We have access to a stream independent and identically distributed samples Graph HT ht with Graph HT ht and Graph HT ht . [Extracted from the article]

Good particulate products require that particle segregation has been minimized. Moreover representative sampling is essential for adequate quality control of such products. This requires optimum design of procedures and equipment for both sampling and measurement. Representative test samples primarily require that all constituent particles have had equal chance of being taken from a lot of material, given their proportion. Moreover, a sufficient number of particles is necessary for good precision. This article discusses and exemplifies various potential errors during collection of small amounts from powder lots and during in-situ and on-line measurement, so that optimum consideration can be given to the design of adequate analytical procedures. It should be realized that the errors during sampling of particulate materials are very similar to those during collection of small portions for production of pharmaceutical tablets. [ABSTRACT FROM AUTHOR]

Examinees who attempt the same test multiple times are often referred to as "repeaters." Previous studies suggested that repeaters should be excluded from the total sample before equating because repeater groups are distinguishable from non-repeater groups. In addition, repeaters might memorize anchor items, causing item drift under a non-equivalent anchor test (NEAT) design. However, under small sample equating conditions, removing repeaters might lead to smaller sample size, which increases sampling errors. Therefore, three solutions were investigated in the current study: 1) excluding repeaters, 2) excluding drifted anchor items, and 3) applying Rasch true score equating to maintain the population invariance if repeaters were removed. The results suggested excluding repeaters if the anchor were exposed to them. Circle arc equating can be applied if it is impossible to exclude all drifted anchor items. Applying Rasch true did outperform solutions. [ABSTRACT FROM AUTHOR]

Acceptance sampling plans are used to determine whether production lots can be accepted or rejected. Existing tools only provide a limited functionality for the two-point design and the risk analysis of such plans. In this article, a web-based tool is presented to study single- and double-stage sampling plans. In contrast to existing solutions, the tool is an interactive applet that is freely available. Analytic properties are derived to support the development of search strategies for the design of double-stage sampling plans that are more efficient and accurate in comparison with existing routines. Several case studies are presented. [ABSTRACT FROM AUTHOR]

The control charts are widely used to control the process stability, and then analyse the 'in-control' data in the statistical process control. However, an 'in-control' process is not enough since it can produce non-conforming or out of specification products. Process capability indices, like Cp, Cpu, Cpl and Cpk, have been proposed to make decisions on how well the process meets specifications in practice. These indices can not be straightforwardly used for illustrating and analysing the multi-process products. Hence, the multi-process performance analysis chart (MPPAC) has been proposed. Nevertheless, the MPPAC approach is not reliable since sampling errors are ignored, and PCAC is only just suitable for full inspection. To overcome these shortcomings, this paper aims to develop an advanced multi-process performance analysis chart (AMPPAC) with process capability zones as well as taking into account interval estimates of these indices for precisely measuring an entire product composed of symmetric tolerances, larger-the-better and smaller-the-better characteristics. Besides, the issue about independent processes has been proposed by Chen et al. (Chen, K.S., Huang, M.L. and Li, R.K., Process capability analysis for an entire product. Int. J. Prod. Res., 2001, 39(17), 4077-4087.), and the solution about dependent processes will be provided in this paper. Thus, the AMPPAC can reliably and appropriately assess the multi-process performance and provide chances for continuous improvement on the manufacturing process. [ABSTRACT FROM AUTHOR]

It is shown that the maximum likelihood estimator of the widely used omega coefficient for reliability of multicomponent measuring instruments converges almost surely to the population reliability coefficient for normal congeneric measures with uncorrelated errors as sample size increases indefinitely. This strong consistency implies convergence in probability (consistency) as well as in distribution for the omega estimator. Strong consistency is also demonstrated for the maximal reliability estimator associated with the optimal linear combination of the instrument components. The findings of this note add (i) to the recommendation to use in the general normality case the omega estimator in empirical research, (ii) to the critical literature on the popular coefficient alpha then, and (iii) to the literature on the properties of the optimal linear combination of observed measures and the maximal reliability estimator. [ABSTRACT FROM AUTHOR]

To analyze the efficiency of area estimations (i.e. estimation accuracy and variation of estimation) impacted by crop mapping error, we simulated error at eight levels for thematic maps using a stratified sampling estimation methodology. The results show that the estimation efficiency is influenced by the combination of the sample size and the error level. Evaluating the trade-offs between sample size and error level showed that reducing the crop mapping error level provides the most benefit (i.e. higher estimation efficiency). Further, sampling performance differed based on the heterogeneity of the crop area. The results demonstrated that the influence of increasing the error level on estimation efficiency is more detrimental in heterogeneous areas than in homogeneous ones. Therefore, to obtain higher estimation efficiency, a larger sample size and lower error level or both are needed, especially in heterogeneous areas. We suggest that existing land-cover maps should first be used to determine the heterogeneity of the area. The appropriate sample size for these areas then can be determined according to all three factors: heterogeneity, expected estimation efficiency, and sampling budget. Overall, extending our understanding of the impacts of crop mapping error is necessary for decision making to improve our ability to effectively estimate crop area. [ABSTRACT FROM AUTHOR]

In this article, using purely and two-stage sequential procedures, the problem of minimum risk point estimation of the reliability parameter (R) under the stress–strength model, in case the loss function is squared error plus sampling cost, is considered when the random stress (X) and the random strength (Y) are independent and both have exponential distributions with different scale parameters. The exact distribution of the total sample size and explicit formulas for the expected value and mean squared error of the maximum likelihood estimator of the reliability parameter under the stress–strength model are provided under the two-stage sequential procedure. Using the law of large numbers and Monte Carlo integration, the exact distribution of the stopping rule under the purely sequential procedure is approximated. Moreover, it is shown that both proposed sequential procedures are finite and for special cases the exact distribution of stopping times has a degenerate distribution at the initial sample size. The performances of the proposed methodologies are investigated with the help of simulations. Finally, using a real data set, the procedures are clearly illustrated. [ABSTRACT FROM AUTHOR]

In an on-demand testing program, some items are repeatedly used across test administrations. This poses a risk to test security. In this study, we considered a scenario wherein a test was divided into two subsets: one consisting of secure items and the other consisting of possibly compromised items. In a simulation study of multistage adaptive testing, we used three methods to detect item preknowledge: a predictive checking method (PCM), a likelihood ratio test (LRT), and an adapted Kullback–Leibler divergence (KLD-A) test. We manipulated four factors: the proportion of compromised items, the stage of adaptive testing at which preknowledge was present, item-parameter estimation error, and the information contained in secure items. The type I error results indicated that the LRT and PCM methods are favored over the KLD-A method because the KLD-A can experience large inflated type I error in many conditions. In regard to power, the LRT and PCM methods displayed a wide range of results, generally from 0.2 to 0.8, depending on the amount of preknowledge and the stage of adaptive testing at which the preknowledge was present. [ABSTRACT FROM AUTHOR]

For passage-based tests, items that belong to a common passage often violate the local independence assumption of unidimensional item response theory (UIRT). In this case, ignoring local item dependence (LID) and estimating item parameters using a UIRT model could be problematic because doing so might result in inaccurate parameter estimates, which, in turn, could impact the results of equating. Under the random groups design, the main purpose of this article was to compare the relative performance of the three-parameter logistic (3PL), graded response (GR), bifactor, and testlet models on equating passage-based tests when various degrees of LID were present due to passage. Simulation results showed that the testlet model produced the most accurate equating results, followed by the bifactor model. The 3PL model worked as well as the bifactor and testlet models when the degree of LID was low but returned less accurate equating results than the two multidimensional models as the degree of LID increased. Among the four models, the polytomous GR model provided the least accurate equating results. [ABSTRACT FROM AUTHOR]

This paper proposes a method of obtaining the shape and the size of irregular rough particles, which are abundant in particle fields. The approach uses the infocused image of the particle to determine its size, and an improved relation between the central peak of the 2D-autocorrelation of its defocused image and its size to obtain the shape. An interferometric particle imaging system with double receivers is established to simultaneously collect the infocused image and defocused image. Experiments confirm the effectiveness of the proposed algorithm with a measurement relative error on the sample particles not exceeding 10.4%. In general, this method can offer significant perspectives on the characterization of particle fields. [ABSTRACT FROM AUTHOR]

S-transform (ST) is an extension of wavelet transform (WT) and short-time Fourier transform (STFT). The ST is depended on a scalable and moving Gaussian window. It overcomes the low-resolution factor of STFT and tackles with a lack of phase in WT. The ST is a useful tool for signal processing and analysis. In this paper, the multiresolution analysis (MRA) related with ST is introduced and after that a sampling theorem associated with ST is proposed which is based on multiresolution subspace. Additionally, truncation and aliasing error generated due to the sampling process, are also derived. The theoretical determinations are exhibited and validated using simulation results. [ABSTRACT FROM AUTHOR]

Introduction: With an increase in the worldwide prevalence of obesity, the incidence of non-alcoholic fatty liver disease (NAFLD) has been on the rise, such that it has been recently considered to be a major public health concern. Traditional interventions, such as lifestyle modifications, regular exercise, and healthy diet, have been significant in improving NAFLD with reduction of liver fat. Areas covered: Although liver biopsy is still the gold standard for diagnosis of NAFLD, there is a need for non-invasive, quantitative assessments of hepatic steatosis, especially in clinical trials of anti-steatotic medications or in the follow-up of patients undergoing lifestyle modifications. Liver biopsy has various shortcomings, such as invasive nature, risk of complications and possibility of sampling error. Therefore, it is impractical to use liver biopsy routinely in patients with NAFLD, clearly indicating the need for non-invasive and accurate diagnostic methods. Recently, controlled attenuation parameter (CAP) and magnetic resonance imaging–proton density fat fraction (MRI–PDFF) have been employed in various studies to monitor the dynamic changes of hepatic steatosis in response to treatment in patients with NAFLD. Expert commentary: Although further validations are required, CAP and MRI–PDFF could be used as potential diagnostic and monitoring tools in clinical setting. [ABSTRACT FROM AUTHOR]

The paper provides a method for generating epoch estimates for time series survey data, allowing for different periods of time (or even point estimates) according to user demand. The method uses a modified kriging estimator, which suppresses the contribution of sampling error variability in order to guarantee that custom epoch estimates have an interpolation property. For the veteran population variable of the American Community Survey, we utilize a simple Brownian Motion model of the population process and derive the modified kriging estimator for this case. The tuning parameters of this population model can be calibrated to the data via simple formulas. We illustrate the application of this method to the generation of point estimates of veteran population, an important objective for Veterans Affairs. [ABSTRACT FROM AUTHOR]

Evaluating the importance and the strength of empirical evidence requires asking three questions: First, what are the practical implications of the findings? Second, how precise are the estimates? Confidence intervals provide an intuitive way to communicate precision. Although nontechnical audiences often misinterpret confidence intervals (CIs), I argue that the result is less dangerous than the misunderstandings that arise from hypothesis tests. Third, is the model correctly specified? The validity of point estimates and CIs depends on the soundness of the underlying model. [ABSTRACT FROM AUTHOR]

Extraordinary amounts of data are being produced in many branches of science. Proven statistical methods are no longer applicable with extraordinary large datasets due to computational limitations. A critical step in big data analysis is data reduction. Existing investigations in the context of linear regression focus on subsampling-based methods. However, not only is this approach prone to sampling errors, it also leads to a covariance matrix of the estimators that is typically bounded from below by a term that is of the order of the inverse of the subdata size. We propose a novel approach, termed information-based optimal subdata selection (IBOSS). Compared to leading existing subdata methods, the IBOSS approach has the following advantages: (i) it is significantly faster; (ii) it is suitable for distributed parallel computing; (iii) the variances of the slope parameter estimators converge to 0 as the full data size increases even if the subdata size is fixed, that is, the convergence rate depends on the full data size; (iv) data analysis for IBOSS subdata is straightforward and the sampling distribution of an IBOSS estimator is easy to assess. Theoretical results and extensive simulations demonstrate that the IBOSS approach is superior to subsampling-based methods, sometimes by orders of magnitude. The advantages of the new approach are also illustrated through analysis of real data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

We consider the reconstruction of a bandlimited function from its finite localized sample data. Truncating the classical Shannon sampling series results in an unsatisfactory convergence rate due to the slow decay of the sinc function. To overcome this drawback, a simple and highly effective method, called the Gaussian regularization of the Shannon series, was proposed in the engineering and has received remarkable attention. It works by multiplying the sinc function in the Shannon series with a regularized Gaussian function. Recently, it was proved that the upper error bound of this method can achieve a convergence rate of the order , where 0 < δ < π is the bandwidth and n the number of sample data. The convergence rate is by far the best convergence rate among all regularized methods for the Shannon sampling series. The main objective of this article is to present the theoretical justification and numerical verification that the convergence rate is optimal when 0 < δ < π/2 by estimating the lower error bound of the truncated Gaussian regularized Shannon sampling series. [ABSTRACT FROM AUTHOR]

Early application timing estimation is essential in decision making during design space exploration of heterogeneous embedded systems in terms of hardware platform dimensioning and component selection. The decisions which have the impact on project duration and cost must be made before a platform prototype is available and software code is ready to be linked and thus timing estimation must be done using high-level models and simulators. Because of the ever increasing need to shorten the time to market, reducing the amount of time required to obtain the results is as important as achieving high estimation accuracy. In this paper, we propose a novel approach to source-level timing estimation with the aim to close the speed-accuracy gap by raising the level of abstraction and improving result reusability. We introduce a concept - elementary operations as distinct parts of source code which enable capturing platform behaviour without having the exact model of the processor pipeline, cache etc. We also present a timing estimation method which relies on elementary operations to craft hardware profiling benchmark and to build application and platform profiles. Experiments show an average estimation error of 5%, with maximum below 16%. [ABSTRACT FROM AUTHOR]

The Cauchy problem for the Helmholtz equation is considered. This problem is severely ill-posed, that is, the solution does not depend continuously on the data. To solve the problem numerically a mollification method is proposed. Convergence on error estimates between the exact solution and its approximation are obtained. Some numerical examples are given to show that the method works effectively. [ABSTRACT FROM AUTHOR]

We study in this work the mixed -filtering problem for Markov jump linear systems in a partial observation context. Rather than the Markov chain , we consider that only an estimation coming from a detector is available to the filter. We present a sufficient condition for the synthesis of a filter depending only on such that the estimation errors for the and filtering problems are bounded by given scalars. The robust mixed filtering considering that the transition and detection probabilities are uncertain, as well as the complete observation, cluster and mode-independent cases, are also studied. The results are given in terms of linear matrix inequalities and are illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Estimates of Graph HT ht did not improve after considering parceling variability. The current study evaluated UPI and LMS via Monte Carlo simulations when parcels are used as measurement indicators. Two popular LIM methods, the unconstrained product indicator (UPI; Marsh et al., [3]) and the latent moderated structural equation modeling (LMS; Klein & Moosbrugger, [2]), have performed well regarding interaction effect estimates with normal and nonnormal latent predictors. [Extracted from the article]

In this study, a fully discrete defect correction finite element method for the unsteady incompressible Magnetohydrodynamics (MHD) equations, which is leaded by combining the Back Euler time discretization with the two-step defect correction in space, is presented. It is a continuous work of our formal paper [Math Method Appl Sci. 2017. DOI:10.1002/mma.4296]. The defect correction method is an iterative improvement technique for increasing the accuracy of a numerical solution without applying a grid refinement. Firstly, the nonlinear MHD equation is solved with an artificial viscosity term. Then, the numerical solutions are improved on the same grid by a linearized defect-correction technique. Then, we introduce the numerical analysis including stability analysis and error analysis. The numerical analysis proves that our method is stable and has an optimal convergence rate. Some numerical results [see Math Method Appl Sci. 2017. DOI:10.1002/mma.4296] show that this method is highly efficient for the unsteady incompressible MHD problems. [ABSTRACT FROM AUTHOR]

Over the last decade or two, multilevel structural equation modeling (ML-SEM) has become a prominent modeling approach in the social sciences because it allows researchers to correct for sampling and measurement errors and thus to estimate the effects of Level 2 (L2) constructs without bias. Because the latent variable modeling software Mplus uses maximum likelihood (ML) by default, many researchers in the social sciences have applied ML to obtain estimates of L2 regression coefficients. However, one drawback of ML is that covariance matrices of the predictor variables at L2 tend to be degenerate, and thus, estimates of L2 regression coefficients tend to be rather inaccurate when sample sizes are small. In this article, I show how an approach for stabilizing covariance matrices at L2 can be used to obtain more accurate estimates of L2 regression coefficients. A simulation study is conducted to compare the proposed approach with ML, and I illustrate its application with an example from organizational research. [ABSTRACT FROM AUTHOR]

The miscible displacement problem of one incompressible fluid is modelled by a nonlinear coupled system of two partial differential equations in porous media. One equation is elliptic form for the pressure and the other equation is parabolic form for the concentration of one of the fluids. In the paper, we present an efficient two-grid method for solving the miscible displacement problem by using mixed finite-element method for the approximation of the pressure equation and standard Galerkin method for concentration equation. We linearize the discretized equations based on the idea of Newton iteration in our methods, firstly, we solve an original nonlinear coupling problem on the coarse grid, then solve two linear systems on the fine grid. we obtain the error estimates for the two-grid algorithm, it is shown that coarse space can be extremely coarse and we achieve asymptotically optimal approximation. Moreover, numerical experimentation is given in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a new recursive identification method which can efficiently estimate time-varying parameters in discrete time systems and has significant advantages over standard recursive least-squares (RLS) method. This new information-weighted recursive algorithm for time-varying systems has three novel features, discounting of inaccurate estimates through weighting by the Information matrix, using the reuse of past data in computing current parameter estimates, a new tuneable damping factor parameter and a precisely designed compensation term to neutralise the estimation error caused by time-varying coefficients. A rigorous proof of convergence is also provided. Simulations show that the new algorithm significantly outperforms standard RLS, exhibiting better tracking performance and faster convergence. Flight tests on a T-REX 800 helicopter Unmanned Aerial Vehicle platform show that it gives system parameter estimates that are accurate enough and converge quickly enough that flight controllers can be designed in real-time based on the online identified model. [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]

Data envelopment analysis (DEA) frequently uses stochastic input and/or output data. If these data are estimated from a sample in each decision-making unit, the DEA efficiency will be an estimation of the obtained efficiency if the population information is available. We propose a methodology to determine the relationship between the sample size and the estimation error of the efficiency in the presence of output data estimated with a sample. The practical utility of this result is to evaluate, with fixed precision, the efficiency of a set of making units, taking deterministic inputs that explain the opinion-satisfaction of the unit users, whose opinion is known through a sampling survey. We illustrate how to apply the proposed research with a case study. [ABSTRACT FROM AUTHOR]

As a well-known method for selecting representative samples of populations, ranked set sampling (RSS) has been considered increasingly in recent years. This (RSS) method has proved to be more efficient than the usual simple random sampling (SRS) for estimating most of the population parameters. In order to have a more efficient estimate of the population mean, a new sampling scheme called as robust extreme double ranked set sampling (REDRSS) is introduced and investigated in this paper. A simulation study shows that using REDRSS scheme gives more efficient estimates of population mean with smaller variance than the usual SRS, RSS and most other sampling schemes based on RSS estimators in non-uniform (symmetric or non-symmetric) distributions. [ABSTRACT FROM AUTHOR]

In this contribution, a new framework for -optimal reduction is presented, motivated by the local nature of both (tangential) interpolation and -optimal approximations. The main advantage is given by a decoupling of the cost of reduction from the cost of optimization, resulting in a significant speedup in -optimal reduction. In addition, a middle-sized surrogate model is produced at no additional cost and can be used e.g. for error estimation. Numerical examples illustrate the new framework, showing its effectiveness in producing -optimal reduced models at a far lower cost than conventional algorithms. Detailed discussions and optimality proofs are presented for applying this framework to the reduction of multiple-input, multiple-output linear dynamical systems. The paper ends with a brief discussion on how this framework could be extended to other system classes, thus indicating how this truly is a general framework for interpolatory reduction. [ABSTRACT FROM AUTHOR]

This project identifies some difficulties when analyzing single-case data and showcases a new method, dynamic multilevel analysis (DMA). We re-analyze a published, meta-analysis of single-case interventions for participants with autism. Analytic difficulties include missing data, nested data, baseline trends, time periods, recency effects, many hypotheses’ false positives, interactions among explanatory variables, indirect effects (including false negatives), and sampling errors. Furthermore, non-overlapping analyses can yield contested results, overvalue data near overlap boundaries, lose statistical power, and lack estimates of explained variance or unexplained residuals. To address these difficulties, DMA integrates several methods, including multilevel and time-series analyses. DMA re-analysis not only showed robust intervention effects, but also time-, outcome-, and intervention component-specific effects. Moreover, DMA informs the suitability of time hypotheses or meta-analysis, and DMA’s components can be used separately, notably its time-series analyses for small samples (e.g., one participant). Hence, DMA can help researchers analyze single-case data more accurately. [ABSTRACT FROM AUTHOR]

In this paper, an admittance control scheme for a user-in-charge exoskeleton is presented. The controller basically consists of a composite adaptive controller implementing a feedback law to estimate the structured uncertainties and to modify the apparent dynamics of the robot, and an LWPR estimator which tries to give an appropriate approximation of unmodeled uncertainty along with a robust term aiming to overcome the approximation residue. The control scheme offers a unified general control structure that explains the effect of each control component on the others. It is proved that based on the developed controller, the tracking and estimation errors converge to small boundaries with ultimate boundedness property due to the presence of the unstructured uncertainty. Based on simulations of a 2-DOF leg, the effectiveness of the controller is investigated. The results show the effectiveness of employing a universal approximator alongside a robust adaptive control and the success of the recommended approach in estimating model parameters and unmodeled dynamics simultaneously. [ABSTRACT FROM AUTHOR]

The publication proposes a non-linear method for determining the pre-crash velocity of the 'Luxury' vehicle class as defined by the National Highway Traffic Safety Administration database. Existing methods of vehicle velocity evaluation are based on the linear relationship between the energy equivalent speed and the value of the car body deformation coefficient Cs, which lead to a significant estimation error, rendering them insufficient for car crash experts. The non-linear model includes linearisation in particular ranges, which may decrease the error value. The most important element in the non-linear method is the bk coefficient, which is calculated based on polynomial regression equations. The article presents an optimisation iteration, which procures accurate results with a reduced computation time for the 'Luxury' vehicle class. [ABSTRACT FROM AUTHOR]