Gotowe bibliografie tematyczne / Averaging

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Gotowa bibliografia na temat „Averaging”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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Artykuły w czasopismach na temat "Averaging"

Nurser, A. J. George, and Mei-Man Lee. "Isopycnal Averaging at Constant Height. Part I: The Formulation and a Case Study." Journal of Physical Oceanography 34, no. 12 (December 1, 2004): 2721–39. http://dx.doi.org/10.1175/jpo2649.1.

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Abstract Simple Eulerian averaging of velocities, density, and tracers at constant position is the most natural way of averaging. However, Eulerian averaging gives incorrect watermass distributions and properties as well as spurious diabatic circulations such as the Deacon cell. Instead of averaging at constant height, averaging along isopycnals removes such fictitious mixing and diabatic circulations. Such isopycnal averaging is normally performed at constant latitude, that is, averaging along isopynals as they heave up and down. As a result, height information is lost and the sea surface becomes much warmer (or lighter) than with simple Eulerian averaging. In fact, averaging can be performed along arbitrarily aligned surfaces. This study considers a particular case in which isopycnal averaging is performed at constant height. Thus, this new isopycnal averaging follows isopycnals as they meander horizontally at constant z. Height information is now retained at the cost of losing latitudinal information. The advantage of this averaging is that it avoids the problem of giving a surface that is too warm. Associated with this new isopycnal averaging, a “vertical” transport streamfunction in (ρ, z) space can be defined, in analogy to the conventional meridional overturning streamfunction in (y, ρ) space. Here in Part I, this constant-height isopycnal averaging is explained and illustrated in an idealized zonal channel model. In Part II the relationship between the two different isopycnal averagings and the Eulerian mean eddy flux divergence is explored.

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Sanders, Jan. "Averaging." Scholarpedia 1, no. 11 (2006): 1760. http://dx.doi.org/10.4249/scholarpedia.1760.

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Zhao, Shangwei, Xinyu Zhang, and Yichen Gao. "Model averaging with averaging covariance matrix." Economics Letters 145 (August 2016): 214–17. http://dx.doi.org/10.1016/j.econlet.2016.06.011.

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Sanders, Paul. "Averaging Sequences." Mathematical Gazette 78, no. 483 (November 1994): 326. http://dx.doi.org/10.2307/3620209.

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Smith, Scott G. "Recursive Averaging." Mathematics Teacher 108, no. 7 (March 2015): 553–57. http://dx.doi.org/10.5951/mathteacher.108.7.0553.

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The limit of a recursive sequence in which every term is the average of the preceding two is discovered; conclusions are generalized to similar sequences; and a theorem is referenced in which a general limit formula has been proved.

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Campbell, L. L. "Averaging entropy." IEEE Transactions on Information Theory 41, no. 1 (1995): 338–39. http://dx.doi.org/10.1109/18.370086.

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BREITHARDT, GÜNTER, MARTIN BORGGREFE, and ANTONI MARTINEZ-RUBIO. "Signal Averaging." Annals of the New York Academy of Sciences 601, no. 1 Electrocardio (September 1990): 180–96. http://dx.doi.org/10.1111/j.1749-6632.1990.tb37300.x.

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Markley, F. Landis, Yang Cheng, John L. Crassidis, and Yaakov Oshman. "Averaging Quaternions." Journal of Guidance, Control, and Dynamics 30, no. 4 (July 2007): 1193–97. http://dx.doi.org/10.2514/1.28949.

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Dubil, Robert. "Investment Averaging." Journal of Wealth Management 7, no. 4 (January 31, 2005): 35–42. http://dx.doi.org/10.3905/jwm.2005.470608.

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Harris, W. F. "Averaging Powers." Optometry and Vision Science 77, no. 5 (May 2000): 235. http://dx.doi.org/10.1097/00006324-200005000-00009.

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Rozprawy doktorskie na temat "Averaging"

Dona', Eleonora <1987&gt. "Dollar Cost Averaging VS Suited Dollar Cost Averaging." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/6960.

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The Dollar Cost Averaging is a strategy based on periodic investment of a fixed amount of money, even small one, into a stock or a portfolio each interval over a given period of time. In this way it may be possible to reduce the volatility effects on the market, since each payments could be done both with positive and with negative conditions. What if the investment is not regular in time, but changes according to the market trend? This thesis project aims to compare the standard Dollar Cost Averaging with a suited plan. The amount of money for each instalment will be kept the same as for the standard plan, what will be changed is the instant of time of each investment, that it will be chosen according to the market trend.

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Ullah, Barkat. "Signal Averaging for Digitizer ADQ214." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143779.

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Signal averaging is a signal processing technique applied in the time domain, intended to increase the strength of a signal relative to noise that is obscuring it. From a very long sequence of data, a number of smaller data sequences called records are collected. The form of averaging performed in this thesis was not among samples within a record, but among samples from different records. For example, let's say a sample x(n, k) which is a sample n from record k, where 1 <= n <= N and N is the record size, and 1 <= k <= K, where K is the total number of records it would perform the averaging. Input signals for multi-record is periodic, typically repeated pulses. These records are stored in the memory of the Signal Processing (SP) Devices Digitizer ADQ214. Averaging is being implemented in two ways: software implementation and hardware implementation. In a software implementation the stored records are read out from a Digitizer to PC over a USB interface and averaging is performed in a PC with Matlab. Averaging in a PC takes a significant amount of time because of reading out data through USB interface. The amount of records and number of samples per record play an important role in transferring a record from the Digitizer on board DDR memory to the PC through a USB interface. A large number of records and long record length increases the time to perform averaging. This limitation is removed by implementing averaging in hardware. Verilog, a hardware description language is being used for designing the averaging unit in one of the Virtex5 FPGAs available on the Digitizer ADQ214. Performing averaging in hardware takes much less time than averaging in software. In a hardware implementation it is required to transfer data, which is the result in this case, only once from the Digitizer board to the PC regardless of the number of records under consideration.

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Chaput, Philippe. "Approximating Markov processes by averaging." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66654.

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We recast the theory of labelled Markov processes in a new setting, in a way "dual" to the usual point of view. Instead of considering state transitions as a collection of subprobability distributions on the state space, we view them as transformers of real-valued functions. By generalizing the operation of conditional expectation, we build a category consisting of labelled Markov processes viewed as a collection of operators; the arrows of this category behave as projections on a smaller state space. We define a notion of equivalence for such processes, called bisimulation, which is closely linked to the usual definition for probabilistic processes. We show that we can categorically construct the smallest bisimilar process, and that this smallest object is linked to a well-known modal logic. We also expose an approximation scheme based on this logic, where the state space of the approximants is finite; furthermore, we show that these finite approximants categorically converge to the smallest bisimilar process. Nous reconsidérons les processus de Markov étiquetés sous une nouvelle approche, dans un certain sens "dual'' au point de vue usuel. Au lieu de considérer les transitions d'état en état en tant qu'une collection de distributions de sous-probabilités sur l'espace d'états, nous les regardons en tant que transformations de fonctions réelles. En généralisant l'opération d'espérance conditionelle, nous construisons une catégorie où les objets sont des processus de Markov étiquetés regardés en tant qu'un rassemblement d'opérateurs; les flèches de cette catégorie se comportent comme des projections sur un espace d'états plus petit. Nous définissons une notion d'équivalence pour de tels processus, que l'on appelle bisimulation, qui est intimement liée avec la définition usuelle pour les processus probabilistes. Nous démontrons que nous pouvons construire, d'une manière catégorique, le plus petit processus bisimilaire à un processus donné, et que ce plus petit object est lié à une logique modale bien connue. Nous développons une méthode d'approximation basée sur cette logique, où l'espace d'états des processus approximatifs est fini; de plus, nous démontrons que ces processus approximatifs convergent, d'une manière catégorique, au plus petit processus bisimilaire.

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Verra, Christina. "Macroeconomic forecasting using model averaging." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/383.

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Recently, there has been a broadening concern on forecasting techniques that are applied on large data sets, since economists in business and management want to deal with the great magnitude of information. In this analysis, the issue of forecasting a large data set by using different model averaging approaches is addressed. In particular, Bayesian and frequentist model averaging methods are considered, including Bayesian model averaging (BMA), information theoretic model averaging (ITMA) and predictive likelihood model averaging (PLMA). The predictive performance of each scheme is compared with the most promising existing alternatives, namely benchmark AR model and the equal weighted model averaging (AV) scheme. An empirical application on Inflation forecasting for five countries using large data sets within the model averaging framework is applied. The average ARX model with weights constructed differently according to each model averaging scheme is compared with both the benchmark AR and the AV model. For the comparison of the accuracy of forecasts several performance indicators have been provided such as the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), the U-Theil’s Inequality Coefficient (U), Mean Square Forecast Error (MSFE) and the Relative Mean Square Forecast Error (RMSFE). Next, within the Granger causality framework through the Diebold & Mariano (DM) test and the Clark & McCracken (CM) test, whether the data-rich models represented by the three different model averaging schemes have made a statistically significant improvement relative to the benchmark forecasts has been tested. Critical values at 5% and at 10% have been calculated based on bootstrap approximation of the finite sample distribution of the DM and CM test statistics. The main outcome is that although the information theoretic model averaging scheme is a more powerful approach, the other two model averaging techniques can be regarded as useful alternatives.

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Rajagopalan, Shreevatsa. "Distributed averaging in dynamic networks." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62315.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (p. 39-40). The question of computing average of numbers present at nodes in a network in a distributed manner using gossip or message-passing algorithms has been of great recent interest across disciplines -- algorithms, control and robotics, estimation, social networks, etc. It has served as a non-trivial, representative model for an important class of questions arising in these disciplines and thus guiding intellectual progress over the past few decades. In most of these applications, there is inherent dynamics present, such as changes in the network topology in terms of communication links, changes in the values of numbers present at nodes, and nodes joining or leaving. The effect of dynamics in terms of communication links on the design and analysis of algorithms for averaging is reasonably well understood, e.g. [14][2][8][4]. However, little is known about the effect of other forms of dynamics. In this thesis, we study the effect of such types of dynamics in the context of maintaining average in the network. Specifically, we design dynamics-aware message-passing or gossip algorithm that maintains good estimate of average in presence of continuous change in numbers at nodes. Clearly, in presence of such dynamics the best one can hope for is a tradeoff between the accuracy of each node's estimate of the average at each time instant and the rate of dynamics. For our algorithm, we characterize this tradeoff and establish it to be near optimal. The dependence of the accuracy of the algorithm on the rate of dynamics as well as on the underlying graph structure is quantified. by Shreevatsa Rajagopalan. S.M.

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Li, Lan (Simone). "Disparity averaging mechanisms in stereopsis." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2007. http://wwwlib.umi.com/cr/syr/main.

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Afsari, Bijan. "Means and averaging on riemannian manifolds." College Park, Md. : University of Maryland, 2009. http://hdl.handle.net/1903/9978.

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Thesis (Ph.D.) -- University of Maryland, College Park, 2009. Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Pan, Fei. "Multifrequency Averaging in Power Electronic Systems." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/62.

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Power electronic systems have been widely used in the electrical power processing for applications with power levels ranging from less than one watt in battery-operated portable devices to more than megawatts in the converters, inverters and rectifiers of the utility power systems. These systems typically involve the passive elements such as inductors, capacitors, and resistors, the switching electronic components such as IGBTs, MOSFETS, and diodes, and other electronic circuits. Multifrequency averaging is one of the widely used modeling and simulation techniques today for the analysis and design of power electronic systems. This technique is capable of providing the average behavior as well as the ripple behavior of power electronic systems. This work begins with the extension of multifrequency averaging to represent uniformly sampled PWM converters. A new multifrequency averaging method of solving an observed issue with model stability is proposed and validated. Multifrequency averaging can also be applied to study the instability phenomenon in power electronic systems. In particular, a reduced-order multifrequency averaging method, along with a genetic algorithm based procedure, is proposed in this work to estimate the regions of attraction of power electronic converters. The performance of this method is shown by comparing the accuracy and efficiency with the existing methods. Finally, a new continuous-time multifrequency averaging method of representing discrete-time systems is proposed. The proposed method is applied to model digitally controlled PWM converters. Simulation and hardware results show that the proposed method is capable of predicting the average behavior as well as the ripple behavior of the closed-loop systems. Future research in the area of multifrequency averaging is proposed.

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Amini, Moghadam Shahram. "Model Uncertainty & Model Averaging Techniques." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28398.

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The primary aim of this research is to shed more light on the issue of model uncertainty in applied econometrics in general and cross-country growth as well as happiness and well-being regressions in particular. Model uncertainty consists of three main types: theory uncertainty, focusing on which principal determinants of economic growth or happiness should be included in a model; heterogeneity uncertainty, relating to whether or not the parameters that describe growth or happiness are identical across countries; and functional form uncertainty, relating to which growth and well-being regressors enter the model linearly and which ones enter nonlinearly. Model averaging methods including Bayesian model averaging and Frequentist model averaging are the main statistical tools that incorporate theory uncertainty into the estimation process. To address functional form uncertainty, a variety of techniques have been proposed in the literature. One suggestion, for example, involves adding regressors that are nonlinear functions of the initial set of theory-based regressors or adding regressors whose values are zero below some threshold and non-zero above that threshold. In recent years, however, there has been a rising interest in using nonparametric framework to address nonlinearities in growth and happiness regressions. The goal of this research is twofold. First, while Bayesian approaches are dominant methods used in economic empirics to average over the model space, I take a fresh look into Frequentist model averaging techniques and propose statistical routines that computationally ease the implementation of these methods. I provide empirical examples showing that Frequentist estimators can compete with their Bayesian peers. The second objective is to use recently-developed nonparametric techniques to overcome the issue of functional form uncertainty while analyzing the variance of distribution of per capita income. Nonparametric paradigm allows for addressing nonlinearities in growth and well-being regressions by relaxing both the functional form assumptions and traditional assumptions on the structure of error terms. Ph. D.

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Noble, Robert Bruce. "Multivariate Applications of Bayesian Model Averaging." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/30180.

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The standard methodology when building statistical models has been to use one of several algorithms to systematically search the model space for a good model. If the number of variables is small then all possible models or best subset procedures may be used, but for data sets with a large number of variables, a stepwise procedure is usually implemented. The stepwise procedure of model selection was designed for its computational efficiency and is not guaranteed to find the best model with respect to any optimality criteria. While the model selected may not be the best possible of those in the model space, commonly it is almost as good as the best model. Many times there will be several models that exist that may be competitors of the best model in terms of the selection criterion, but classical model building dictates that a single model be chosen to the exclusion of all others. An alternative to this is Bayesian model averaging (BMA), which uses the information from all models based on how well each is supported by the data. Using BMA allows a variance component due to the uncertainty of the model selection process to be estimated. The variance of any statistic of interest is conditional on the model selected so if there is model uncertainty then variance estimates should reflect this. BMA methodology can also be used for variable assessment since the probability that a given variable is active is readily obtained from the individual model posterior probabilities. The multivariate methods considered in this research are principal components analysis (PCA), canonical variate analysis (CVA), and canonical correlation analysis (CCA). Each method is viewed as a particular multivariate extension of univariate multiple regression. The marginal likelihood of a univariate multiple regression model has been approximated using the Bayes information criteria (BIC), hence the marginal likelihood for these multivariate extensions also makes use of this approximation. One of the main criticisms of multivariate techniques in general is that they are difficult to interpret. To aid interpretation, BMA methodology is used to assess the contribution of each variable to the methods investigated. A second issue that is addressed is displaying of results of an analysis graphically. The goal here is to effectively convey the germane elements of an analysis when BMA is used in order to obtain a clearer picture of what conclusions should be drawn. Finally, the model uncertainty variance component can be estimated using BMA. The variance due to model uncertainty is ignored when the standard model building tenets are used giving overly optimistic variance estimates. Even though the model attained via standard techniques may be adequate, in general, it would be difficult to argue that the chosen model is in fact the correct model. It seems more appropriate to incorporate the information from all plausible models that are well supported by the data to make decisions and to use variance estimates that account for the uncertainty in the model estimation as well as model selection. Ph. D.

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Książki na temat "Averaging"

Edleson, Michael E. Value Averaging. New York: John Wiley & Sons, Ltd., 2006.

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Fletcher, David. Model Averaging. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-58541-2.

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United States. Internal Revenue Service. Income averaging. 8th ed. [Washington, D.C.?]: Dept. of the Treasury, Internal Revenue Service, 1985.

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Lowry, Malcolm, ed. Averaging Insight. 2nd ed. Toronto: Letters, 2005.

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Kagan, R. L. Averaging of Meteorological Fields. Edited by Lev S. Gandin and Thomas M. Smith. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-015-8820-1.

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Hapaev, M. M. Averaging in Stability Theory. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2644-1.

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Kagan, Ruvim Leĭzerovich. Averaging of meteorological fields. Dordrecht: Kluwer Academic Publishers, 1997.

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S, Gandin L., and Smith Thomas M, eds. Averaging of meteorological fields. Dordrecht: Kluwer Academic, 1997.

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Woźniak, Czesław. Averaging techniques in thermomechanics of composite solids: Tolerance averaging versus homogenization. Częstochowa: Wydawn. politechniki Częstochowskiej, 2000.

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Whitaker, Stephen. The Method of Volume Averaging. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-017-3389-2.

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Części książek na temat "Averaging"

Andrianov, I., J. Awrejcewicz, and L. I. Manevitch. "Averaging." In Foundations of Engineering Mechanics, 241–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45246-1_8.

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Skorokhod, Anatoli V., Frank C. Hoppensteadt, and Habib Salehi. "Averaging." In Applied Mathematical Sciences, 88–113. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-22446-6_4.

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Chicone, Carmen. "Averaging." In Texts in Applied Mathematics, 591–622. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-51652-8_10.

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Jähne, Bernd. "Averaging." In Digital Image Processing, 283–314. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04781-1_11.

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Rand, Richard H., and Dieter Armbruster. "Averaging." In Perturbation Methods, Bifurcation Theory and Computer Algebra, 107–31. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4612-1060-3_5.

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Fletcher, David. "Why Model Averaging?" In Model Averaging, 1–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-58541-2_1.

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Fletcher, David. "Bayesian Model Averaging." In Model Averaging, 31–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-58541-2_2.

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Fletcher, David. "Frequentist Model Averaging." In Model Averaging, 57–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-58541-2_3.

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Fletcher, David. "Summary and Future Directions." In Model Averaging, 99–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-58541-2_4.

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Mahan, Gerald Dennis. "Markov Averaging." In Applied Mathematics, 177–94. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1315-5_7.

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Streszczenia konferencji na temat "Averaging"

Van Baren, John. "Random Averaging." In SAE 2015 Noise and Vibration Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2015. http://dx.doi.org/10.4271/2015-01-2213.

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Bodas, Shreeshankar, and Devavrat Shah. "Fast averaging." In 2011 IEEE International Symposium on Information Theory - ISIT. IEEE, 2011. http://dx.doi.org/10.1109/isit.2011.6033939.

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Barbosa, Samuel, Dan Cosley, Amit Sharma, and Roberto M. Cesar. "Averaging Gone Wrong." In WWW '16: 25th International World Wide Web Conference. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 2016. http://dx.doi.org/10.1145/2872427.2883083.

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Potey, M. A., and N. V. Upasani. "Generalized ring averaging." In ICWET '10: International Conference and Workshop on Emerging Trends in Technology. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/1741906.1741951.

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Dibaji, Seyed Mehran, Mostafa Safi, and Hideaki Ishii. "Resilient Distributed Averaging." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8814959.

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Su, Han-I., and Abbas El Gamal. "Distributed lossy averaging." In 2009 IEEE International Symposium on Information Theory - ISIT. IEEE, 2009. http://dx.doi.org/10.1109/isit.2009.5205877.

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Sun, Shaoyuan, and Haitao Zhao. "Kernel Averaging Filter." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.591.

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Chen, Yu, Ji Zhao, and Laurent Kneip. "Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01022.

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Tsai, Chun-Ming, and Zong-Mu Yeh. "Fast Spatial Averaging Filter." In 2012 International Symposium on Computer, Consumer and Control (IS3C). IEEE, 2012. http://dx.doi.org/10.1109/is3c.2012.47.

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King, P. R. "Effective Values in Averaging." In Mathematics in Oil Production. European Association of Geoscientists & Engineers, 1987. http://dx.doi.org/10.3997/2214-4609-pdb.235.006.

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Raporty organizacyjne na temat "Averaging"

Sepke, S. Inverse Bremsstrahlung Averaging. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1888624.

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Steinerberger, Stefan, and Aleh Tsyvinski. On Vickrey’s Income Averaging. Cambridge, MA: National Bureau of Economic Research, April 2020. http://dx.doi.org/10.3386/w27024.

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Soanes, Royce. Function Smoothing by Repeated Averaging. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada196575.

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Chen, Xiaohong, David Jacho-Chávez, and Oliver Linton. Averaging of moment condition estimators. Institute for Fiscal Studies, September 2012. http://dx.doi.org/10.1920/wp.cem.2012.2612.

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Banternghansa, Chanont, and Michael W. McCracken. Real-Time Forecast Averaging with ALFRED. Federal Reserve Bank of St. Louis, 2010. http://dx.doi.org/10.20955/wp.2010.033.

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McCracken, Michael W., and Todd E. Clark. Averaging Forecasts from VARs with Uncertain Instabilities. Federal Reserve Bank of St. Louis, 2008. http://dx.doi.org/10.20955/wp.2008.030.

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McCallen, R. C. Time averaging of instantaneous quantities in HYDRA. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/453332.

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Ernst, R. E., and G. W. Pearce. Averaging of Anisotropy of Magnetic Susceptibility Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128071.

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Yost, G. P. Averaging in the presence of sliding errors. Office of Scientific and Technical Information (OSTI), August 1991. http://dx.doi.org/10.2172/79719.

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Crockett, Carl E. An Introduction to the Method of Averaging. Fort Belvoir, VA: Defense Technical Information Center, November 1992. http://dx.doi.org/10.21236/ada258990.

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