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Journal articles on the topic 'Errors correlated'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

Fischer, Paul E., and Robert E. Verrecchia. "Correlated Forecast Errors." Journal of Accounting Research 36, no. 1 (1998): 91. http://dx.doi.org/10.2307/2491322.

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2

Schweizer, Karl. "On Correlated Errors." European Journal of Psychological Assessment 28, no. 1 (September 2012): 1–2. http://dx.doi.org/10.1027/1015-5759/a000094.

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3

ARIMITSU, T., T. HAYASHI, S. KITAJIMA, and F. SHIBATA. "QUANTUM ERROR-CORRECTION FOR SPATIALLY CORRELATED ERRORS." International Journal of Quantum Information 06, supp01 (July 2008): 575–80. http://dx.doi.org/10.1142/s0219749908003803.

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It is shown that errors due to spatially correlated noises can be corrected by the quantum error-correction code and error-correction procedure prepared for those for independent noises. A model of noisy-channel which is under the influence of spatially correlated quantum Brownian motion is investigated within the framework of non-equilibrium thermo field dynamics that is a canonical operator formalism for dissipative quantum systems.
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4

Lu, Feng, and Dan C. Marinescu. "Quantum Error Correction of Time-correlated Errors." Quantum Information Processing 6, no. 4 (July 7, 2007): 273–93. http://dx.doi.org/10.1007/s11128-007-0058-1.

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5

Das, Rabindra Nath. "Slope Rotatability with Correlated Errors." Calcutta Statistical Association Bulletin 54, no. 1-2 (March 2003): 57–70. http://dx.doi.org/10.1177/0008068320030105.

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In Das (Cal. Statist. Assoc. Bull. 47, 1997. 199 -214) a study of second order rotatable designs with correlated errors was initiated. Robust second order rotatable designs under autocorrelated structures was developed. In this paper, general conditions for second order slope rotatability have been derived assuming errors have a general correlated error structure. Further, these conditions have been simplified under the intra-class structure of errors and verified with uncorrelated case.
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6

De Brabanter, K., J. De Brabanter, J. A. K. Suykens, and B. De Moor. "Kernel Regression with Correlated Errors." IFAC Proceedings Volumes 43, no. 6 (2010): 13–18. http://dx.doi.org/10.3182/20100707-3-be-2012.0001.

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7

Yang, Yuhong, Yuedong Wang, and Jean Opsomer. "Nonparametric Regressin with Correlated Errors." Statistical Science 16, no. 2 (May 2001): 134–53. http://dx.doi.org/10.1214/ss/1009213287.

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8

Kim, T. Y. "Nonparametric detection of correlated errors." Biometrika 91, no. 2 (June 1, 2004): 491–96. http://dx.doi.org/10.1093/biomet/91.2.491.

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9

RIFE, JASON, and DEMOZ GEBRE-EGZIABHER. "Symmetric Overbounding of Correlated Errors." Navigation 54, no. 2 (June 2007): 109–24. http://dx.doi.org/10.1002/j.2161-4296.2007.tb00398.x.

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10

Appadoo, S. S., A. Thavaneswaran, and Jagbir Singh. "RCA models with correlated errors." Applied Mathematics Letters 19, no. 8 (August 2006): 824–29. http://dx.doi.org/10.1016/j.aml.2005.11.003.

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11

de Wit, C. "Optimal Position Estimate from a Stars Fix with Correlated Errors." Journal of Navigation 45, no. 1 (January 1992): 126–33. http://dx.doi.org/10.1017/s0373463300010535.

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This paper concerns the estimate of a ship's position from a sequence of measurements of 3, 4 or 5 altitudes of stars or planets. The measurement errors are assumed to be mutually correlated. This correlation is mainly caused by the appearance of so-called systematic errors. It is the main intention of this paper to dispense with the policy of pre-separation of these systematic errors. Instead, the equal contribution of some partial errors to the total measuring errors is fully accounted for by the formation of the covariance matrix, which corresponds with the vector of measurement errors. The algorithm produces a position estimate with a bias-free estimation error, meaning that the estimate and the error are stochastically independent. The resulting covariance matrix of the position error has a minimal trace.
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12

Stewart, L. M., S. L. Dance, and N. K. Nichols. "Correlated observation errors in data assimilation." International Journal for Numerical Methods in Fluids 56, no. 8 (2008): 1521–27. http://dx.doi.org/10.1002/fld.1636.

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13

Morgan, John P., and Nizam Uddin. "Two-Dimensional Design for Correlated Errors." Annals of Statistics 19, no. 4 (December 1991): 2160–82. http://dx.doi.org/10.1214/aos/1176348391.

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14

Basu, Sabyasachi, and Gregory C. Reinsel. "Regression Models with Spatially Correlated Errors." Journal of the American Statistical Association 89, no. 425 (March 1994): 88–99. http://dx.doi.org/10.1080/01621459.1994.10476449.

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15

Nickerson, Naomi H., and Benjamin J. Brown. "Analysing correlated noise on the surface code using adaptive decoding algorithms." Quantum 3 (April 8, 2019): 131. http://dx.doi.org/10.22331/q-2019-04-08-131.

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Laboratory hardware is rapidly progressing towards a state where quantum error-correcting codes can be realised. As such, we must learn how to deal with the complex nature of the noise that may occur in real physical systems. Single qubit Pauli errors are commonly used to study the behaviour of error-correcting codes, but in general we might expect the environment to introduce correlated errors to a system. Given some knowledge of structures that errors commonly take, it may be possible to adapt the error-correction procedure to compensate for this noise, but performing full state tomography on a physical system to analyse this structure quickly becomes impossible as the size increases beyond a few qubits. Here we develop and test new methods to analyse blue a particular class of spatially correlated errors by making use of parametrised families of decoding algorithms. We demonstrate our method numerically using a diffusive noise model. We show that information can be learnt about the parameters of the noise model, and additionally that the logical error rates can be improved. We conclude by discussing how our method could be utilised in a practical setting blue and propose extensions of our work to study more general error models.
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16

Lach, Saul. "Decomposition of Variables and Correlated Measurement Errors." International Economic Review 34, no. 3 (August 1993): 715. http://dx.doi.org/10.2307/2527190.

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17

Kang, Joonsung. "Minimum Hellinger distance estimation for correlated errors." Journal of the Korean Data And Information Science Society 30, no. 1 (January 31, 2019): 219–31. http://dx.doi.org/10.7465/jkdi.2019.30.1.219.

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18

Williams, E. R., and J. A. John. "CONSTRUCTION OF CROSSOVER DESIGNS WITH CORRELATED ERRORS." Australian & New Zealand Journal of Statistics 49, no. 1 (January 31, 2007): 61–68. http://dx.doi.org/10.1111/j.1467-842x.2006.00463.x.

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19

Chan, David, Robert Kohn, and Chris Kirby. "Multivariate Stochastic Volatility Models with Correlated Errors." Econometric Reviews 25, no. 2-3 (September 2006): 245–74. http://dx.doi.org/10.1080/07474930600713309.

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20

Singh, Rakhi, Joachim Kunert, and John Stufken. "On optimal fMRI designs for correlated errors." Journal of Statistical Planning and Inference 212 (May 2021): 84–96. http://dx.doi.org/10.1016/j.jspi.2020.08.003.

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21

Uddin, Nizam. "MV-optimal block designs for correlated errors." Statistics & Probability Letters 78, no. 17 (December 2008): 2926–31. http://dx.doi.org/10.1016/j.spl.2008.04.017.

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22

Dette, Holger, Andrey Pepelyshev, and Anatoly Zhigljavsky. "Optimal designs in regression with correlated errors." Annals of Statistics 44, no. 1 (February 2016): 113–52. http://dx.doi.org/10.1214/15-aos1361.

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23

Altman, N. S. "Kernel Smoothing of Data with Correlated Errors." Journal of the American Statistical Association 85, no. 411 (September 1990): 749–59. http://dx.doi.org/10.1080/01621459.1990.10474936.

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24

Francisco-Fernández, M., and J. M. Vilar-Fernández. "LOCAL POLYNOMIAL REGRESSION ESTIMATION WITH CORRELATED ERRORS." Communications in Statistics - Theory and Methods 30, no. 7 (June 30, 2001): 1271–93. http://dx.doi.org/10.1081/sta-100104745.

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25

Balcerak, Ernie. "Models should account for correlated observation errors." Eos, Transactions American Geophysical Union 94, no. 44 (October 29, 2013): 408. http://dx.doi.org/10.1002/2013eo440016.

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26

Glasbey, C. A. "Examples of Regression with Serially Correlated Errors." Statistician 37, no. 3 (1988): 277. http://dx.doi.org/10.2307/2348165.

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27

REILMAN, MIRIAM A., RICHARD F. GUNST, and MANI Y. LAKSHMINARAYANAN. "Structural model estimation with correlated measurement errors." Biometrika 72, no. 3 (1985): 669–72. http://dx.doi.org/10.1093/biomet/72.3.669.

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28

KUNERT, JOACHIM. "Neighbour balanced block designs for correlated errors." Biometrika 74, no. 4 (1987): 717–24. http://dx.doi.org/10.1093/biomet/74.4.717.

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29

Wang, Yuedong. "Smoothing Spline Models with Correlated Random Errors." Journal of the American Statistical Association 93, no. 441 (March 1998): 341–48. http://dx.doi.org/10.1080/01621459.1998.10474115.

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30

Jacobsen, Sol H., and Florian Mintert. "Optimal correction of independent and correlated errors." Journal of Physics A: Mathematical and Theoretical 47, no. 4 (January 8, 2014): 045306. http://dx.doi.org/10.1088/1751-8113/47/4/045306.

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31

Agiakloglou, Christos. "Resolving spurious regressions and serially correlated errors." Empirical Economics 45, no. 3 (November 3, 2012): 1361–66. http://dx.doi.org/10.1007/s00181-012-0647-4.

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32

Kim, Gunky, and Raymond Chambers. "Unbiased regression estimation under correlated linkage errors." Stat 4, no. 1 (February 2015): 32–45. http://dx.doi.org/10.1002/sta4.76.

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33

Zhang, Jianlin, Bo Yuan, Zheng Jiang, Yuanyue Guo, and Dongjin Wang. "Microwave Staring Correlated Imaging Method Based on Steady Radiation Fields Sequence." Sensors 20, no. 23 (November 30, 2020): 6859. http://dx.doi.org/10.3390/s20236859.

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Microwave Staring Correlated Imaging (MSCI) is a newly proposed computational high-resolution imaging technique. The imaging performance of MSCI with the existence of modeling errors depends on the properties of the imaging matrix and the relative perturbation error resulted from existing errors. In conventional transient-radiation-fields-based MSCI, which is commonly accomplished by utilizing random frequency-hopping (FH) waveforms, the multiple transmitters should be controlled individually and simultaneously. System complexity and control difficulty are hence increased, and various types of modeling errors are introduced as well. The computation accuracy of radiation fields is heavily worsened by the modeling errors, and the transient effect makes it hard to take direct and high-precision measurements of the radiation fields and calibrate the modeling errors with the measuring result. To simplify the system complexity and reduce error sources, in this paper, steady-radiation-fields-sequence-based MSCI (SRFS-MSCI) method is proposed. The multiple transmitters are excited with coherent signals at the same observation moment, with the signal frequency varying in the whole frequency band during the imaging process. By elaborately designing the array configuration and the amplitude and phase sequences of the coherent transmitters, the SRFS-MSCI is thus implemented. Comparing the system architecture of the proposed SRFS-MSCI with the conventional random FH-based MSCI, it can be found that the proposed method significantly reduces the number of baseband modules and simplifies the system architecture and control logic, which contributes to reducing error sources such as baseband synchronization errors and decreasing deterioration caused by error cascade. To further optimize the design parameters in the proposed SRFS-MSCI system, the Simulated Annealing (SA) algorithm is utilized to optimize the amplitude sequences, the phase sequences, and the antenna positions individually and jointly. Numerical imaging experiments and real-world imaging experiment demonstrate the effectiveness of the proposed SRFS-MSCI method that recognizable high-resolution recovery results are obtained with simplified system structure and optimized system parameters.
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34

Yan, Fei, Yiyi Chu, Kai Zhang, Fangfu Zhang, Narayan Bhandari, Gedeng Ruan, Zhaoyi Dai, et al. "Determination of adsorption isotherm parameters with correlated errors by measurement error models." Chemical Engineering Journal 281 (December 2015): 921–30. http://dx.doi.org/10.1016/j.cej.2015.07.021.

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35

Patacchini, Eleonora. "Unobserved Heterogeneity or Measurement Errors? Testing for Correlated Effects with Measurement Errors." Oxford Bulletin of Economics and Statistics 69, no. 6 (December 2007): 867–80. http://dx.doi.org/10.1111/j.1468-0084.2007.00485.x.

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36

D, Gold, Boulos K, Coolbrith N, and Piryatinsky I. "A-035 What Make Them Tick? The Clock Drawing Test and Correlations Between Cognitive and Functional Abilities." Archives of Clinical Neuropsychology 35, no. 6 (August 28, 2020): 825. http://dx.doi.org/10.1093/arclin/acaa068.035.

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Abstract Objective The Clock Drawing Test (CDT) is among the most researched cognitive measures and is frequently used to screen for neurocognitive disorders (NCDs). No study to date has investigated the relationship between qualitative errors on the CDT and independence in instrumental activities of daily living (IADLs) or discrete cognitive abilities. Therefore, this study sought to evaluate the correlations between qualitative errors on the CDT and IADL status as well as performance in individual cognitive domains. Method Data were retrospectively collected from patients seen at an outpatient clinic in eastern Massachusetts, including 16 healthy controls, 22 patients with mild NCD, and 35 patients with major NCD. Analyses were performed between qualitative errors on the CDT and patients’ scores on the Lawton IADL Scale, Mattis Dementia Rating Scale-2 (DRS-2), Digit Span Forward and Backward, Trail Making Test (TMT), and the Boston Naming Test (BNT). Results IADL scores were moderately correlated with CDT error types. DRS-2 scores were strongly correlated commission of qualitative errors. Strong to very strong correlations were observed between TMT parts A & B scores and all qualitative error types. BNT performance was strongly correlated with conceptual deficits and spatial/planning errors. Digit Span Forward and Backward scores showed low correlations with all CDT errors. Conclusions Functional status appears only moderately correlated with commission of various CDT errors; however, several cognitive measures showed high correlation with various CDT error types. These findings suggest that certain qualitative errors may be indicative of cognitive impairments warranting further workup. Clinical implications and future directions are discussed.
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37

Baireuther, Paul, Thomas E. O'Brien, Brian Tarasinski, and Carlo W. J. Beenakker. "Machine-learning-assisted correction of correlated qubit errors in a topological code." Quantum 2 (January 29, 2018): 48. http://dx.doi.org/10.22331/q-2018-01-29-48.

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A fault-tolerant quantum computation requires an efficient means to detect and correct errors that accumulate in encoded quantum information. In the context of machine learning, neural networks are a promising new approach to quantum error correction. Here we show that a recurrent neural network can be trained, using only experimentally accessible data, to detect errors in a widely used topological code, the surface code, with a performance above that of the established minimum-weight perfect matching (or blossom) decoder. The performance gain is achieved because the neural network decoder can detect correlations between bit-flip (X) and phase-flip (Z) errors. The machine learning algorithm adapts to the physical system, hence no noise model is needed. The long short-term memory layers of the recurrent neural network maintain their performance over a large number of quantum error correction cycles, making it a practical decoder for forthcoming experimental realizations of the surface code.
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38

Liao, C. T., C. H. Taylor, and H. K. Iyer. "Optimal balanced measurement designs when errors are correlated." Journal of Statistical Planning and Inference 84, no. 1-2 (March 2000): 295–321. http://dx.doi.org/10.1016/s0378-3758(99)00110-x.

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39

Pázman, Andrej, and Werner G. Müller. "Optimal design of experiments subject to correlated errors." Statistics & Probability Letters 52, no. 1 (March 2001): 29–34. http://dx.doi.org/10.1016/s0167-7152(00)00201-7.

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40

Cafaro, Carlo, and Stefano Mancini. "Repetition versus noiseless quantum codes for correlated errors." Physics Letters A 374, no. 26 (June 2010): 2688–700. http://dx.doi.org/10.1016/j.physleta.2010.04.047.

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41

Muller, W. G. "Measures for designs in experiments with correlated errors." Biometrika 90, no. 2 (June 1, 2003): 423–34. http://dx.doi.org/10.1093/biomet/90.2.423.

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42

Rao, S. S. "On multiple regression models with nonstationary correlated errors." Biometrika 91, no. 3 (September 1, 2004): 645–59. http://dx.doi.org/10.1093/biomet/91.3.645.

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43

de Groot, Peter J. "Correlated errors in phase-shifting laser Fizeau interferometry." Applied Optics 53, no. 19 (June 30, 2014): 4334. http://dx.doi.org/10.1364/ao.53.004334.

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44

Ou, Juchi, and Jeffrey M. Albert. "Robust Inference for Regression with Spatially Correlated Errors." Journal of Modern Applied Statistical Methods 10, no. 2 (November 1, 2011): 462–75. http://dx.doi.org/10.22237/jmasm/1320120360.

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45

Kuster, Mark. "Applying the Welch-Satterthwaite Formula to Correlated Errors." NCSLI Measure 8, no. 1 (March 2013): 42–55. http://dx.doi.org/10.1080/19315775.2013.11721629.

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46

Nath Das, Rabindra, and Sung H. Park. "Slope rotatability over all directions with correlated errors." Applied Stochastic Models in Business and Industry 22, no. 5-6 (2006): 445–57. http://dx.doi.org/10.1002/asmb.655.

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47

Tutmez, Bulent, and Ahmet Dag. "Mapping water chemical variables with spatially correlated errors." Environmental and Ecological Statistics 20, no. 1 (June 15, 2012): 19–35. http://dx.doi.org/10.1007/s10651-012-0205-4.

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48

McCartin, Brian J. "The geometry of linear regression with correlated errors." Statistics 39, no. 1 (February 2005): 1–11. http://dx.doi.org/10.1080/02331880412331328260.

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49

Lee, Young Kyung, Enno Mammen, and Byeong U. Park. "Bandwidth selection for kernel regression with correlated errors." Statistics 44, no. 4 (August 2010): 327–40. http://dx.doi.org/10.1080/02331880903138452.

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50

Näther, Wolfgang. "Exact designs for regression models with correlated errors." Statistics 16, no. 4 (January 1985): 479–84. http://dx.doi.org/10.1080/02331888508801879.

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