Existence Of Measurement Errors Research Articles

A Cross-matching Service for Data Center of Xinjiang Astronomical Observatory

Cross-matching is a key technique to achieve fusion of multi-band astronomical catalogs. Due to different equipment such as various astronomical telescopes, the existence of measurement errors, and proper motions of the celestial bodies, the same celestial object will have different positions in different catalogs, making it difficult to integrate multi-band or full-band astronomical data. In this study, we propose an online cross-matching method based on pseudo-spherical indexing techniques and develop a service combining with high performance computing system (Taurus) to improve cross-matching efficiency, which is designed for the Data Center of Xinjiang Astronomical Observatory. Specifically, we use Quad Tree Cube to divide the spherical blocks of the celestial object and map the 2D space composed of R.A. and decl. to 1D space and achieve correspondence between real celestial objects and spherical patches. Finally, we verify the performance of the service using Gaia 3 and PPMXL catalogs. Meanwhile, we send the matching results to VO tools-Topcat and Aladin respectively to get visual results. The experimental results show that the service effectively solves the speed bottleneck problem of cross-matching caused by frequent I/O, and significantly improves the retrieval and matching speed of massive astronomical data.

On classes of robust estimators in presence of correlated measurement errors

It is an established fact that the existence of measurement errors (ME) vitiates the conventional properties of the estimators. The impact of correlated measurement errors (CME) was first assessed by Shalabh and Tsai (2017) using classical ratio and product estimators. This article makes a modest attempt to suggest some classes of robust estimators of population mean in the presence of CME. The bias and mean square error of the suggested estimators are reported with the first-order approximation and the efficiency conditions are derived. The theoretical results are extended with the help of a simulation study executed over a hypothetically rendered population. Apart from this, the effect of ME on the suggested estimators is also studied both theoretically and empirically.

Research on Positioning Accuracy of Passive Acoustic Positioning System Based on Feature Matching in Air

Passive acoustic localization is an important interdisciplinary research branch which combines acoustics, communication and signal processing. Passive acoustic positioning system, as an important part of battlefield sensing and monitoring system, has been paid more and more attention. Due to the disturbance of battlefield and other environments and the existence of measurement errors, the positioning accuracy often cannot meet the requirements. Passive acoustic positioning system, as an important part of battlefield sensing and monitoring system, has been paid more and more attention. Due to the disturbance of battlefield and other environments and the existence of measurement errors, the positioning accuracy often cannot meet the requirements. In multi-sensor passive location, the measured data of each array element is not only related, but also compensatory and redundant, so some people use the optimal data fusion algorithm as post-processing algorithm to improve the positioning accuracy. In this paper, the positioning accuracy of ground passive acoustic positioning system is analyzed based on feature matching method, and some useful conclusions are drawn.

Bayesian EWMA control chart with measurement error under different loss functions

AbstractThis paper presents the Bayesian EWMA control chart under two different loss functions (i) squared error loss function (SELF) and (ii) linex loss function (LLF) in the presence of measurement error (ME). We take posterior and posterior predictive distribution under the conjugate prior. We used a linear covariate model in the existence of ME to evaluate the control chart. We also studied the effects of multiple measurements and linear increasing variance methods in the existence of the ME. The average run length and standard deviation of run length are used to measure the performance of the Bayesian EWMA control chart with ME. We conducted the Monte Carlo simulation study to evaluate the performance of Bayesian EWMA control chart with ME. A real‐life data example is also presented to demonstrate the application of the control chart.

COMPARISONS OF SOME BIASED ESTIMATORS FOR LINEAR MEASUREMENT ERROR MODELS

Measurement errors are very often come upon in data analysis. Classical statistical methods become disadvantageous and ordinary least squares estimator of parameters turns into inconsistent and biased, in the existence of measurement errors in the data. Although some methods are used, the performances of them are not good enough in the presence of multicollinearity and measurement errors in the data, simultaneously. That’s why researchers have been inquiring about the estimation of the parameters if the measurement error models have multicollinearity, lately. Especially, biased estimation techniques have been researched in the existence of multicollinearity for measurement error models recently. In this paper, the ridge and Liu estimation approaches to the measurement error models in the existence of multicollinearity are investigated. The comparisons of the biased estimators’ performances are analyzed theoretically and numerically.

Boundary Shape Inversion of Two-Dimensional Steady-State Heat Transfer System Based on Finite Volume Method and Decentralized Fuzzy Adaptive PID Control

A shape identification scheme was developed to determine the geometric shape of the inaccessible parts of two-dimensional objects using the measured temperatures on their accessible surfaces. The finite volume method was used to calculate the measured point’s temperature in the forward problem. In the inversion problem, the decentralized fuzzy adaptive Proportion Integral Differential (PID) control (DFAC) algorithm was used to compensate for the inversion boundary by using the difference between the measurement temperature and the calculation temperature. More accurate inversion results were obtained by introducing the weighted and synthesized normal distribution. In the inversion problem, the effects of the initial guess, the number of measuring points, and the measurement error were studied. The experiment calculation and analysis showed that the methods adopted in this paper still maintain good validity and accuracy with different initial guesses and decrease the number of measuring points and the existence of measurement errors.

Inversion of Thermal Conductivity in Two-Dimensional Unsteady-State Heat Transfer System Based on Finite Difference Method and Artificial Bee Colony

Based on the finite difference method and the artificial bee colony algorithm, the thermal conductivity in the two-dimensional unsteady-state heat transfer system is deduced. An improved artificial bee colony algorithm (IABCA), that artificial bee colony algorithm (ABCA) coupled with calculated deviation feedback, is proposed to overcome the shortcomings of insufficient local exploitation capacity and slow convergence rate in the late stage of the artificial bee colony algorithm (ABCA). For the forward problems, the finite difference method (FDM) is used to calculate the required temperature value of a discrete point; for the inverse problems, the IABCA is applied to minimize the objective function. In the inversion problem, the effects of colony size, number of measuring points, and the existence of measurement errors on the results are studied, and the inversion convergence rate of IABCA and ABCA is compared. The results demonstrate that the methods adopted in this paper had good effectiveness and accuracy even if colony sizes differ and measurement errors exist; and that IABCA has a more efficient convergence rate than ABCA.

Statistical inference for varying-coefficient partially linear errors-in-variables models with missing data

The purpose of this paper is twofold. First, we investigate estimations in varying-coefficient partially linear errors-in-variables models with covariates missing at random. However, the estimators are often biased due to the existence of measurement errors, the bias-corrected profile least-squares estimator and local liner estimators for unknown parametric and coefficient functions are obtained based on inverse probability weighted method. The asymptotic properties of the proposed estimators both for the parameter and nonparametric parts are established. Second, we study asymptotic distributions of an empirical log-likelihood ratio statistic and maximum empirical likelihood estimator for the unknown parameter. Based on this, more accurate confidence regions of the unknown parameter can be constructed. The methods are examined through simulation studies and illustrated by a real data analysis.

The performance of the adaptive EWMA median chart in the presence of measurement error

AbstractIn quality control applications, the control chart is a powerful tool but its performance is adversely affected by the contamination from either the inspector or the measuring device leading to measurement errors. In this paper, we investigate the performance of the AEWMA median chart with measurement errors, and a methodology is proposed to obtain the optimal parameters by considering a linearly covariate error model. Several figures and tables show that, with the existence of measurement errors, the efficiency of the AEWMA median chart can be strongly affected, but this negative effect can be compensated by taking multiple measurements at each sample point. Comparisons with the Shewhart and the classical EWMA schemes confirm the superiority of the AEWMA scheme for detecting a wide range of shifts in the case of precise and imprecise data. An example is provided to illustrate the use of the AEWMA median chart with measurement errors.

Robustness Analysis of Asynchronous Sampled-Data Multiagent Networks With Time-Varying Delays

In this paper, we study the simultaneous stability problem of a finite number of locally interconnected linear subsystems under practical constraints, including asynchronous and aperiodic sampling, time-varying delays, and measurement errors. We establish a new Lyapunov-based stability result for such a decentralized system. This system has a particular simple structure of interconnections, but it captures some key characteristics of a large class of intermediate models derived from the consensus analysis of multiagent systems. The asynchrony of aperiodic sampling and the existence of measurement errors allow the utilization of some kinds of quantizing devices, such as Logarithmic quantizers, in the process of data sampling, and allow the introduction of a period of dwell time after each update of state measurement to eliminate the Zeno behavior of events in event-based control. The stability result is applicable to the estimation of the maximum allowable intersampling periods and time delays based on individual dynamics and coupling structures in the scenarios of consensus control via asynchronous sampling of relative states and asynchronous broadcasting of self-sampled states, respectively.

The effect of measurement errors on the adaptive EWMA chart

AbstractThe AEWMA chart is an Adaptive EWMA (Exponentially Weighted Moving Average)–type chart that combines the Shewhart and the classical EWMA schemes in a smooth way. Its performance has been studied under the assumption that there is no measurement error in the quality characteristic. In this paper, we investigate the performance of the AEWMA chart when measurement errors exist using a linearly covariate error model. The presence of measurement errors with a linearly increasing measurement error standard deviation is also investigated. It turns out that, with the existence of measurement errors, the efficiency of the AEWMA chart can be strongly affected, and the negative effect can be compensated by taking multiple measurements at each sample point. Comparisons with the classical EWMA scheme confirm the superiority of the AEWMA scheme in detecting a wide range of shifts even in the presence of measurement errors. An example is provided to illustrate the use of the AEWMA chart with measurement errors.

Inversion of Thermal Conductivity in Two-Dimensional Unsteady-State Heat Transfer System Based on Boundary Element Method and Decentralized Fuzzy Inference

Based on the boundary element method and the decentralized fuzzy inference algorithm, the thermal conductivity in the two-dimensional unsteady-state heat transfer system changing with the temperature is deduced. The more accurate inversion results are obtained by introducing the variable universe method. The concrete method is as follows: using experimental means to obtain the instantaneous temperature in the material or on the boundary, to determine the thermal conductivity of the material by solving the inversion problem. The boundary element method is used to calculate the regional boundary and internal temperature in the direct problem. With the inversion problem, the decentralized fuzzy inference algorithm is used to compensate for the initial guess of the thermal conductivity by using the difference between the temperature measurement and the temperature calculation. In the inversion problem, the influence of the initial guess of different thermal conductivities, different numbers of measuring points, and the existence of measurement errors on the results is discussed. The example calculation and analysis prove that, with different initial guesses, existence of measurement errors, and the number of boundary measurements decrease, the methods adopted in this paper still maintain good validity and accuracy.

An Examination of Bruno De Finetti's Understanding of J M Keynes's a Treatise on Probability: Close but No Cigar

Bruno de Finetti came close to understanding, but still disagreeing with, what Keynes was doing in the A Treatise on Probability, which Keynes had started working on in 1904 and continued developing through August, 1914. Keynes then put the work aside until he reviewed it for publication in 1920. It was published in 1921. Bruno de Finetti thought that, perhaps, Keynes ‘s views on “non numerical” probability might or could be supported if the decision maker had to use or resort to interval valued probabilities in the initial or beginning stages of an analysis of a decision problem. He gave an example where this is the case given conflicting evidence. His example is an interval valued probability. However, it is not an example involving missing, unavailable, relevant evidence, which is what Keynes meant when discussing the making of decisions under uncertainty. Unfortunately, de Finetti, like Emile Borel, F Y Edgeworth, and E B. Wilson before him, skipped the analysis in Part II of the A Treatise on Probability that would have allowed him to conclude that Keynes’s views on “non numerical probabilities” were not mysterious at all, but were interval valued probabilities based on the non (sub) additivity of probability applications in the real world. This means that Keynes was arguing that non-additivity is the general case in the real world of decision making not additivity. There is not a single reference in the corpus of de Finetti’s lifetime works that connects Keynes’s non numerical probabilities to both Boole’s and Keynes’s interval valued probability, as done explicitly by Keynes in chapters 3, 15, 16, 17, 20, 22, 26, 29, and 30 of the A Treatise on Probability. On the other hand, de Finetti always makes it clear that he believed that Keynes’s “non numerical probabilities” could never be the general case in decision making. The general case had to end up always leading to a precise, exact probability estimate and never a non numerical probability, either imprecise or logically indeterminate, even if it did turn out to be the case that what Keynes meant by non numerical probability was interval valued probability. For de Finetti, all intermediate and final probability estimates must be point estimate probabilities. Thus, what de Finetti meant by uncertainty is the existence of measurement error resulting from the concept of inner and outer measures in measure theory when dealing with initial conditions that would likely involve a paucity of relevant evidence. This is completely different from what Keynes meant by the word uncertainty. For Keynes, uncertainty meant that there was missing or unavailable relevant evidence or knowledge. However, Keynes clearly restricted the existence of ignorance, where there is no relevant evidence, to the distant future where technological change, innovation and advance threatened to make current technologies obsolete overnight. This threat of obsolescence is limited to long run investment in physical durable capital good and financial products. Keynes completely and totally rejected the Post Keynesian claims of GLS Shackle, Joan and Austin Robinson, and Paul Davidson that there was complete and total uncertainty about the future so that the weight of the evidence, w, had to equal 0. Note that de Finetti also ignored Frank Ramsey’s two highly critical, but error filled, reviews of Keynes and A Treatise on Probability. Of course, de Finetti supported Ramsey’s additive and linear approach to the use of numerical probability using betting quotients because it established the coherence and consistency of the better’s beliefs with the purely mathematical laws of the probability calculus and prevented a Dutch book from being able to be made theoretically against the better. Therefore, rationality had only one meaning for de Finetti. It meant consistency with the purely mathematical laws of the probability calculus, while for Keynes rationality meant basing one’s estimate of a probability on all of the available, relevant evidence or knowledge. Note that the usual definition of rationality used by economists is Maximizing Utility or Maximizing Subjective Expected Utility (SEU theory). Finally, de Finetti ultimately changed his mind as he grew older. Bruno de Finetti came to regard all logical theories of probability as being partly defective.

A multilevel Bayesian method for ultrasound-based damage identification in composite laminates

Estimating deterministic single-valued damage parameters when evaluating the actual health state of a material has a limited meaning if one considers not only the existence of measurement errors, but also that the model chosen to represent the damage behavior is just an idealization of reality. This paper proposes a multilevel Bayesian inverse problem framework to deal with these sources of uncertainty in the context of ultrasound-based damage identification. Although the methodology has a broad spectrum of applicability, here it is oriented to model-based damage assessment in layered composite materials using through-transmission ultrasonic measurements. The overall procedure is first validated on synthetically generated signals and then evaluated on real signals obtained from a post-impact fatigue damage experiment in a cross-ply carbon-epoxy laminate. The evidence of the hypothesized model of damage is revealed as a suitable measure of the overall ability of that candidate hypothesis to represent the actual damage state observed by the ultrasound, thus avoiding the extremes of over-fitting or under-fitting the ultrasonic signal.

Inequality constrained nonlinear data reconciliation of a steam turbine power plant for enhanced parameter estimation

Measurement errors inevitably exist amongst on-line measured data in steam turbine power plants. Problematic steam turbine isentropic efficiencies and turbine expansion curves are therefore obtained in the existence of measurement errors. Data reconciliation is widely used for uncertainty reduction of measurements and parameter estimation. Inequality constraints or bounds are rather necessary in some cases to adjust parameter estimates to be physically meaningful. In this work, we apply an inequality constrained nonlinear data reconciliation approach to the thermal system of a power plant, and compare its effect with equality constrained approaches. Case studies using performance test data and operational measurement data of a real-life 1000 MW steam turbine power plant are provided. The necessity and difference brought by inequality constraints are also discussed. Corrected expansion curve with reasonable enthalpy–entropy relationships and better estimates of isentropic efficiencies are obtained after implementation of inequality constraints. Results show that uncertainties of most measured parameters are reduced by 30–80 percent, and uncertainty of the calculated exhaust steam enthalpy is reduced by 22 percent.

Cell constant studies of bipolar and tetrapolar electrode systems for impedance measurement

In comparison to the bipolar or two-electrode system, the tetrapolar or four-electrode arrangement is a well-known technique to reduce electrode polarisation just as effectively. However, recent studies have showed that phenomena such as negative sensitivity and multiple current paths can compromise the advantages of the tetrapolar electrode arrangement, thereby potentially limiting its applications. This paper reveals a novel method to evaluate the performance of the different electrode systems in which the concept of cell constant is extended to an impedance measurement system. We employ it as a standardised parameter to quantitatively analyse planar electrode systems in bipolar and tetrapolar measurement modes. Indeed, the cell constant is a key parameter in conductivity sensors to evaluate electrodes designs since it is independent of any readout electronics. A comparison of measurement modes using finite element methods (FEM) simulations and measurements for sodium chloride solutions is presented. While the cell constant of the bipolar electrode system is one order of magnitude greater than that of the tetrapolar arrangement, it shows large discrepancy over the measured frequency range. In contrast, despite the existence of measurement errors, the tetrapolar arrangement yields a uniform cell constant and good agreement with the simulations.

Methodological Challenges in Mendelian Randomization

We give critical attention to the assumptions underlying Mendelian randomization analysis and their biological plausibility. Several scenarios violating the Mendelian randomization assumptions are described, including settings with inadequate phenotype definition, the setting of time-varying exposures, the presence of gene-environment interaction, the existence of measurement error, the possibility of reverse causation, and the presence of linkage disequilibrium. Data analysis examples are given, illustrating that the inappropriate use of instrumental variable techniques when the Mendelian randomization assumptions are violated can lead to biases of enormous magnitude. To help address some of the strong assumptions being made, three possible approaches are suggested. First, the original proposal of Katan (Lancet. 1986;1:507-508) for Mendelian randomization was not to use instrumental variable techniques to obtain estimates but merely to examine genotype-outcome associations to test for the presence of an effect of the exposure on the outcome. We show that this more modest goal and approach can circumvent many, though not all, the potential biases described. Second, we discuss the use of sensitivity analysis in evaluating the consequences of violations in the assumptions and in attempting to correct for those violations. Third, we suggest that a focus on negative, rather than positive, Mendelian randomization results may turn out to be more reliable.

Alternative approaches to structural modeling of ordinal data: A Monte Carlo study

In practice, several measures of association are used when analyzing structural equation models with ordinal variables: ordinary Pearson correlations (PE approach), polychoric and polyserial correlations (PO approach), and conditional polychoric correlations (CPO approach). In the case of structural equation models without latent variables, the literature has shown that the PE approach is outperformed by the alternatives. In this article we report a Monte Carlo study showing the comparative performance of the aforementioned alternative approaches under deviations from their respective assumptions in the case of structural equation models with latent variables when attention is restricted to point estimates of model parameters. The CPO approach is shown to be the most robust against nonnormality. It is also robust to randomness of the exogenous variables, but not to the existence of measurement errors in them. The PO approach lacks robustness against nonnormality. The PE approach lacks robustness against transformation errors but otherwise it can perform about as well as the alternative approaches.

A Causal Approach to Nonrandom Measurement Errors

The purpose of this paper is to examine several specific kinds of nonrandom measurement errors and to note their implications for causal model construction. In doing so, my secondary purpose is to sensitize the reader to the crucial importance of making one's assumptions fully explicit and to the advantages of a causal models approach to measurement errors. It is well known that the presence of even random measurement errors can produce serious distortions in our estimates, particularly whenever one is attempting to assess the relative contributions of intercorrelated independent variables. Nevertheless, common practice is to utilize what Duncan refers to as the naive approach to the presence of measurement errors: that of acknowledging the existence of measurement errors, and even discussing possible sources of such errors, while completely ignoring them in the analysis stage of the research process. That is, measured values are inserted directly into causal models as though they adequately reflect the true values. It can easily be shown that such a practice, while leading to important simplifications, can readily lead one astray. In particular, it may blind the analyst to searching for alternative plausible explanations that allow for measurement error.There have been a number of very recent papers in the sociological literature, some of which will be briefly summarized since they may not be familiar to the reader. For the most part, these papers have dealt rather systematically with ways to handle random measurement errors, whereas nonrandom errors have been dealt with only incidentally and much less carefully.