Journal articles: 'Heterogeneity Error'

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Ballinger, T. Parker, and Nathaniel T. Wilcox. "Decisions, Error and Heterogeneity." Economic Journal 107, no. 443 (July 1, 1997): 1090–105. http://dx.doi.org/10.1111/j.1468-0297.1997.tb00009.x.

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Darban, Ameneh, Mojtaba Ghaedi, and Jafar Qajar. "Analysis of the impacts of relative permeability and mobility ratio on heterogeneity loss error during upscaling of geological models." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 75 (2020): 53. http://dx.doi.org/10.2516/ogst/2020049.

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The detailed geological fine grids are upscaled to create reliably sized simulation coarse models to solve flow equations in a more efficient way. Any upscaling process results in a loss of accuracy, along with an increase of errors. Numerical dispersion, heterogeneity loss, and connectivity misrepresentation are responsible for the upscaling errors. Recognizing the source of each error, and the behavior of influential factors through upscaling process could provide an optimum level of upscaling and an evaluation of upscaling methods’ accuracy. Despite the importance of upscaling error, little attention has been paid to this subject. This paper represents a rigorous analysis of the heterogeneity loss behavior associated with the relative permeability contrast and the mobility ratio under a waterflooding process. For this purpose, heterogeneous fine grid models are constructed by the fractional Brownian motion process. The models are upscaled by three upscaling factors. The models achieved are implemented to eliminate the impact of numerical error among upscaling errors in order to focus strictly on heterogeneity loss. Water–oil displacement simulation is then performed on fine and corresponding refined upscaled models at three different ratios of relative permeabilities and mobility ratios. In the next stage, the relation between flow performance error and heterogeneity loss is investigated by the heterogeneity loss plot. The slope of this plot provides the reservoir engineer an insight to evaluate the performance of upscaling methods and the behavior of the influential factors on upscaling errors. Moreover, by using the heterogeneity loss plot for each ratio, a limit of coarsening is presented. Based on the results, the heterogeneity loss error is affected more by the mobility ratio contrast than the relative permeability difference. Also, it is demonstrated that water-wet reservoirs with light oil are more sensitive to the level of upscaling.

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Shurygin, D. N., V. M. Kalinchenko, and V. V. Shutkova. "INTERPOLATION ERROR ESTIMATION CONSIDERING GEOLOGICAL SPACE HETEROGENEITY." MINING INFORMATIONAL AND ANALYTICAL BULLETIN 5 (2018): 113–21. http://dx.doi.org/10.25018/0236-1493-2018-5-0-113-121.

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Fan, Caiyun, Wenbin Lu, and Yong Zhou. "Testing error heterogeneity in censored linear regression." Computational Statistics & Data Analysis 161 (September 2021): 107207. http://dx.doi.org/10.1016/j.csda.2021.107207.

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Shugan, Steven M. "Editorial: Errors in the Variables, Unobserved Heterogeneity, and Other Ways of Hiding Statistical Error." Marketing Science 25, no. 3 (May 2006): 203–16. http://dx.doi.org/10.1287/mksc.1060.0215.

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Griffith, Daniel A., Robert Haining, and Giuseppe Arbia. "Heterogeneity of Attribute Sampling Error in Spatial Data Sets." Geographical Analysis 26, no. 4 (September 3, 2010): 300–320. http://dx.doi.org/10.1111/j.1538-4632.1994.tb00328.x.

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Nichols, Ellert R., Elnaz Shadabi, and Douglas B. Craig. "Effect of alteration of translation error rate on enzyme microheterogeneity as assessed by variation in single molecule electrophoretic mobility and catalytic activity." Biochemistry and Cell Biology 87, no. 3 (June 2009): 517–29. http://dx.doi.org/10.1139/o09-010.

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The role of translation error for Escherichia coli individual β-galactosidase molecule catalytic and electrophoretic heterogeneity was investigated using CE-LIF. An E. coli rpsL mutant with a hyperaccurate translation phenotype produced enzyme molecules that exhibited significantly less catalytic heterogeneity but no reduction of electrophoretic heterogeneity. Enzyme expressed with streptomycin-induced translation error had increased thermolability, lower activity, and no significant change to catalytic or electrophoretic heterogeneity. Modeling of the electrophoretic behaviour of β-galactosidase suggested that variation of the hydrodynamic radius may be the most significant contributor to electrophoretic heterogeneity.

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Campbell, Patrick J., Mira Patel, Jennifer R. Martin, Ana L. Hincapie, David Rhys Axon, Terri L. Warholak, and Marion Slack. "Systematic review and meta-analysis of community pharmacy error rates in the USA: 1993–2015." BMJ Open Quality 7, no. 4 (October 2018): e000193. http://dx.doi.org/10.1136/bmjoq-2017-000193.

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ImportanceWhile much is known about hospital pharmacy error rates in the USA, comparatively little is known about community pharmacy dispensing error rates.ObjectiveThe aim of this study was to determine the rate of community pharmacy dispensing errors in the USA.MethodsEnglish language, peer-reviewed observational and interventional studies that reported community pharmacy dispensing error rates in the USA from January 1993 to December 2015 were identified in 10 bibliographic databases and topic-relevant grey literature. Studies with a denominator reflecting the total number of prescriptions in the sample were necessary for inclusion in the meta-analysis. A random effects meta-analysis was conducted to estimate an aggregate community pharmacy dispensing error rate. Heterogeneity was assessed using the I2 statistic prior to analysis.ResultsThe search yielded a total of 8490 records, of which 11 articles were included in the systematic review. Two articles did not have adequate data components to be included in the meta-analysis. Dispensing error rates ranged from 0.00003% (43/1 420 091) to 55% (55/100). The meta-analysis included 1 461 128 prescriptions. The overall community pharmacy dispensing error rate was estimated to be 0.015 (95% CI 0.014 to 0.018); however, significant heterogeneity was observed across studies (I2=99.6). Stratification by study error identification methodology was found to have a significant impact on dispensing error rate (p<0.001).Conclusion and relevanceThere are few published articles that describe community pharmacy dispensing error rates in the USA. Thus, there is limited information about the current rate of community pharmacy dispensing errors. A robust investigation is needed to assess dispensing error rates in the USA to assess the nature and magnitude of the problem and establish prevention strategies.

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Byun, Bok S., and Chi‐Yuh Young. "Effects of subsurface characteristics on surface seismic measurements: A simulation study on horizontally layered media." GEOPHYSICS 54, no. 6 (June 1989): 730–36. http://dx.doi.org/10.1190/1.1442700.

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We use seismic modeling of shot records to simulate the effects of acoustic‐wave propagation, P/SV-wave conversion, intrabed multiple reflections, anelastic attenuation, and transverse isotropy in materials representative of a shallow Gulf of Mexico environment. The study focuses on the estimates of three kinematic properties: vertical traveltime, velocity used for depth conversion, and depth to the reflector. The primary tool used for the measurement is velocity spectral analysis along hyperbolic traveltime‐distance curves. Analyses of numerous modeling experiments in the Gulf of Mexico environment lead to the following conclusions: First, the kinematic properties are affected mainly by anelastic attenuation, vertical velocity heterogeneity, and velocity anisotropy. The effects of P/SV-wave mode conversion and intrabed multiples are of secondary significance. Second, anelastic attenuation coupled with heterogeneity contribute up to 3 percent in depth error, mainly in the form of apparent time delays. Transverse isotropy is likely to add up to an additional 2 percent, for a total of 5 percent depth error. Since the depth error magnitude agrees closely with published field observations, we conclude, therefore, that attenuation, heterogeneity, and transverse isotropy account for most of the errors in depth estimation in horizontally layered media.

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Esbensen, Kim H. "Materials Properties: Heterogeneity and Appropriate Sampling Modes." Journal of AOAC INTERNATIONAL 98, no. 2 (March 1, 2015): 269–74. http://dx.doi.org/10.5740/jaoacint.14-234.

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Abstract The target audience for this Special Section comprises parties related to the food and feed sectors, e.g., field samplers, academic and industrial scientists, laboratory personnel, companies, organizations, regulatory bodies, and agencies who are responsible for sampling, as well as project leaders, project managers, quality managers, supervisors, and directors. All these entities face heterogeneous materials,and the characteristics of heterogeneous materials needs to be competently understood by all of them. Before delivering analytical results for decision-making, one form or other of primary sampling is always necessary, which must counteract the effects of the sampling target heterogeneity. Up to five types of sampling error may arise as a specific sampling process interacts with a heterogeneous material; two sampling errors arise because of the heterogeneity of the sampling target, and three additional sampling errors are produced by the sampling process itself—if not properly understood, reduced, and/or eliminated, which is the role of Theory of Sampling. Thispaper discusses the phenomenon and concepts involvedin understanding, describing, and managing the adverse effects of heterogeneity in sampling.

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Bifftu, Berhanu Boru, and Bezenaw Yimer Mekonnen. "The Magnitude of Medication Administration Errors among Nurses in Ethiopia: a Systematic Review and Meta-analysis." Journal of Caring Sciences 9, no. 1 (March 1, 2020): 1–8. http://dx.doi.org/10.34172/jcs.2020.001.

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Introduction: Nurses are the final safety check in the process of medication administration process to prevent errors that adversely affect life; yet death of comprehensive evidences in Ethiopia. The present study aimed to assess the pooled magnitude of MAEs (Medication Administration Errors) in Ethiopia. Methods: Systematic literature search in the databases of Pub-Med, Cochrane, and Google Scholar for gray literature were performed until December 3, 2018. The quality of study was assessed using criteria adopted from similar studies. Heterogeneity test and evidence of publication bias were assessed. Moreover, sensitivity analysis was also performed. Pooled prevalence of MAE was calculated using the random effects model. Results: A total of 2142 medication administrations were from observational and 681from selfreported studies were included in this systematic review and meta-analysis. The most prevalent and frequently reported type of MAEs was documentation error (52% to 87.5%) and time error (25.5% to 58.5%) respectively. Overall, the pooled magnitude of MAE was found to be 39.3% (95% CI, 29.1%-49.5%).It has no evidence of significant heterogeneity (I2 = 0%, P = 0.57) and publication bias Egger’s test (P = 0.40). Conclusion: Overall, more than one in four observed/perceived medication administrations had errors. Documentation error is the most prevalent type of error. Nurses are suggested to strengthen their focus on the rights of medication administration guide particularly, documentation of their activities need special attention.

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Abreu, Paula C., Susan E. Hodge, and David A. Greenberg. "Quantification of type I error probabilities for heterogeneity LOD scores." Genetic Epidemiology 22, no. 2 (January 10, 2002): 156–69. http://dx.doi.org/10.1002/gepi.0155.

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Ruscio, John, and Brendan Roche. "Variance Heterogeneity in Published Psychological Research." Methodology 8, no. 1 (August 1, 2012): 1–11. http://dx.doi.org/10.1027/1614-2241/a000034.

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Parametric assumptions for statistical tests include normality and equal variances. Micceri (1989) found that data frequently violate the normality assumption; variances have received less attention. We recorded within-group variances of dependent variables for 455 studies published in leading psychology journals. Sample variances differed, often substantially, suggesting frequent violation of the assumption of equal population variances. Parallel analyses of equal-variance artificial data otherwise matched to the characteristics of the empirical data show that unequal sample variances in the empirical data exceed expectations from normal sampling error and can adversely affect Type I error rates of parametric statistical tests. Variance heterogeneity was unrelated to relative group sizes or total sample size and observed across subdisciplines of psychology in experimental and correlational research. These results underscore the value of examining variances and, when appropriate, using data-analytic methods robust to unequal variances. We provide a standardized index for examining and reporting variance heterogeneity.

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Glenny, R. W., L. Polissar, and H. T. Robertson. "Relative contribution of gravity to pulmonary perfusion heterogeneity." Journal of Applied Physiology 71, no. 6 (December 1, 1991): 2449–52. http://dx.doi.org/10.1152/jappl.1991.71.6.2449.

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We designed a series of experiments and analyses to quantify the contribution of gravity to pulmonary perfusion heterogeneity. Regional pulmonary perfusion was measured in five anesthetized and ventilated dogs in both supine and prone positions by use of radiolabeled microspheres injected during apnea at functional residual capacity. Measurements of flow were repeated in each position, and the sequence of positions was prospectively designed to nullify any effect of order. The lungs of each animal were excised, perfused with saline until clear, dried at an inflation pressure of 25 cmH2O, and cut into 1.9-cm3 pieces. Each piece was weighed and the radioactivity determined in a scintillation counter. Measurement errors were minimized by excluding lung pieces that had greater than 25% airway and weighed less than 10 mg or greater than 60 mg. Weight-normalized flows in each position and repetition were determined for each lung piece. An analysis of variance model was used to identify the percentage of variation in regional flow that was due to position (supine vs. prone), to random error and time (measurement and repetition), and to structure, where structure was defined as the component of flow that remained constant across position and replication. The contributions of position, error/time, and structure to the total variability of flow across the five dogs were 7.8 +/- 0.6, 8.4 +/- 8.3, and 83.8 +/- 8.4%, (SD), respectively. Because the contribution of position represents the additive effect of gravity between two opposite positions, the contribution of gravity to perfusion heterogeneity in one position may be as little as 4%.(ABSTRACT TRUNCATED AT 250 WORDS)

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Bonini, Pierangelo, Mario Plebani, Ferruccio Ceriotti, and Francesca Rubboli. "Errors in Laboratory Medicine." Clinical Chemistry 48, no. 5 (May 1, 2002): 691–98. http://dx.doi.org/10.1093/clinchem/48.5.691.

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Abstract Background: The problem of medical errors has recently received a great deal of attention, which will probably increase. In this minireview, we focus on this issue in the fields of laboratory medicine and blood transfusion. Methods: We conducted several MEDLINE queries and searched the literature by hand. Searches were limited to the last 8 years to identify results that were not biased by obsolete technology. In addition, data on the frequency and type of preanalytical errors in our institution were collected. Results: Our search revealed large heterogeneity in study designs and quality on this topic as well as relatively few available data and the lack of a shared definition of “laboratory error” (also referred to as “blunder”, “mistake”, “problem”, or “defect”). Despite these limitations, there was considerable concordance on the distribution of errors throughout the laboratory working process: most occurred in the pre- or postanalytical phases, whereas a minority (13–32% according to the studies) occurred in the analytical portion. The reported frequency of errors was related to how they were identified: when a careful process analysis was performed, substantially more errors were discovered than when studies relied on complaints or report of near accidents. Conclusions: The large heterogeneity of literature on laboratory errors together with the prevalence of evidence that most errors occur in the preanalytical phase suggest the implementation of a more rigorous methodology for error detection and classification and the adoption of proper technologies for error reduction. Clinical audits should be used as a tool to detect errors caused by organizational problems outside the laboratory.

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Guttmann, Aline, Xinran Li, Jean Gaudart, Yan Gérard, Jacques Demongeot, Jean-Yves Boire, and Lemlih Ouchchane. "Spatial heterogeneity of type I error for local cluster detection tests." International Journal of Health Geographics 13, no. 1 (2014): 15. http://dx.doi.org/10.1186/1476-072x-13-15.

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Gartlehner, Gerald, Suzanne L. West, Alyssa J. Mansfield, Charles Poole, Elizabeth Tant, Linda J. Lux, and Kathleen N. Lohr. "CLINICAL HETEROGENEITY IN SYSTEMATIC REVIEWS AND HEALTH TECHNOLOGY ASSESSMENTS: SYNTHESIS OF GUIDANCE DOCUMENTS AND THE LITERATURE." International Journal of Technology Assessment in Health Care 28, no. 1 (January 2012): 36–43. http://dx.doi.org/10.1017/s0266462311000687.

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Objectives: The aim of this study was to synthesize best practices for addressing clinical heterogeneity in systematic reviews and health technology assessments (HTAs).Methods: We abstracted information from guidance documents and methods manuals made available by international organizations that develop systematic reviews and HTAs. We searched PubMed® to identify studies on clinical heterogeneity and subgroup analysis. Two authors independently abstracted and assessed relevant information.Results: Methods manuals offer various definitions of clinical heterogeneity. In essence, clinical heterogeneity is considered variability in study population characteristics, interventions, and outcomes across studies. It can lead to effect-measure modification or statistical heterogeneity, which is defined as variability in estimated treatment effects beyond what would be expected by random error alone. Clinical and statistical heterogeneity are closely intertwined but they do not have a one-to-one relationship. The presence of statistical heterogeneity does not necessarily indicate that clinical heterogeneity is the causal factor. Methodological heterogeneity, biases, and random error can also cause statistical heterogeneity, alone or in combination with clinical heterogeneity.Conclusions: Identifying potential modifiers of treatment effects (i.e., effect-measure modifiers) is important for researchers conducting systematic reviews and HTAs. Recognizing clinical heterogeneity and clarifying its implications helps decision makers to identify patients and patient populations who benefit the most, who benefit the least, and who are at greatest risk of experiencing adverse outcomes from a particular intervention.

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Caliari, Thiago, Marco Valente, and Ricardo Machado Ruiz. "Heterogeneity of demand and product innovation." Estudos Econômicos (São Paulo) 47, no. 1 (March 2017): 5–37. http://dx.doi.org/10.1590/0101-416147115trm.

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Abstract This paper discusses the relationship between heterogeneity of demand regarding choice procedures and product innovation. We propose an evolutionary model showing how consumers with imperfect information chose and select differentiated goods. The model shows the role of information and choice procedures and its relation with the innovative process. The price plays an important role, but the quality of information, error tolerance and criteria for preference ordering are important determinants of the performance of firms in an industry with product innovation.

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Alexander, Ralph A., and Diane M. Govern. "A New and Simpler Approximation for ANOVA Under Variance Heterogeneity." Journal of Educational Statistics 19, no. 2 (June 1994): 91–101. http://dx.doi.org/10.3102/10769986019002091.

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A new approximation is proposed for testing the equality of k independent means in the face of heterogeneity of variance. Monte Carlo simulations show that the new procedure has Type I error rates that are very nearly nominal and Type II error rates that are quite close to those produced by James’s (1951) second-order approximation. In addition, it is computationally the simplest approximation yet to appear, and it is easily applied to Scheffé (1959) -type multiple contrasts and to the calculation of approximate tail probabilities.

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Cai, Z. S., B. A. Luxon, and E. L. Forker. "Intralobular zonal heterogeneity and hepatic indicator dilution curves." American Journal of Physiology-Gastrointestinal and Liver Physiology 268, no. 2 (February 1, 1995): G189—G199. http://dx.doi.org/10.1152/ajpgi.1995.268.2.g189.

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Conventional interpretation of hepatic indicator dilution curves rests on the assumption, among others, that every hepatocyte operates with the same rate constants. When this assumption is false, owing to intralobular zonal variation in surface-to-volume ratios and/or to zonal differences in permeability, the apparent rate constants recoverable from outflow transients are wrong estimates of average liver performance. We develop the theoretical basis for this conclusion and illustrate by example how it can confuse the interpretation of experimental data. The analysis proceeds from vascular and extracellular reference curves recorded from perfused rat livers and from a simple model of intralobular architecture in which highly arborized periportal sinusoids have a larger surface-to-volume ratio than the less-branched vasculature around the central vein. The experimental data and the model, applied to a wide range of hypothetical solutes, are used to compare the true average rate constants for uptake, efflux, and intracellular removal with the apparent values recoverable from outflow curves. When zonal differences in surface-to-volume ratios are the sole source of heterogeneity, the wrong estimates prove of little practical importance. By contrast, assigning larger regional variations in permeability leads to substantial errors. The confusion arising from such errors may be qualitative as well as quantitative. The presence of heterogeneity and thus the risk of interpretive error appears unrecognizable from outflow curves.

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Rezapour, Mahdi, and Khaled Ksaibati. "Accommodating Taste and Scale Heterogeneity for Front-Seat Passenger’ Choice of Seat Belt Usage." Mathematics 9, no. 5 (February 24, 2021): 460. http://dx.doi.org/10.3390/math9050460.

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There is growing interest in implementation of the mixed model to account for heterogeneity across population observations. However, it has been argued that the assumption of independent and identically distributed (i.i.d) error terms might not be realistic, and for some observations the scale of the error is greater than others. Consequently, that might result in the error terms’ scale to be varied across those observations. As the standard mixed model could not account for the aforementioned attribute of the observations, extended model, allowing for scale heterogeneity, has been proposed to relax the equal error terms across observations. Thus, in this study we extended the mixed model to the model with heterogeneity in scale, or generalized multinomial logit model (GMNL), to see if accounting for the scale heterogeneity, by adding more flexibility to the distribution, would result in an improvement in the model fit. The study used the choice data related to wearing seat belt across front-seat passengers in Wyoming, with all attributes being individual-specific. The results highlighted that although the effect of the scale parameter was significant, the scale effect was trivial, and accounting for the effect at the cost of added parameters would result in a loss of model fit compared with the standard mixed model. Besides considering the standard mixed and the GMNL, the models with correlated random parameters were considered. The results highlighted that despite having significant correlation across the majority of the random parameters, the goodness of fits favors more parsimonious models with no correlation. The results of this study are specific to the dataset used in this study, and due to the possible fact that the heterogeneity in observations related to the front-seat passengers seat belt use might not be extreme, and do not require extra layer to account for the scale heterogeneity, or accounting for the scale heterogeneity at the cost of added parameters might not be required. Extensive discussion has been made in the content of this paper about the model parameters’ estimations and the mathematical formulation of the methods.

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Jaumann, Stefan, and Kurt Roth. "Effect of unrepresented model errors on estimated soil hydraulic material properties." Hydrology and Earth System Sciences 21, no. 9 (September 1, 2017): 4301–22. http://dx.doi.org/10.5194/hess-21-4301-2017.

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Abstract. Unrepresented model errors influence the estimation of effective soil hydraulic material properties. As the required model complexity for a consistent description of the measurement data is application dependent and unknown a priori, we implemented a structural error analysis based on the inversion of increasingly complex models. We show that the method can indicate unrepresented model errors and quantify their effects on the resulting material properties. To this end, a complicated 2-D subsurface architecture (ASSESS) was forced with a fluctuating groundwater table while time domain reflectometry (TDR) and hydraulic potential measurement devices monitored the hydraulic state. In this work, we analyze the quantitative effect of unrepresented (i) sensor position uncertainty, (ii) small scale-heterogeneity, and (iii) 2-D flow phenomena on estimated soil hydraulic material properties with a 1-D and a 2-D study. The results of these studies demonstrate three main points: (i) the fewer sensors are available per material, the larger is the effect of unrepresented model errors on the resulting material properties. (ii) The 1-D study yields biased parameters due to unrepresented lateral flow. (iii) Representing and estimating sensor positions as well as small-scale heterogeneity decreased the mean absolute error of the volumetric water content data by more than a factor of 2 to 0. 004.

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Antman, Francisca, and David McKenzie. "Poverty traps and nonlinear income dynamics with measurement error and individual heterogeneity." Journal of Development Studies 43, no. 6 (August 2007): 1057–83. http://dx.doi.org/10.1080/00220380701466567.

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Herman, Allen A., Kai F. Yu, Howard J. Hoffman, Cara J. Krulewitch, and Leiv S. Bakketeig. "Birth weight, gestational age and perinatal mortality: biological heterogeneity and measurement error." Early Human Development 33, no. 1 (April 1993): 29–44. http://dx.doi.org/10.1016/0378-3782(93)90171-p.

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Madsen, Rasmus Bødker, and Thomas Mejer Hansen. "Estimation and accounting for the modeling error in probabilistic linearized amplitude variation with offset inversion." GEOPHYSICS 83, no. 2 (March 1, 2018): N15—N30. http://dx.doi.org/10.1190/geo2017-0404.1.

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A linearized form of Zoeppritz equations combined with the convolution model is widely used in inversion of amplitude variation with offset (AVO) seismic data. This is shown to introduce a “modeling error,” compared with using the full Zoeppritz equations, whose magnitude depends on the degree of subsurface heterogeneity. Then, we evaluate a methodology for quantifying this modeling error through a probability distribution. First, a sample of the unknown probability density describing the modeling error is generated. Then, we determine how this sample can be described by a correlated Gaussian probability distribution. Finally, we develop how such modeling errors affect the linearized AVO inversion results. If not accounted for (which is most often the case), the modeling errors can introduce significant artifacts in the inversion results, if the signal-to-noise ratio is less than 2, as is the case for most AVO data obtained today. However, if accounted for, such artifacts can be avoided. The methodology can easily be adapted and applied to most linear AVO inversion methods, by allowing the use of the inferred modeling error as a correlated Gaussian noise model.

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Wilches, Camilo, Maik Vaske, Kilian Hartmann, and Michael Nelles. "Representative Sampling Implementation in Online VFA/TIC Monitoring for Anaerobic Digestion." Energies 12, no. 6 (March 26, 2019): 1179. http://dx.doi.org/10.3390/en12061179.

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This paper describes an automatic sampling system for anaerobic reactors that allows taking representative samples following the guidelines of Gy’s (1998) theory of sampling. Due to the high heterogeneity degree in a digester the sampling errors are larger than the analysis error, making representative sampling a prerequisite for successful process control. In our system, samples are automatically processed, generating a higher density of data and avoiding human error by sample manipulation. The combination of a representative sampling system with a commercial automate titration unit generates a robust online monitoring system for biogas plants. The system was successfully implemented in an operating biogas plant to control a feeding-on-demand biogas system.

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Chalak, Karim. "INSTRUMENTAL VARIABLES METHODS WITH HETEROGENEITY AND MISMEASURED INSTRUMENTS." Econometric Theory 33, no. 1 (February 15, 2016): 69–104. http://dx.doi.org/10.1017/s0266466615000390.

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We study the consequences of substituting an error-laden proxy W for an instrument Z on the interpretation of Wald, local instrumental variable (LIV), and instrumental variable (IV) estimands in an ordered discrete choice structural system with heterogeneity. A proxy W need only satisfy an exclusion restriction and that the treatment and outcome are mean independent from W given Z. Unlike Z, W need not satisfy monotonicity and may, under particular specifications, fail exogeneity. For example, W could code Z with error, with missing observations, or coarsely. We show that Wald, LIV, and IV estimands using W identify weighted averages of local or marginal treatment effects (LATEs or MTEs). We study a necessary and sufficient condition for nonnegative weights. Further, we study a condition under which the Wald or LIV estimand using W identifies the same LATE or MTE that would have been recovered had Z been observed. For example, this holds for binary Z and therefore the Wald estimand using W identifies the same “average causal response,” or LATE for binary treatment, that would have been recovered using Z. Also, under this condition, LIV using W can be used to identify MTE and average treatment effects for e.g., the population, treated, and untreated.

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Overall, John E., Robert S. Atlas, and Janet M. Gibson. "Power of a Test That is Robust against Variance Heterogeneity." Psychological Reports 77, no. 1 (August 1995): 155–59. http://dx.doi.org/10.2466/pr0.1995.77.1.155.

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Welch (1947) proposed an adjusted t test that can be used to correct the serious bias in Type I error protection that is otherwise present when both sample sizes and variances are unequal. The implications of the Welch adjustment for power of tests for the difference between two treatments across k levels of a concomitant factor are evaluated in this article for k × 2 designs with unequal sample sizes and unequal variances. Analyses confirm that, although Type I error is uniformly controlled, power of the Welch test of significance for the main effect of treatments remains rather seriously dependent on direction of the correlation between unequal variances and unequal sample sizes. Nevertheless, considering the fact that analysis of variance is not an acceptable option in such cases, the Welch t test appears to have an important role to play in the analysis of experimental data.

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Fisher, Jared A., Maya Spaur, Ian D. Buller, Abigail R. Flory, Laura E. Beane Freeman, Jonathan N. Hofmann, Michael Giangrande, Rena R. Jones, and Mary H. Ward. "Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study." International Journal of Environmental Research and Public Health 18, no. 4 (February 9, 2021): 1637. http://dx.doi.org/10.3390/ijerph18041637.

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Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012–2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using “gold standard” rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: −2 to 168) and 9 m (IQR: −80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: −1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies.

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Baek, Eunkyeng, and John J. M. Ferron. "Modeling heterogeneity of the level-1 error covariance matrix in multilevel models for single-case data." Methodology 16, no. 2 (June 18, 2020): 166–85. http://dx.doi.org/10.5964/meth.2817.

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Previous research applying multilevel models to single-case data has made a critical assumption that the level-1 error covariance matrix is constant across all participants. However, the level-1 error covariance matrix may differ across participants and ignoring these differences can have an impact on estimation and inferences. Despite the importance of this issue, the effects of modeling between-case variation in the level-1 error structure had not yet been systematically studied. The purpose of this simulation study was to identify the consequences of modeling and not modeling between-case variation in the level-1 error covariance matrices in single-case studies, using Bayesian estimation. The results of this study found that variance estimation was more sensitive to the method used to model the level-1 error structure than fixed effect estimation, with fixed effects only being impacted in the most extreme heterogeneity conditions. Implications for applied single-case researchers and methodologists are discussed.

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Andrabi, Tahir, Jishnu Das, Asim Ijaz Khwaja, and Tristan Zajonc. "Do Value-Added Estimates Add Value? Accounting for Learning Dynamics." American Economic Journal: Applied Economics 3, no. 3 (July 1, 2011): 29–54. http://dx.doi.org/10.1257/app.3.3.29.

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This paper illustrates the central role of persistence in estimating and interpreting value-added models of learning. Using data from Pakistani public and private schools, we apply dynamic panel methods that address three key empirical challenges: imperfect persistence, unobserved heterogeneity, and measurement error. Our estimates suggest that only one-fifth to one-half of learning persists between grades and that private schools increase average achievement by 0.25 standard deviations each year. In contrast, value-added models that assume perfect persistence yield severely downward estimates of the private school effect. Models that ignore unobserved heterogeneity or measurement error produce biased estimates of persistence. (JEL I21, J13, O15)

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Dessai, Kissan G. Gauns, and Venkatesh V. Kamat. "Computer Assisted Evaluation Using Rubrics for Reduction of Errors and Inter and Intra Examiner Heterogeneity." International Journal of Information and Communication Technology Education 14, no. 4 (October 2018): 49–65. http://dx.doi.org/10.4018/ijicte.2018100104.

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Educational institutions worldwide conduct summative examinations to evaluate academic performance of students. Such summative examinations are normally subjective in nature in higher education institutions and needs manual evaluation. However, the manual evaluation of subjective answer-scripts often suffers from evaluation anomalies and the impact of ‘Examiner variability' or ‘Examiner subjectivity'. Examiner variability mainly occurs due to differences in perception and expectation of each examiner coupled with lapses/errors in evaluation. Most of the currently employed methods partly address the problem of evaluation errors/lapses and examiner subjectivity with the aid of extra checks such as re-checking, re-verification, re-evaluation, etc. We need a pragmatic and unified approach to ensure uniformity and error-free evaluation. In this article, the authors present a method of computer aided evaluation of subjective answer-scripts using rubrics. The proposed approach focuses on improving the evaluation by reducing/eliminating the errors and examiner variability.

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Duch, Raymond, Denise Laroze, Thomas Robinson, and Pablo Beramendi. "Multi-modes for Detecting Experimental Measurement Error." Political Analysis 28, no. 2 (October 14, 2019): 263–83. http://dx.doi.org/10.1017/pan.2019.34.

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Experiments should be designed to facilitate the detection of experimental measurement error. To this end, we advocate the implementation of identical experimental protocols employing diverse experimental modes. We suggest iterative nonparametric estimation techniques for assessing the magnitude of heterogeneous treatment effects across these modes. And we propose two diagnostic strategies—measurement metrics embedded in experiments, and measurement experiments—that help assess whether any observed heterogeneity reflects experimental measurement error. To illustrate our argument, first we conduct and analyze results from four identical interactive experiments: in the lab; online with subjects from the CESS lab subject pool; online with an online subject pool; and online with MTurk workers. Second, we implement a measurement experiment in India with CESS Online subjects and MTurk workers.

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Wang, Yue Qi, Pascal Lava, Dimitri Debruyne, and Paul van Houtte. "Error Estimation of DIC for Heterogeneous Strain States." Applied Mechanics and Materials 70 (August 2011): 177–82. http://dx.doi.org/10.4028/www.scientific.net/amm.70.177.

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Digital image correlation (DIC) involves certain errors during correlation, which are highly influenced by factors, e.g. image qualities, DIC parameters, and furthermore, degrees of deformation or strain states. In this contribution, attention is paid to the influence of strain states on the uncertainty of DIC, including the magnitude and the heterogeneity of the strains. A series of 2D-DIC numerical experiments are carried out on tensile specimens associated with finite element analysis (FEA). The specimens are made of 3 materials, i.e. steel DC06, steel DX54D+Z, and aluminium alloy Al6016, and cut into 3 different geometries, i.e. standard and 2 complex designs. Initial images were taken from these real specimens, which were all painted manually with random speckle patterns. Deformed images were obtained by imposing FE displacement fields on these undeformed initial images. Consequently, the errors source from imaging system are avoided, and only intrinsic errors of DIC itself are taken into account. The hardening behaviours of those materials in 3 different orientations were introduced to FEA for simulation, namely rolling direction (RD), transverse direction (TD) and 45o w.r.t. RD (45o). In FEA, homogeneous and heterogeneous strain states are achieved through applying uniaxial tension on two ends of the standard and complex specimens, respectively. The strain states are characterized by different material properties and geometries of specimens. DIC calculation are performed at various load steps to investigate the influence of the magnitude of the strain. Errors of the fields are compared among the different specimens to study the influence of the heterogeneity. In this contribution, the qualities of the speckle patterns are considered, since different patterns are applied to each experiment.

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de Moor, C., and A. Golembesky. "PMC7 EFFECTS OF HETEROGENEITY ON THE ESTIMATION AND COMPARISON OF MEDICATION ERROR RATES." Value in Health 12, no. 3 (May 2009): A20. http://dx.doi.org/10.1016/s1098-3015(10)73160-0.

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Wright, Michael J., Mark H. Houck, and Celso M. Ferreira. "Discriminatory Power of Heterogeneity Statistics with Respect to Error of Precipitation Quantile Estimation." Journal of Hydrologic Engineering 20, no. 10 (October 2015): 04015011. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001172.

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Lonjou, C., A. Collins, R. S. Ajioka, L. B. Jorde, J. P. Kushner, and N. E. Morton. "Allelic association under map error and recombinational heterogeneity: A tale of two sites." Proceedings of the National Academy of Sciences 95, no. 19 (September 15, 1998): 11366–70. http://dx.doi.org/10.1073/pnas.95.19.11366.

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Singh, B. "Distribution of variance ratio in unbalanced random model under heterogeneity of error variances." Communications in Statistics - Theory and Methods 20, no. 9 (January 1991): 3021–28. http://dx.doi.org/10.1080/03610929108830684.

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Gomides, Mariuche Rodrigues de Almeida, Gizele Alves Martins, Isabela Starling Alves, Annelise Júlio-Costa, Antônio Jaeger, and Vitor Geraldi Haase. "Heterogeneity of math difficulties and its implications for interventions in multiplication skills." Dementia & Neuropsychologia 12, no. 3 (September 2018): 256–63. http://dx.doi.org/10.1590/1980-57642018dn12-030006.

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Abstract Math learning disability (MLD) is a heterogeneous condition characterized by severe and persistent difficulties in learning math, including difficulties in learning multiplication facts. Objective: In this article, we compared the responses of two MLD children to multiplication facts training. Methods: One of the children was a 9 year-old girl (HV) who presented mild math difficulties associated with lower accuracy of the Approximate Number System (ANS). The other was an 11 year-old boy (GA) who presented severe math difficulties related to impaired phonological processing due to developmental dyslexia. Both children underwent an intervention for multiplication, comprising conceptual instructions and retrieval practice of the times table. Results: HV’s accuracy and response speed improved consistently on both training tasks, while GA’s accuracy improved on the Simple Calculation Task only. Error analyses indicated that, after training, HV produced fewer errors of the type “close miss”, and GA produced less omission but more operand errors. Conclusion: We argue that these differences between their responses to the training tasks were caused by differences in the mechanisms underlying their math difficulties. These results support the notion that individual specificities regarding math disabilities should be taken into account during preparation of training interventions.

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Jiang, Linnie, and Ashok Litwin-Kumar. "Models of heterogeneous dopamine signaling in an insect learning and memory center." PLOS Computational Biology 17, no. 8 (August 10, 2021): e1009205. http://dx.doi.org/10.1371/journal.pcbi.1009205.

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The Drosophila mushroom body exhibits dopamine dependent synaptic plasticity that underlies the acquisition of associative memories. Recordings of dopamine neurons in this system have identified signals related to external reinforcement such as reward and punishment. However, other factors including locomotion, novelty, reward expectation, and internal state have also recently been shown to modulate dopamine neurons. This heterogeneity is at odds with typical modeling approaches in which these neurons are assumed to encode a global, scalar error signal. How is dopamine dependent plasticity coordinated in the presence of such heterogeneity? We develop a modeling approach that infers a pattern of dopamine activity sufficient to solve defined behavioral tasks, given architectural constraints informed by knowledge of mushroom body circuitry. Model dopamine neurons exhibit diverse tuning to task parameters while nonetheless producing coherent learned behaviors. Notably, reward prediction error emerges as a mode of population activity distributed across these neurons. Our results provide a mechanistic framework that accounts for the heterogeneity of dopamine activity during learning and behavior.

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Hill, Terrence D., Andrew P. Davis, J. Micah Roos, and Michael T. French. "Limitations of Fixed-Effects Models for Panel Data." Sociological Perspectives 63, no. 3 (July 17, 2019): 357–69. http://dx.doi.org/10.1177/0731121419863785.

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Although fixed-effects models for panel data are now widely recognized as powerful tools for longitudinal data analysis, the limitations of these models are not well known. We provide a critical discussion of 12 limitations, including a culture of omission, low statistical power, limited external validity, restricted time periods, measurement error, time invariance, undefined variables, unobserved heterogeneity, erroneous causal inferences, imprecise interpretations of coefficients, imprudent comparisons with cross-sectional models, and questionable contributions vis-à-vis previous work. Instead of discouraging the use of fixed-effects models, we encourage more critical applications of this rigorous and promising methodology. The most important deficiencies—Type II errors, biased coefficients and imprecise standard errors, misleading p values, misguided causal claims, and various theoretical concerns—should be weighed against the likely presence of unobserved heterogeneity in other regression models. Ultimately, we must do a better job of communicating the pitfalls of fixed-effects models to our colleagues and students.

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Wang, Qiying, and Ying Xiang Rachel Wang. "NONPARAMETRIC COINTEGRATING REGRESSION WITH NNH ERRORS." Econometric Theory 29, no. 1 (July 6, 2012): 1–27. http://dx.doi.org/10.1017/s0266466612000205.

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This paper studies a nonlinear cointegrating regression model with nonlinear nonstationary heteroskedastic error processes. We establish uniform consistency for the conventional kernel estimate of the unknown regression function and develop atwo-stage approach for the estimation of the heterogeneity generating function.

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Ruppar, Todd. "Meta-analysis: How to quantify and explain heterogeneity?" European Journal of Cardiovascular Nursing 19, no. 7 (August 5, 2020): 646–52. http://dx.doi.org/10.1177/1474515120944014.

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The number of systematic reviews and meta-analyses submitted to nursing and allied health journals continues to grow. Well-conducted and reported syntheses of research are valuable to advancing science. One of the common critiques identified in these manuscripts involves how the authors addressed heterogeneity among the studies in their meta-analyses. Methodologically inappropriate approaches regarding heterogeneity introduce error and bias into analyses and may lead to incorrect findings and conclusions. This article will discuss some of the approaches to take as well as avoid when addressing heterogeneity in meta-analyses, including suggestions for how to choose a fixed-effect or random-effects meta-analysis model and steps to follow to address heterogeneity in meta-analysis results.

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Bayley, Peter B. "Quasi-likelihood Estimation of Marked Fish Recapture." Canadian Journal of Fisheries and Aquatic Sciences 50, no. 10 (October 1, 1993): 2077–85. http://dx.doi.org/10.1139/f93-231.

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In models of the fraction of fish recaptured in field experiments on gear efficiency the binomial error distribution is usually assumed. However, variance in excess of that defined by the error distribution (overdispersion) is typical in fish capture because of heterogeneity among and within groups of individuals and incomplete model specification. Quasi-likelihood offers a parsimonious solution to the typical problem of incomplete definition of an error distribution with discrete responses. An example is given from the recapture of marked fish following rotenone treatment in lake enclosures, in which a generalized linear-logistic model includes an extra-binomial variance as a function of the mean. Estimated standard errors of fitted parameters were two to three times lower in a linear-logistic maximum likelihood model than in the quasi-likelihood model because extra-binomial variation (overdispersion) was ignored in the former model. In a cross-validation trial, 95% confidence intervals included 85% of independent observations with the quasi-likelihood model compared with 69% with the maximum likelihood model.

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Khalid, Haniza. "Spatial heterogeneity and spatial bias analyses in hedonic price models: some practical considerations." Bulletin of Geography. Socio-economic Series 28, no. 28 (June 1, 2015): 113–28. http://dx.doi.org/10.1515/bog-2015-0019.

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Abstract A great number of contemporary studies are incorporating explicit consideration of spatial effects in the estimation of hedonic price functions. At the most basic level, interactive spatial regime models are employed to detect the presence of spatial heterogeneity in datasets. A full-scale spatial analysis would include determination and adjustments for spatial lag and spatial error dependences. However, there is still plenty of room for future research to help unravel the numerous modelling and practical issues associated with a comprehensive spatial examination, such as the specification of the spatial dependence structure or functional ‘neighbourhoods’. Another important issue relates to the use of spatial multipliers to filter spatial bias particularly in models which use log-transformed variables. Estimation of a hedonic price function using Malaysian dataset of agricultural land sale values indicates spatial disaggregation and spatial dependence. However, diagnostic tests and actual estimation of spatial models do not always provide unambiguous conclusions while predicted errors do not vary all that much from those generated by simpler models. Despite the conceptual appeal of spatial analyses, the inefficiency attributable to spatial biases may not be large enough to cause critical errors in policy decisions.

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Li, Li, Ya Qi, Wei Shi, Yuan Wang, Wen Liu, and Man Hu. "A Meta-Analysis for Association of Maternal Smoking with Childhood Refractive Error and Amblyopia." Journal of Ophthalmology 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/8263832.

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Background. We aimed to evaluate the association between maternal smoking and the occurrence of childhood refractive error and amblyopia.Methods. Relevant articles were identified from PubMed and EMBASE up to May 2015. Combined odds ratio (OR) corresponding with its 95% confidence interval (CI) was calculated to evaluate the influence of maternal smoking on childhood refractive error and amblyopia. The heterogeneity was evaluated with the Chi-square-basedQstatistic and theI2test. Potential publication bias was finally examined by Egger’s test.Results. A total of 9 articles were included in this meta-analysis. The pooled OR showed that there was no significant association between maternal smoking and childhood refractive error. However, children whose mother smoked during pregnancy were 1.47 (95% CI: 1.12–1.93) times and 1.43 (95% CI: 1.23-1.66) times more likely to suffer from amblyopia and hyperopia, respectively, compared with children whose mother did not smoke, and the difference was significant. Significant heterogeneity was only found among studies involving the influence of maternal smoking on children’s refractive error (P<0.05;I2=69.9%). No potential publication bias was detected by Egger’s test.Conclusion. The meta-analysis suggests that maternal smoking is a risk factor for childhood hyperopia and amblyopia.

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Minkkinen, Pentti O., and Kim H. Esbensen. "Sampling of particulate materials with significant spatial heterogeneity - Theoretical modification of grouping and segregation factors involved with correct sampling errors: Fundamental Sampling Error and Grouping and Segregation Error." Analytica Chimica Acta 1049 (February 2019): 47–64. http://dx.doi.org/10.1016/j.aca.2018.10.056.

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Deutschman, Douglas H., Simon A. Levin, and Stephen W. Pacala. "ERROR PROPAGATION IN A FOREST SUCCESSION MODEL:THE ROLE OF FINE-SCALE HETEROGENEITY IN LIGHT." Ecology 80, no. 6 (September 1999): 1927–43. http://dx.doi.org/10.1890/0012-9658(1999)080[1927:epiafs]2.0.co;2.

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Casanoves, F., R. Macchiavelli, and M. Balzarini. "Error Variation in Multienvironment Peanut Trials: Within‐Trial Spatial Correlation and Between‐Trial Heterogeneity." Crop Science 45, no. 5 (September 2005): 1927–33. http://dx.doi.org/10.2135/cropsci2004.0547.

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WANG, Chun-Ping, Xi-Yuan HU, and Kun-Lun SHEN. "Heterogeneity of Error Variance and Its Effect on Variety Evaluation in Corn Regional Trials." Acta Agronomica Sinica 39, no. 3 (2013): 449. http://dx.doi.org/10.3724/sp.j.1006.2013.00449.

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Journal articles on the topic 'Heterogeneity Error'

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