Relevant bibliographies by topics / Error response

Academic literature on the topic 'Error response'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Error response"

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Papila, Melih, and Raphael T. Haftka. "Response approximations - Noise, error repair, modeling errors." AIAA Journal 38 (January 2000): 2336–43. http://dx.doi.org/10.2514/3.14685.

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Rabbitt, Patrick. "Consciousness is slower than you think." Quarterly Journal of Experimental Psychology Section A 55, no. 4 (October 2002): 1081–92. http://dx.doi.org/10.1080/02724980244000080.

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In easy serial choice reaction time tasks (CRT tasks) young adults can very rapidly “correct” nearly all their errors by making the responses that they should have made (error-correcting responses). They are much less accurate at signalling their errors by making the same, deliberate, response to each (error-signalling responses), and they poorly remember errors that they have not signalled or corrected. When instructed to ignore errors they nevertheless involuntarily register them because the response immediately following them (responses following unacknowledged errors) are unusually slow, and they sometimes make involuntary error correction responses. Errors that are neither signalled nor remembered are registered at some level because responses following unacknowledged errors are slowed. Old age does not impair the accuracy of error correction or reduce the proportion of errors that are acknowledged because they are followed by unusually slow responses, but it does reduce the accuracy of error signalling and of recall of errors. Groups of 40 young adults (mean age 20.1 years, SD 1.1) and 40 older adults (mean 71.2 years, SD 5.1) signalled and recalled their errors increasingly accurately as intervals between each response and the next signal were increased from 150 ms to 1000 ms. Error signalling and recall improved as response-signal interval (RSI) durations increased, reaching asymptote at RSIs of 800 ms for the young and 1000 ms for the older adults. Thus processes necessary for conscious and deliberate choice or error-signalling responses and for subsequent recall of errors require more than 150 ms to complete, are slowed by old age, and may be interrupted by onset of new signals occurring earlier than 800 to 1000 ms after completion of an incorrect response.
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Ratcliff, Roger, and Jeffrey N. Rouder. "Modeling Response Times for Two-Choice Decisions." Psychological Science 9, no. 5 (September 1998): 347–56. http://dx.doi.org/10.1111/1467-9280.00067.

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The diffusion model for two-choice real-time decisions is applied to four psychophysical tasks. The model reveals how stimulus information guides decisions and shows how the information is processed through time to yield sometimes correct and sometimes incorrect decisions. Rapid two-choice decisions yield multiple empirical measures: response times for correct and error responses, the probabilities of correct and error responses, and a variety of interactions between accuracy and response time that depend on instructions and task difficulty. The diffusion model can explain all these aspects of the data for the four experiments we present. The model correctly accounts for error response times, something previous models have failed to do. Variability within the decision process explains how errors are made, and variability across trials correctly predicts when errors are faster than correct responses and when they are slower.
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Papila, Melih, and Raphael T. Haftka. "Response Surface Approximations: Noise, Error Repair, and Modeling Errors." AIAA Journal 38, no. 12 (December 2000): 2336–43. http://dx.doi.org/10.2514/2.903.

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Crump, Kenny S. "The Effect of Random Error in Exposure Measurement upon the Shape of the Exposure Response." Dose-Response 3, no. 4 (October 1, 2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.002.

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Although statistical analyses of epidemiological data usually treat the exposure variable as being known without error, estimated exposures in epidemiological studies often involve considerable uncertainty. This paper investigates the theoretical effect of random errors in exposure measurement upon the observed shape of the exposure response. The model utilized assumes that true exposures are log-normally distributed, and multiplicative measurement errors are also log-normally distributed and independent of the true exposures. Under these conditions it is shown that whenever the true exposure response is proportional to exposure to a power r, the observed exposure response is proportional to exposure to a power K, where K < r. This implies that the observed exposure response exaggerates risk, and by arbitrarily large amounts, at sufficiently small exposures. It also follows that a truly linear exposure response will appear to be supra-linear—i.e., a linear function of exposure raised to the K-th power, where K is less than 1.0. These conclusions hold generally under the stated log-normal assumptions whenever there is any amount of measurement error, including, in particular, when the measurement error is unbiased either in the natural or log scales. Equations are provided that express the observed exposure response in terms of the parameters of the underlying log-normal distribution. A limited investigation suggests that these conclusions do not depend upon the log-normal assumptions, but hold more widely. Because of this problem, in addition to other problems in exposure measurement, shapes of exposure responses derived empirically from epidemiological data should be treated very cautiously. In particular, one should be cautious in concluding that the true exposure response is supra-linear on the basis of an observed supra-linear form.
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Kim, ChangHwan, and Christopher R. Tamborini. "Response Error in Earnings." Sociological Methods & Research 43, no. 1 (November 5, 2012): 39–72. http://dx.doi.org/10.1177/0049124112460371.

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Chesher, Andrew, Montezuma Dumangane, and Richard J. Smith. "Duration response measurement error." Journal of Econometrics 111, no. 2 (December 2002): 169–94. http://dx.doi.org/10.1016/s0304-4076(02)00103-3.

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Ehlis, Ann-Christine, Martin J. Herrmann, Achim Bernhard, and Andreas J. Fallgatter. "Monitoring of Internal and External Error Signals." Journal of Psychophysiology 19, no. 4 (January 2005): 263–69. http://dx.doi.org/10.1027/0269-8803.19.4.263.

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Abstract: In the present study, a modified version of the Eriksen Flanker Task has been used to study event-related potentials (ERPs) elicited by correct responses, response errors, and invalid negative response feedback following correct button presses (“PC-error trials”). Conventional error potentials (error related negativity [ERN/Ne]; error-positivity [Pe]) were observed after incorrect button presses but not following negative response feedback in PC-error trials. Furthermore, a late positive deflection occurred specifically after PC-errors (Late positivity [PL]), which might reflect a conscious processing of these unexpected events. The results imply some restrictions for the notion that the ERN/Ne reflects the activity of a general and “generic” neural error-detection system in the human brain. Furthermore, the existence of an “event-detection system” is indicated, which might be involved in the processing of events that violate learned expectations.
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Kolev, V., C. Beste, M. Falkenstein, and J. Yordanova. "Error-Related Oscillations." Journal of Psychophysiology 23, no. 4 (January 2009): 216–23. http://dx.doi.org/10.1027/0269-8803.23.4.216.

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The present study assesses the origins of reduction of error negativity (Ne) with advancing age in humans. Response-related potentials were recorded from young (mean age 22.5 years, n = 10) and older (mean age 58.3 years, n = 11) adults while they performed a four-choice reaction task (4CRT) in two modalities, auditory and visual. Trials from correct and error responses were analyzed separately for each modality. To achieve a reference-free evaluation, the current source density (CSD) of the signals was computed. RRPs were analyzed in the time-frequency (TF) domain by means of wavelet decomposition. Two TF components of RRPs from the delta (1.5–3.5 Hz) and theta (3.5–7 Hz) frequency ranges were assessed. The measured parameters were total power reflecting both the phase-locked and non-phase-locked activity, and phase-locking factor (PLF) reflecting the strength of phase-synchronization with stimulus, independent of magnitude. It was found that the total power of both the delta and theta TF components increased after errors in the two age groups, although this increase was more pronounced in young than older adults. Response-locked synchronization of delta responses also increased after errors, with this synchronizing ability being preserved in older subjects. What differentiated the error processing in the two age groups was the synchronization of theta oscillations with error responses, with this parameter being substantially reduced in older subjects. The results demonstrate that Ne reduction with aging is the result of an overall decrease in the power of delta and theta components, primarily of a decrease in the response-locked synchronization of theta oscillations after errors.
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Voormann, Anne, Annelie Rothe-Wulf, Jeffrey J. Starns, and Karl Christoph Klauer. "Does speed of recognition predict two-alternative forced-choice performance? Replicating and extending Starns, Dubé, and Frelinger (2018)." Quarterly Journal of Experimental Psychology 74, no. 1 (October 21, 2020): 122–34. http://dx.doi.org/10.1177/1747021820963033.

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Does the speed of single-item recognition errors predict performance in subsequent two-alternative forced-choice (2AFC) trials that include an item with a previous error response? Starns, Dubé, and Frelinger found effects of this kind in two experiments and accounted for them in terms of continuous memory-strength signal guiding recognition decisions. However, the effects of error speed might just as well only reflect an artefact due to an error-correction strategy that uses response latency as a heuristic cue to guide 2AFC responses, elicited through confounding factors in their experimental design such as error-correction instructions and feedback. Using two conditions, a replication condition, replicating the procedure from Starns et al., and an extension condition (each n = 130), controlling for the named shortcomings, we replicated the error speed effect. In both conditions, speed of errors in a single-item recognition task was predictive of subsequent 2AFC performance, including the respective error item. To be more precise, fast errors were associated with decreased 2AFC performance. As there was no interaction with the factor condition, the results support the idea that speed of single-item recognition responses reflects the amount of memory information underlying the respective response rather than being used for a simple error-correction strategy to improve 2AFC performance.
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Dissertations / Theses on the topic "Error response"

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Boaventura, Guimareas Dumangane Montezuma. "Essays on duration response measurement error." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368683.

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Ling, Xiang. "Adaptive design in dose-response studies." Columbus, Ohio : Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1133365136.

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Rashid, Horn Susan G. "What's wrong and who cares? : reader reaction to error /." View online ; access limited to URI, 2006. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/3248240.

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Chai, Lauren (Lauren Amy). "Twist error response of periodic lattices to strain energy distribution." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101331.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 79-80).
Periodic lattices, when used as assembly scaffolds, can augment pre-existing 2D manufacturing techniques to fabricate 3D structures with heterogeneous materials, components and architecture such as human organs for transplant patients, and micro batteries. Periodic lattices are first preformed and then folded using externally actuating walls that properly constrain the lattice edges. Angular errors of the actuation walls cause the lattice to distort, misaligning components on the lattice panels. Research into the response of a lattice to geometric errors imposed on the lattice edges does not account for how much strain energy is put into the lattice during folding and its impact on the lattice distortion response and magnitude. This thesis shows how design parameters of the lattice can change the magnitude and shape of the twist response of the lattice when external geometric errors are applied to the lattice during folding. A Buckingham Pi analysis was used to show how the twist response of the lattice due to an external angular wall error depends on the torsional stiffnesses of the panels, the initial fold angle of the preformed accordion unit in the lattice and the angular wall error. A FEA simulation study quantified the Buckingham Pi results by varying the torsional stiffness ratio of the panels, the initial fold angle and the final lattice length after folding. The results showed that increasing the ratio of the torsional stiffnesses by two orders of magnitude decreases the magnitude of the response by as much as an order of magnitude and increases the asymmetry by 0.5 to 1.5 orders of magnitude. Increasing the initial fold angle by 50% increases the magnitude of the result by as much as 250% and decreases asymmetry by 26%.
by Lauren Amy Chai.
S.M.
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Davison, Jennifer J. "Response surface designs and analysis for bi-randomization error structures." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-10042006-143852/.

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Bedi, Aman. "The effects of response probability on commission errors in high go low no-go dual response versions of the sustained attention to response task (SART)." Thesis, University of Canterbury. Psychology, 2015. http://hdl.handle.net/10092/10520.

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In the current investigation, we modified the high Go low No-Go Sustained Attention to Response Task (SART) by replacing the single response on Go trials with a dual response (dual response SART or DR SART). In three experiments a total of 80 participants completed the SART and versions of the DR SART in which response probabilities varied from 50-50, through 70-30 to 90-10. The probability of No-Go withhold stimuli was .11 in all experiments. Using a dynamic utility based model proposed by Peebles and Bothell (2004) we predicted that the 50-50 DR-SART would dramatically reduce commission errors. Additionally, the model predicted that the probability of commission errors to be an increasing function of response frequency. Both predictions were confirmed. Although the increasing rate of commission errors with response probability can also be accommodated by the rationale originally proposed for the SART by its creators (Robertson, Manly, Andrade, Baddeley, & Yiend, 1997) the fact that the current DR SART results and SART findings in general can be accommodated by a utility model without need for any attention processes is a challenge to views that ascribe commission errors to lapses of sustained attention.
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McEwen, Peter A. "Trellis coding for partial response channels /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1999. http://wwwlib.umi.com/cr/ucsd/fullcit?p9968170.

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Kim, Hongman. "Statistical Modeling of Simulation Errors and Their Reduction via Response Surface Techniques." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/28390.

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Errors of computational simulations in design of a high-speed civil transport (HSCT) are investigated. First, discretization error from a supersonic panel code, WINGDES, is considered. Second, convergence error from a structural optimization procedure using GENESIS is considered along with the Rosenbrock test problem. A grid converge study is performed to estimate the order of the discretization error in the lift coefficient (CL) of the HSCT calculated from WINGDES. A response surface (RS) model using several mesh sizes is applied to reduce the noise magnification problem associated with the Richardson extrapolation. The RS model is shown to be more efficient than Richardson extrapolation via careful use of design of experiments. A programming error caused inaccurate optimization results for the Rosenbrock test function, while inadequate convergence criteria of the structural optimization produced error in wing structural weight of the HSCT. The Weibull distribution is successfully fit to the optimization errors of both problems. The probabilistic model enables us to estimate average errors without performing very accurate optimization runs that can be expensive, by using differences between two sets of results with different optimization control parameters such as initial design points or convergence criteria. Optimization results with large errors, outliers, produced inaccurate RS approximations. A robust regression technique, M-estimation implemented by iteratively reweighted least squares (IRLS), is used to identify the outliers, which are then repaired by higher fidelity optimizations. The IRLS procedure is applied to the results of the Rosenbrock test problem, and wing structural weight from the structural optimization of the HSCT. A nonsymmetric IRLS (NIRLS), utilizing one-sidedness of optimization errors, is more effective than IRLS in identifying outliers. Detection and repair of the outliers improve accuracy of the RS approximations. Finally, configuration optimizations of the HSCT are performed using the improved wing bending material weight RS models.
Ph. D.
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Prasad, N. G. Narasimha Carleton University Dissertation Mathematics. "Small area estimation and measurement of response error variance in surveys." Ottawa, 1985.

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Potter, Kevin Whitman. "Perfectionism, Decision-Making, and Post-error Slowing." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1418307845.

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Books on the topic "Error response"

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1949-, Means Barbara, National Center for Health Statistics (U.S.), and SRI International, eds. Cognitive research on response error in survey questions on smoking. Hyattsville, Md: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Health Statistics, 1992.

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Boer, A. de. Coorelation, error-localisation and updating of the second problem defined in GARTEUR AG11. Amsterdam: National Aerospace Laboratory, 1990.

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Spray, Judith A. The effect of item parameter estimation error on decisions made using the sequential probability ratio test. Iowa City, Iowa: American College Testing Program, 1988.

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Saris, Willem E. Variation in response functions: A source of measurement error in attitude research. Amsterdam: Sociometric Research Foundation, 1988.

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Ghatak, Subrata. Supply response in Turkish agriculture: An error correction and cointegration analysis, 1950-1990. [Leicester]: Faculty of Social Sciences, Dept. of Economics, University of Leicester, 1994.

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Ghatak, Subrata. Supply response in Turkish agriculture: An error correction and cointegration analysis: 1950-1990. Leicester: Department of Economics, University of Leicester, 1994.

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J, Donohue John. Measurement error, legalized abortion, and the decline in crime: A response to Foote and Goetz (2005). Cambridge, Mass: National Bureau of Economic Research, 2006.

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Error free mental measurements: Applying qualitative item response theory to assessment and program validation including a developmental theory of assessment. San Francisco: Austin & Winfield, Publishers, 1998.

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Tsang, K. M. A prediction-error estimation algorithm for the reconstruction of linear and nonlinear continuous time models from frequency response data. Sheffield: University of Sheffield, Dept. of Control Engineering, 1991.

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Mueller, Curt D. Statistical properties of physician surveys: Proxy response and survey error : additional evidence from the 1988 Physicians' practice cost and income survey : final report. Bethesda, Maryland: Project HOPE Center for Health Affairs, 1994.

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Book chapters on the topic "Error response"

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Carroll, R. J., D. Ruppert, and L. A. Stefanski. "Response Variable Error." In Measurement Error in Nonlinear Models, 229–42. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-4477-1_13.

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Denning, Jaime Rice, and James J. McCarthy. "Organizational Response to Error." In Quality Improvement and Patient Safety in Orthopaedic Surgery, 209–14. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07105-8_20.

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Billiet, Jaak, and Hideko Matsuo. "Non-Response and Measurement Error." In Handbook of Survey Methodology for the Social Sciences, 149–78. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3876-2_10.

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Woodhouse, E. J., and Patrick W. Hamlett. "Decision Making About Biotechnology: The Costs of Learning from Error." In The Social Response to Environmental Risk, 131–50. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2954-1_6.

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Westphal, L. C. "Steady-state response: error constants and system type." In Sourcebook of Control Systems Engineering, 407–18. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1805-1_17.

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Westphal, Louis C. "Steady state response: error constants and system type." In Handbook of Control Systems Engineering, 377–87. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1533-3_17.

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Le Ruyet, Didier, Han Vu Thien, and Hong Sun. "Properties of Finite Response Input Sequences of Recursive Convolutional Codes." In Applied Algebra, Algebraic Algorithms and Error-Correcting Codes, 324–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46796-3_32.

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Zhao, Yunfei, and Carol Smidts. "A Dynamic Mechanistic Model of Human Response Proposed for Human Reliability Analysis." In Advances in Human Error, Reliability, Resilience, and Performance, 261–70. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60645-3_26.

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Angelo, Robert. "UVR Measurement Error Sources: Spectral Response of Filtered Broadband Detectors." In Biologic Effects of Light 2001, 57–63. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0937-0_4.

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Quicke, Donald L. J., Buntika A. Butcher, and Rachel A. Kruft Welton. "Count data as response variable." In Practical R for biologists: an introduction, 147–54. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0012.

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Abstract This chapter is devoted specifically to count data for three reasons: (i) they are common in ecological studies (e.g. clutch sizes, numbers of fledglings from a nest, numbers of seeds per pod...); (ii) they are simple to collect and are therefore often the data collected by students (e.g. numbers of beetles in a pitfall trap, number of pollinator visits to flowers...); and (iii) they pose numerous issues that linear models with their normal error structure cannot deal with. Two studies will be examined with the response variable being counts, starting with one that nearly fits the ideals of a Poisson distribution well, the other less so. Example 1 deals with fledgling numbers in relation to clutch initiation date. The data are on the northern cardinal bird, Cardinalis cardinalis, and were collected to test the hypothesis that birds that start their clutches later may suffer higher pre-fledging offspring mortality. Example 2 focuses on pollinator flower visits in Passiflora speciosa in relation to flower size.
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Conference papers on the topic "Error response"

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Stiber, Maia, Russell Taylor, and Chien-Ming Huang. "Modeling Human Response to Robot Errors for Timely Error Detection." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981726.

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Fergoug, M., N. Feld, S. Forest, B. Marchand, and A. Parret-Fréaud. "Model Error Estimation Based on Asymptotic Homogenization for Periodic Heterogeneous Structures." In VIII Conference on Mechanical Response of Composites. CIMNE, 2021. http://dx.doi.org/10.23967/composites.2021.069.

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Romero, Vicente, Raisa Slepoy, Laura Swiler, Anthony Giunta, and T. Krishnamurthy. "Error Estimation Approaches for Progressive Response Surfaces." In 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-1822.

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Wen, Yuejiang, and Yingjie Lao. "Efficient PUF Error Correction through Response Weighting." In 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018. http://dx.doi.org/10.1109/mwscas.2018.8623979.

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Márialigeti, János, and László Lovas. "Analysis of Load Dependent Dynamic Transmission Error Response of Gears With Random Pitch Error." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21619.

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Abstract For more realistic gear dynamic behaviour predictions, detailed gear dynamic models are needed, allowing taking into consideration the most important influencing factors. System model is presented, based on the separate handling of individual tooth pairs, with their specific profile corrections, manufacturing errors etc. Further on, non-linear single tooth pair force-deflection curve is considered, resulting in load dependent eigenfrequency characteristics. Simulation results are presented for gears with randomly distributed pitch errors. Gears with normal tooth profile and with tip relief are compared, and vibration response characteristics are analysed based on Fourier analysis of simulated transmission error response curves.
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Ramu, Palaniappan, Nam H. Kim, and Raphael T. Haftka. "Error Amplification in Failure Probability Estimates of Small Errors in Response Surface Approximations." In SAE World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2007. http://dx.doi.org/10.4271/2007-01-0549.

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Eschbach, Reiner. "Error diffusion with homogeneous highlight and shadow response." In Advanced Imaging and Network Technologies, edited by Jan Bares, Christopher T. Bartlett, Paul A. Delabastita, Jose L. Encarnacao, Nelson V. Tabiryan, Panos E. Trahanias, and Arthur R. Weeks. SPIE, 1997. http://dx.doi.org/10.1117/12.266338.

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Patel, Grishma, and Gaurang Panchal. "Quick response codes decodability improvements using error correction levels." In 2017 International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2017. http://dx.doi.org/10.1109/iss1.2017.8389404.

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Balakrishnan, A. V. "Attitude Error Response of Structures to Actuator/Sensor Noise." In 1991 American Control Conference. IEEE, 1991. http://dx.doi.org/10.23919/acc.1991.4791624.

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Maru, Koichi, Masahiro Okawa, and Hisato Uetsuka. "Influence of Statistical Phase Error on AWG Spectral Response." In 1999 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 1999. http://dx.doi.org/10.7567/ssdm.1999.e-11-3.

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Reports on the topic "Error response"

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Parra-Polanía, Julián Andrés, and Carmiña Ofelia Vargas-Riaño. Changes in GDP's measurement error volatility and response of the monetary policy rate : two approaches. Bogotá, Colombia: Banco de la República, March 2014. http://dx.doi.org/10.32468/be.814.

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Donohue, John, and Steven Levitt. Measurement Error, Legalized Abortion, and the Decline in Crime: A Response to Foote and Goetz (2005). Cambridge, MA: National Bureau of Economic Research, January 2006. http://dx.doi.org/10.3386/w11987.

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Brown, Nicolas. The Impact of Leader Race and Gender on Perceptions of Organizations in Response to Corporate Error. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6430.

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Gupta, Shanti S., and Xun Lin. On Selecting the Strongest Linear Relationship Between a Response Variable and an Explanatory Variable in Measurement Error Models. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada384447.

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Patel, Reena, David Thompson, Guillermo Riveros, Wayne Hodo, John Peters, and Felipe Acosta. Dimensional analysis of structural response in complex biological structures. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41082.

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The solution to many engineering problems is obtained through the combination of analytical, computational and experimental methods. In many cases, cost or size constraints limit testing of full-scale articles. Similitude allows observations made in the laboratory to be used to extrapolate the behavior to full-scale system by establishing relationships between the results obtained in a scaled experiment and those anticipated for the full-scale prototype. This paper describes the application of the Buckingham Pi theorem to develop a set of non-dimensional parameters that are appropriate for describing the problem of a distributed load applied to the rostrum of the paddlefish. This problem is of interest because previous research has demonstrated that the rostrum is a very efficient structural system. The ultimate goal is to estimate the response of a complex, bio-inspired structure based on the rostrum to blast load. The derived similitude laws are verified through a series of numerical experiments having a maximum error of 3.39%.
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Gilbert, E. S., and J. J. Fix. Laboratory measurement error in external dose estimates and its effects on dose-response analyses of Hanford worker mortality data. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/379945.

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Gunay, Selim, Fan Hu, Khalid Mosalam, Arpit Nema, Jose Restrepo, Adam Zsarnoczay, and Jack Baker. Blind Prediction of Shaking Table Tests of a New Bridge Bent Design. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/svks9397.

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Considering the importance of the transportation network and bridge structures, the associated seismic design philosophy is shifting from the basic collapse prevention objective to maintaining functionality on the community scale in the aftermath of moderate to strong earthquakes (i.e., resiliency). In addition to performance, the associated construction philosophy is also being modernized, with the utilization of accelerated bridge construction (ABC) techniques to reduce impacts of construction work on traffic, society, economy, and on-site safety during construction. Recent years have seen several developments towards the design of low-damage bridges and ABC. According to the results of conducted tests, these systems have significant potential to achieve the intended community resiliency objectives. Taking advantage of such potential in the standard design and analysis processes requires proper modeling that adequately characterizes the behavior and response of these bridge systems. To evaluate the current practices and abilities of the structural engineering community to model this type of resiliency-oriented bridges, the Pacific Earthquake Engineering Research Center (PEER) organized a blind prediction contest of a two-column bridge bent consisting of columns with enhanced response characteristics achieved by a well-balanced contribution of self-centering, rocking, and energy dissipation. The parameters of this blind prediction competition are described in this report, and the predictions submitted by different teams are analyzed. In general, forces are predicted better than displacements. The post-tension bar forces and residual displacements are predicted with the best and least accuracy, respectively. Some of the predicted quantities are observed to have coefficient of variation (COV) values larger than 50%; however, in general, the scatter in the predictions amongst different teams is not significantly large. Applied ground motions (GM) in shaking table tests consisted of a series of naturally recorded earthquake acceleration signals, where GM1 is found to be the largest contributor to the displacement error for most of the teams, and GM7 is the largest contributor to the force (hence, the acceleration) error. The large contribution of GM1 to the displacement error is due to the elastic response in GM1 and the errors stemming from the incorrect estimation of the period and damping ratio. The contribution of GM7 to the force error is due to the errors in the estimation of the base-shear capacity. Several teams were able to predict forces and accelerations with only moderate bias. Displacements, however, were systematically underestimated by almost every team. This suggests that there is a general problem either in the assumptions made or the models used to simulate the response of this type of bridge bent with enhanced response characteristics. Predictions of the best-performing teams were consistently and substantially better than average in all response quantities. The engineering community would benefit from learning details of the approach of the best teams and the factors that caused the models of other teams to fail to produce similarly good results. Blind prediction contests provide: (1) very useful information regarding areas where current numerical models might be improved; and (2) quantitative data regarding the uncertainty of analytical models for use in performance-based earthquake engineering evaluations. Such blind prediction contests should be encouraged for other experimental research activities and are planned to be conducted annually by PEER.
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Kott, Phillip S. The Role of Weights in Regression Modeling and Imputation. RTI Press, April 2022. http://dx.doi.org/10.3768/rtipress.2022.mr.0047.2203.

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When fitting observations from a complex survey, the standard regression model assumes that the expected value of the difference between the dependent variable and its model-based prediction is zero, regardless of the values of the explanatory variables. A rarely failing extended regression model assumes only that the model error is uncorrelated with the model’s explanatory variables. When the standard model holds, it is possible to create alternative analysis weights that retain the consistency of the model-parameter estimates while increasing their efficiency by scaling the inverse-probability weights by an appropriately chosen function of the explanatory variables. When a regression model is used to impute for missing item values in a complex survey and when item missingness is a function of the explanatory variables of the regression model and not the item value itself, near unbiasedness of an estimated item mean requires that either the standard regression model for the item in the population holds or the analysis weights incorporate a correctly specified and consistently estimated probability of item response. By estimating the parameters of the probability of item response with a calibration equation, one can sometimes account for item missingness that is (partially) a function of the item value itself.
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Schoen, Robert, Xiaotong Yang, and Gizem Solmaz. Psychometric Report for the 2019 Knowledge for Teaching Early Elementary Mathematics (K-TEEM) Test. Florida State University Libraries, May 2021. http://dx.doi.org/10.33009/lsi.1620243057.

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The 2019 Knowledge for Teaching Early Elementary Mathematics (2019 K-TEEM) test measures teachers’ mathematical knowledge for teaching early elementary mathematics. This report presents information about a large-scale field test of the 2019 K-TEEM test with 649 practicing educators. The report contains information about the development process used for the test; a description of the sample; descriptions of the procedures used for data entry, scoring of responses, and analysis of data; recommended scoring procedures; and findings regarding the distribution of test scores, standard error of measurement, and marginal reliability. The intended use of the data from the 2019 K-TEEM test is to serve as a measure of teacher knowledge that will be used in a randomized controlled trial to investigate the impact—and variation in impact—of a teacher professional-development program for early elementary teachers.
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Pulugurtha, Srinivas S., and Raghuveer Gouribhatla. Drivers’ Response to Scenarios when Driving Connected and Automated Vehicles Compared to Vehicles with and without Driver Assist Technology. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.1944.

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Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver assistance systems (ADAS) that aid in various driving tasks. These features are aimed at enhancing safety by either warning drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane. These features are already part of vehicles with Driver Assistance Technology, and they are vital for successful deployment of connected and automated vehicles in the near future. However, drivers' responses to driving vehicles with advanced features have been meagerly explored. This research evaluates driver participants' response to scenarios when driving connected and automated vehicles compared to vehicles with and without Driver Assistance Technology. The research developed rural, urban, and freeway driving scenarios in a driver simulator and tested on participants sixteen years to sixty-five years old. The research team explored two types of advanced features by categorizing them into warnings and automated features. The results show that the advanced features affected driving behavior by making driver participants less aggressive and harmonizing the driving environment. This research also discovered that the type of driving scenario influences the effect of advanced features on driver behavior. Additionally, aggressive driving behavior was observed most in male participants and during nighttime conditions. Rainy conditions and female participants were associated with less aggressive driving behavior. The findings from this research help to assess driver behavior when driving vehicles with advanced features. They can be inputted into microsimulation software to model the effect of vehicles with advanced features on the performance of transportation systems, advancing technology that could eventually save millions of dollars and thousands of lives.
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