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

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

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

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

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

The measurement error (ME), one of the serious kinds of non sampling errors, is the difference between the value of a characteristic provided by the respondent and the true value of it. Generally, in practice it is almost impossible to get the true value due to non availability of an appropriate and feasible method of measuring it. In fact, the assumption of getting information from a unit without any biases or measuring a unit correctly is very unrealistic. ME may give rise to serious bias and variable errors both in an estimator obtained through a survey. This work is mainly concentrated on the study of variations that may occur in certain components of ME models considered and their impact on some existing usual estimators of population mean along with a proposed family of exponential-type estimators which might be considered as an alternative to classical ratio/product estimators in some sense. Some important features of these estimators in presence of ME have been dealt with. Results discussed have been highlighted with the help of an empirical data which reflects the extent of consequences of changes in model parameters on the performance of estimators. [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]

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]

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]

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]

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]

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]

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]

High-precision entry navigation capability is essential for future Mars pinpoint landing missions. An augmented robust three-stage extended Kalman filter (ARThSEKF) for integrated navigation algorithm of Mars atmospheric entry with models containing parameter uncertainties and measurement errors is presented in this paper. The derivation is conducted, and the character of stability has also been analysed, in which it has been proved to be uniformly asymptotically stable. In the further simulation of Mars entry-phase navigation, ARThSEKF showed a good performance to compare with the standard extended Kalman filter. As the atmosphere density uncertainties and unknown measurement errors have been estimated precisely, the state estimation errors were controlled to a low level, of which the position and velocity were less than 100 m and 5 m/s, respectively. Therefore, ARThSEKF is suitable for dealing with non-linear systems in the presence of parameter uncertainties and unknown measurement errors, which can fulfil the requirement of future pinpoint Mars landing mission. [ABSTRACT FROM PUBLISHER]

In many applications of multilevel modeling, group-level (L2) variables for assessing group-level effects are generated by aggregating variables from a lower level (L1). However, the observed group mean might not be a reliable measure of the unobserved true group mean. In this article, we propose a Bayesian approach for estimating a multilevel latent contextual model that corrects for measurement error and sampling error (i.e., sampling only a small number of L1 units from a L2 unit) when estimating group-level effects of aggregated L1 variables. Two simulation studies were conducted to compare the Bayesian approach with the maximum likelihood approach implemented in Mplus. The Bayesian approach showed fewer estimation problems (e.g., inadmissible solutions) and more accurate estimates of the group-level effect than the maximum likelihood approach under problematic conditions (i.e., small number of groups, predictor variable with a small intraclass correlation). An application from educational psychology is used to illustrate the different estimation approaches. [ABSTRACT FROM PUBLISHER]

The main objective of this study was to quantify the impact of failing to account for measurement error on school compositional effects. Multilevel structural equation models were incorporated to control for measurement error and/or sampling error. Study 1, a large sample of English primary students in Years 1 and 4, revealed a significantly negative compositional effect associated with school-average achievement that became more negative after controlling for measurement error. Study 2, a large study of Cypriot primary students in Year 4, showed a small, positive but statistically significant compositional effect that became non-significant after controlling for measurement error. Our findings have important methodological, substantive, and theoretical implications for on-going debates on the school compositional effects on students’ outcomes, because nearly all previous research has been based on traditional approaches to multilevel models, which are positively biased, due to the failure to control for measurement error. [ABSTRACT FROM PUBLISHER]