Relevant bibliographies by topics / Dynamic model averaging

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dynamic model averaging'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Dynamic model averaging"

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Koop, Gary, and Dimitris Korobilis. "FORECASTING INFLATION USING DYNAMIC MODEL AVERAGING*." International Economic Review 53, no. 3 (2012): 867–86. http://dx.doi.org/10.1111/j.1468-2354.2012.00704.x.

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Onorante, Luca, and Adrian E. Raftery. "Dynamic model averaging in large model spaces using dynamic Occam׳s window." European Economic Review 81 (January 2016): 2–14. http://dx.doi.org/10.1016/j.euroecorev.2015.07.013.

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Mahmud, Md Rasel, Ahmed F. Abdou, and Hemanshu Pota. "Stability Analysis of Grid-Connected Photovoltaic Systems with Dynamic Phasor Model." Electronics 8, no. 7 (2019): 747. http://dx.doi.org/10.3390/electronics8070747.

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The typical layout of power systems is experiencing significant change, due to the high penetration of renewable energy sources (RESs). The ongoing evaluation of power systems is expecting more detailed and accurate mathematical modeling approaches for RESs which are dominated by power electronics. Although modeling techniques based on state–space averaging (SSA) have traditionally been used to mathematically represent the dynamics of power systems, the performance of such a model-based system degrades under high switching frequency. The multi-frequency averaging (MFA)-based higher-index dynamic phasor modeling tool is proposed in this paper, which is entirely new and can provide better estimations of dynamics. Dynamic stability analysis is presented in this paper for the MFA-based higher-index dynamical model of single-stage single-phase (SSSP) grid-connected photovoltaic (PV) systems under different switching frequencies.
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Koop, Gary, and Simon Potter. "Forecasting in dynamic factor models using Bayesian model averaging." Econometrics Journal 7, no. 2 (2004): 550–65. http://dx.doi.org/10.1111/j.1368-423x.2004.00143.x.

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McCormick, Tyler H., Adrian E. Raftery, David Madigan, and Randall S. Burd. "Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification." Biometrics 68, no. 1 (2011): 23–30. http://dx.doi.org/10.1111/j.1541-0420.2011.01645.x.

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Styrin, Konstantin. "Forecasting Inflation in Russia Using Dynamic Model Averaging." Russian Journal of Money and Finance 78, no. 1 (2019): 03–18. http://dx.doi.org/10.31477/rjmf.201901.03.

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Yang, Hongxia, Jonathan R. M. Hosking, and Yasuo Amemiya. "Dynamic Latent Class Model Averaging for Online Prediction." Journal of Forecasting 34, no. 1 (2014): 1–14. http://dx.doi.org/10.1002/for.2315.

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Aye, Goodness, Rangan Gupta, Shawkat Hammoudeh, and Won Joong Kim. "Forecasting the price of gold using dynamic model averaging." International Review of Financial Analysis 41 (October 2015): 257–66. http://dx.doi.org/10.1016/j.irfa.2015.03.010.

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Bork, Lasse, and Stig V. Møller. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection." International Journal of Forecasting 31, no. 1 (2015): 63–78. http://dx.doi.org/10.1016/j.ijforecast.2014.05.005.

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Chen, Yong, Huiling Yuan, Yize Yang, and Ruochen Sun. "Sub-daily soil moisture estimate using dynamic Bayesian model averaging." Journal of Hydrology 590 (November 2020): 125445. http://dx.doi.org/10.1016/j.jhydrol.2020.125445.

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Dissertations / Theses on the topic "Dynamic model averaging"

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Kashefi, Kaviani Ali. "Dynamic Modeling and Analysis of Single-Stage Boost Inverters under Normal and Abnormal Conditions." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/655.

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Inverters play key roles in connecting sustainable energy (SE) sources to the local loads and the ac grid. Although there has been a rapid expansion in the use of renewable sources in recent years, fundamental research, on the design of inverters that are specialized for use in these systems, is still needed. Recent advances in power electronics have led to proposing new topologies and switching patterns for single-stage power conversion, which are appropriate for SE sources and energy storage devices. The current source inverter (CSI) topology, along with a newly proposed switching pattern, is capable of converting the low dc voltage to the line ac in only one stage. Simple implementation and high reliability, together with the potential advantages of higher efficiency and lower cost, turns the so-called, single-stage boost inverter (SSBI), into a viable competitor to the existing SE-based power conversion technologies. The dynamic model is one of the most essential requirements for performance analysis and control design of any engineering system. Thus, in order to have satisfactory operation, it is necessary to derive a dynamic model for the SSBI system. However, because of the switching behavior and nonlinear elements involved, analysis of the SSBI is a complicated task. This research applies the state-space averaging technique to the SSBI to develop the state-space-averaged model of the SSBI under stand-alone and grid-connected modes of operation. Then, a small-signal model is derived by means of the perturbation and linearization method. An experimental hardware set-up, including a laboratory-scaled prototype SSBI, is built and the validity of the obtained models is verified through simulation and experiments. Finally, an eigenvalue sensitivity analysis is performed to investigate the stability and dynamic behavior of the SSBI system over a typical range of operation.
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Muraleedharan, Nair Jayakrishnan. "Signature Verification Model: A Long Term Memory Approach." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1427210243.

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Li, Guangjie. "Essays on economic and econometric applications of Bayesian estimation and model comparison." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/4792.

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This thesis consists of three chapters on economic and econometric applications of Bayesian parameter estimation and model comparison. The first two chapters study the incidental parameter problem mainly under a linear autoregressive (AR) panel data model with fixed effect. The first chapter investigates the problem from a model comparison perspective. The major finding in the first chapter is that consistency in parameter estimation and model selection are interrelated. The reparameterization of the fixed effect parameter proposed by Lancaster (2002) may not provide a valid solution to the incidental parameter problem if the wrong set of exogenous regressors are included. To estimate the model consistently and to measure its goodness of fit, the Bayes factor is found to be more preferable for model comparson than the Bayesian information criterion based on the biased maximum likelihood estimates. When the model uncertainty is substantial, Bayesian model averaging is recommended. The method is applied to study the relationship between financial development and economic growth. The second chapter proposes a correction function approach to solve the incidental parameter problem. It is discovered that the correction function exists for the linear AR panel model of order p when the model is stationary with strictly exogenous regressors. MCMC algorithms are developed for parameter estimation and to calculate the Bayes factor for model comparison. The last chapter studies how stock return's predictability and model uncertainty affect a rational buy-and-hold investor's decision to allocate her wealth for different lengths of investment horizons in the UK market. The FTSE All-Share Index is treated as the risky asset, and the UK Treasury bill as the riskless asset in forming the investor's portfolio. Bayesian methods are employed to identify the most powerful predictors by accounting for model uncertainty. It is found that though stock return predictability is weak, it can still affect the investor's optimal portfolio decisions over different investment horizons.
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Schaal, Peter. "Observer-based engine air charge characterisation : rapid, observer-assisted engine air charge characterisation using a dynamic dual-ramp testing method." Thesis, Loughborough University, 2018. https://dspace.lboro.ac.uk/2134/33247.

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Characterisation of modern complex powertrains is a time consuming and expensive process. Little effort has been made to improve the efficiency of testing methodologies used to obtain data for this purpose. Steady-state engine testing is still regarded as the golden standard, where approximately 90% of testing time is wasted waiting for the engine to stabilize. Rapid dynamic engine testing, as a replacement for the conventional steady-state method, has the potential to significantly reduce the time required for characterisation. However, even by using state of the art measurement equipment, dynamic engine testing introduces the problem that certain variables are not directly measurable due to the excitation of the system dynamics. Consequently, it is necessary to develop methods that allow the observation of not directly measurable quantities during transient engine testing. Engine testing for the characterisation of the engine air-path is specifically affected by this problem since the air mass flow entering the cylinder is not directly measurable by any sensor during transient operation. This dissertation presents a comprehensive methodology for engine air charge characterisation using dynamic test data. An observer is developed, which allows observation of the actual air mass flow into the engine during transient operation. The observer is integrated into a dual-ramp testing procedure, which allows the elimination of unaccounted dynamic effects by averaging over the resulting hysteresis. A simulation study on a 1-D gas dynamic engine model investigates the accuracy of the developed methodology. The simulation results show a trade-off between time saving and accuracy. Experimental test result confirm a time saving of 95% compared to conventional steady-state testing and at least 65% compared to quasi steady-state testing while maintaining the accuracy and repeatability of conventional steady-state testing.
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Beraldi, Fidel. "Atualização dinâmica de modelo de regressão logística binária para detecção de fraudes em transações eletrônicas com cartão de crédito." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-05022015-232801/.

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Com o avanço tecnológico e econômico, que facilitaram o processo de comunicação e aumento do poder de compra, transações com cartão de crédito tornaram-se o principal meio de pagamento no varejo nacional e internacional (Bolton e Hand , 2002). Neste aspecto, o aumento do número de transações com cartão de crédito é crucial para a geração de mais oportunidades para fraudadores produzirem novas formas de fraudes, o que resulta em grandes perdas para o sistema financeiro (Chan et al. , 1999). Os índices de fraudes têm mostrado que transações no comércio eletrônico (e-commerce) são mais arriscadas do que transações presencias em terminais, pois aquelas não fazem uso de processos seguros e eficientes de autenticação do portador do cartão, como utilização de senha eletrônica. Como os fraudadores se adaptam rapidamente às medidas de prevenção, os modelos estatísticos para detecção de fraudes precisam ser adaptáveis e flexíveis para evoluir ao longo do tempo de maneira dinâmica. Raftery et al. (2010) desenvolveram um método chamado Dynamic Model Averaging (DMA), ou Ponderação Dinâmica de Modelos, que implementa um processo de atualização contínuo ao longo do tempo. Nesta dissertação, desenvolvemos modelos DMA no espaço de transações eletrônicas oriundas do comércio eletrônico que incorporem as tendências e características de fraudes em cada período de análise. Também desenvolvemos modelos de regressão logística clássica com o objetivo de comparar as performances no processo de detecção de fraude. Os dados utilizados para tal são provenientes de uma empresa de meios de pagamentos eletrônico. O experimento desenvolvido mostra que os modelos DMA apresentaram resultados melhores que os modelos de regressão logística clássica quando analisamos a medida F e a área sob a curva ROC (AUC). A medida F para o modelo DMA ficou em 58% ao passo que o modelo de regressão logística clássica ficou em 29%. Já para a AUC, o modelo DMA alcançou 93% e o modelo de regressão logística clássica 84%. Considerando os resultados encontrados para os modelos DMA, podemos concluir que sua característica de atualização ao longo do tempo se mostra um grande diferencial em dados como os de fraude, que sofrem mudanças de comportamento a todo momento. Deste modo, sua aplicação se mostra adequada no processo de detecção de transações fraudulentas no ambiente de comércio eletrônico.<br>Regarding technological and economic development, which made communication process easier and increased purchasing power, credit card transactions have become the primary payment method in national and international retailers (Bolton e Hand , 2002). In this scenario, as the number of transactions by credit card grows, more opportunities are created for fraudsters to produce new ways of fraud, resulting in large losses for the financial system (Chan et al. , 1999). Fraud indexes have shown which e-commerce transactions are riskier than card present transactions, since those do not use secure and efficient processes to authenticate the cardholder, such as using personal identification number (PIN). Due to fraudsters adapt quickly to fraud prevention measures, statistical models for fraud detection need to be adaptable and flexible to change over time in a dynamic way. Raftery et al. (2010) developed a method called Dynamic Model Averaging (DMA), which implements a process of continuous updating over time. In this thesis, we develop DMA models within electronic transactions coming from ecommerce environment, which incorporate the trends and characteristics of fraud in each period of analysis. We have also developed classic logistic regression models in order to compare their performances in the fraud detection processes. The database used for the experiment was provided by a electronic payment service company. The experiment shows that DMA models present better results than classic logistic regression models in respect to the analysis of the area under the ROC curve (AUC) and F measure. The F measure for the DMA was 58% while the classic logistic regression model was 29%. For the AUC, the DMA model reached 93% and the classical model reached 84%. Considering the results for DMA models, we can conclude that its update over time characteristic makes a large difference when it comes to the analysis of fraud data, which undergo behavioral changes continuously. Thus, its application has proved to be appropriate for the detection process of fraudulent transactions in the e-commerce environment.
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Sen, Gokhan. "Voltage and Current Programmed Modes in Control of the Z-Source Converter." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1226508637.

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Tranchida, Julien. "Multiscale description of dynamical processes in magnetic media : from atomistic models to mesoscopic stochastic processes." Thesis, Tours, 2016. http://www.theses.fr/2016TOUR4027/document.

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Les propriétés magnétiques détaillées des solides peuvent être vu comme le résultat de l'interaction de plusieurs sous-systèmes: celui des spins effectifs, portant l'aimantation, celui des électrons et celui du réseau crystallin. Différents processus permettent à ces sous-systèmes d'échanger de l'énergie. Parmis ceux-ci, les phénomènes de relaxation jouent un rôle prépondérants. Cependant, la complexité de ces processus en rend leur modélisation ardue. Afin de prendre en compte ces interactions de façon abordable aux calculs, l'approche de Langevin est depuis longtemps appliquée à la dynamique d'aimantation, qui peut être vue comme la réponse collective des spins. Elle consiste à modéliser les interactions entre les trois sous-systèmes par des interactions effectives entre le sous-système d'intérêt, les spins, et un bain thermique, dont seulement la densité de probabilité constituerait une quantité pertinente. Après avoir présenté cette approche, nous verrons en quoi elle permet de bâtir une dynamique atomique de spin. Une fois son implémentation détaillée, cette méthodologie sera appliquée à un exemple tiré de la littérature et basé sur le superparamagnétisme de nanoaimants de fer<br>Detailed magnetic properties of solids can be regarded as the result of the interaction between three subsystems: the effective spins, that will be our focus in this thesis, the electrons and the crystalline lattice. These three subsystems exchange energy, in many ways, in particular, through relaxation processes. The nature of these processes remains extremely hard to understand, and even harder to simulate. A practical approach, for performing such simulations, involves adapting the description of random processes by Langevin to the collective dynamics of the spins, usually called the magnetization dynamics. It consists in describing the, complicated, interactions between the subsystems, by the effective interactions of the subsystem of interest, the spins, and a thermal bath, whose probability density is only of relevance. This approach allows us to interpret the results of atomistic spin dynamics simulations in appropriate macroscopic terms. After presenting the numerical implementation of this methodology, a typical study of a magnetic device based on superparamagnetic iron monolayers is presented, as an example. The results are compared to experimental data and allow us to validate the atomistic spin dynamics simulations
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Sousa, Diogo António Montez de. "Forecasting house prices using dynamic model averaging." Master's thesis, 2018. http://hdl.handle.net/10362/35668.

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This work project applies the Dynamic Model Averaging methodology to forecast quarterly house price growth in Portugal, Spain, Italy, Ireland, the Euro Area and the United States. This recent econometric technique uses the Kalman filter to recursively estimate dynamic models and ultimately produces a forecast by averaging these models using a prediction performance criterion. Results show the superior predictive ability of this methodology when compared to the usual autoregressive benchmarks. Furthermore, we make use of the model’s outputs to provide a comparative analysis of the six series, concluding that there is no single predictor transversally important for all series.
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Huang, Tzu-Yu, and 黃子祐. "Dynamic investment analysis of model averaging method in multi-asset under the value-at-risk constraint." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/02717662848592437045.

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碩士<br>淡江大學<br>管理科學研究所碩士班<br>98<br>The paper follows Pesaran, Schleicher and Zaffaroni(2008)&apos;&apos;s spirit to consider the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. We adopt model selection criteria of Kupiec’s(1995) LR value of likelihood ratio test or missing times of VaR backtesting other than the minimum AIC or SBC value. The model averaging idea and the VaR diagnostic tests are illustrated by an application to portfolios of daily reurns on two stock index and four future contracts. The empirical evidence supports the use of model averaging strategies dominate single models.
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Drachal, Krzysztof. "Novel Bayesian model combination schemes with model uncertainty: An application to prices of selected energy commodities." Doctoral thesis, 2020. https://depotuw.ceon.pl/handle/item/3635.

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The aim of this dissertation is to apply the selected Bayesian model combination schemes in case of forecasting the prices of the selected energy commodities. These methods have the following advantages. First, the estimation is done in a recursive way, i.e., the forecast in time t is formulated only on the basis of the data available during time t -1, which corresponds with the real-life situation, of, for example, an investor on the market. Secondly, the coefficients of a multilinear individual, i.e., being put into the model combination scheme, regression model, as well as, the weights ascribed to each of these individual models, are time-varying. These features allow to “grasp” the changing relationships between the explained variable and the explanatory variables, as well as, the time-varying usefulness in forecasting of various explanatory variables in different time periods. Finally, these methods allow to analyse regressions with „large p, small n”, i.e., they allow for the number of explanatory variables in a multilinear regression model to be relatively large comparing with the length of time-series representing these explanatory variables (the number of observations). In most of the cases the analysis was done over monthly data from the period between 1986 and 2016. The time period was chosen due to the data availability. On the other hand, the frequency of the data was a compromise between eliminating short-term speculative market fluctuations, necessity to obtain long enough time-series and ability to include both macroeconomic and financial market indices. The dissertation consists of four published scientific papers. In the first article Dynamic Model Averaging (DMA) and Dynamic Model Selection (DMS) were used to analyse crude oil prices. In the second article, the analysis of crude oil prices with DMA was extended, basing on the results obtained in the first paper. In the third article DMA, DMS and Median Probability Model (MED) were used to analyse crude oil, natural gas and coal prices. The results were compared with a non-Bayesian method of model averaging, based on the Akaike criterion. Moreover, the data about Internet queries from the Google search website were used as the additional one. These was done by a small modification in the considered model combination schemes framework. The fourth article is focused on the analysis of 69 various commodities. It was concluded that in most of the cases the Bayesian model combination schemes allow to obtain statistically significantly more accurate forecasts (the Diebold-Mariano test) than, for example, the naive method or ARIMA models. This last article concludes also that solely time-varying coefficients in a multilinear regression model does not lead to such an improvement in forecast accuracy as combining a large number of individual models. In all articles the spot prices were analysed.<br>Celem niniejszej rozprawy jest zastosowanie wybranych Bayesowskich metod kombinowania prognoz do prognozowania cen wybranych surowców energetycznych. Metody te posiadają następujące zalety. Po pierwsze estymacja następuje w sposób rekurencyjny, tzn. w chwili t prognoza formułowana jest jedynie w oparciu o dane dostępne w okresie t -1, co oddaje rzeczywistą sytuację, na przykład inwestora na rynku. Po drugie, zarówno współczynniki regresji wielorakiej w pojedynczych, poddawanych kombinowaniu, modelach regresji wielorakiej, jak i przypisywane im wagi są zmienne w czasie. Pozwala to „wychwycić” zmieniające się na rynku relacje między zmienną objaśnianą a zmiennymi objaśniającymi, jak również samą zmieniającą się czasie przydatność do prognozowania różnych zmiennych objaśniających w różnych okresach czasu. Wreszcie, metody te pozwalają na analizę regresji z tzw. problemem „duże p, małe n”, tzn. dopuszczają sytuację, gdy liczba zmiennych objaśniających w modelu regresji wielorakiej jest relatywnie duża w porównaniu z długością szeregów czasowych reprezentujących te zmienne (liczbą obserwacji). W większości przypadków analizie poddano dane o częstotliwości miesięcznej pomiędzy 1986 a 2016 rokiem. Zakres czasowy badania podyktowany był dostępnością danych. Natomiast częstotliwość była kompromisem między wyeliminowaniem krótkotrwałych spekulacyjnych wahań rynkowych, koniecznością uzyskania odpowiednio długich szeregów czasowych oraz możliwością uwzględnienia zarówno indeksów makroekonomicznych jak i tych z rynków finansowych. Rozprawa składa się z czterech opublikowanych artykułów naukowych. W pierwszym artykule tzw. Dynamic Model Averaging (DMA) i Dynamic Model Selection (DMS) zostały zastosowane do analizy cen ropy naftowej. W drugim artykule pogłębiono analizę cen ropy naftowej przy pomocy metody DMA, w oparciu o uzyskane w pierwszym artykule wyniki. W artykule trzecim zastosowano DMA, DMS oraz tzw. Median Probability Model (MED) do analizy cen ropy naftowej, gazu ziemnego i węgla kamiennego. Wyniki porównano z nie-Bayesowską metodą uśredniania opartą o kryterium Akaike’a. Jak również wykorzystano, jako pomocnicze, dane dotyczące zapytań z wyszukiwarki Google, poprzez nieznaczną modyfikację wybranych metod kombinowania prognoz. W artykule czwartym analizie poddano 69 różnych surowców i skonkludowano, że w większości przypadków Bayesowskie metody kombinowania prognoz pozwalają osiągnąć statystycznie istotnie bardziej dokładne prognozy (test Diebolda-Mariano) aniżeli, na przykład, tzw. metoda naiwna, czy modele ARIMA. Z ostatniego artykułu wynika także, że samo dopuszczenie zmiennych w czasie współczynników w regresji wielorakiej nie prowadzi do takiej poprawy otrzymanych prognoz, jak kombinowanie dużej liczby pojedynczych modeli. We wszystkich pracach analizowane były tzw. ceny spot.
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Books on the topic "Dynamic model averaging"

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Rigby, David L. Prediction of heat and mass transfer in a rotating ribbed coolant passage with a 180 degree turn. National Aeronautics and Space Administration, Lewis Research Center, 1999.

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Zeitlin, Vladimir. Geophysical Fluid Dynamics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198804338.001.0001.

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The book explains the key notions and fundamental processes in the dynamics of the fluid envelopes of the Earth (transposable to other planets), and methods of their analysis, from the unifying viewpoint of rotating shallow-water model (RSW). The model, in its one- or two-layer versions, plays a distinguished role in geophysical fluid dynamics, having been used for around a century for conceptual understanding of various phenomena, for elaboration of approaches and methods, to be applied later in more complete models, for development and testing of numerical codes and schemes of data assimilations, and many other purposes. Principles of modelling of large-scale atmospheric and oceanic flows, and corresponding approximations, are explained and it is shown how single- and multi-layer versions of RSW arise from the primitive equations by vertical averaging, and how further time-averaging produces celebrated quasi-geostrophic reductions of the model. Key concepts of geophysical fluid dynamics are exposed and interpreted in RSW terms, and fundamentals of vortex and wave dynamics are explained in Part 1 of the book, which is supplied with exercises and can be used as a textbook. Solutions of the problems are available at Editorial Office by request. In-depth treatment of dynamical processes, with special accent on the primordial process of geostrophic adjustment, on instabilities in geophysical flows, vortex and wave turbulence and on nonlinear wave interactions follows in Part 2. Recently arisen new approaches in, and applications of RSW, including moist-convective processes constitute Part 3.
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Zeitlin, Vladimir. Rotating Shallow-Water model with Horizontal Density and/or Temperature Gradients. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198804338.003.0014.

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The derivation of rotating shallow-water equations by vertical averaging and columnar motion hypothesis is repeated without supposing horizontal homogeneity of density/potential temperature. The so-called thermal rotating shallow-water model arises as the result. The model turns to be equivalent to gas dynamics with a specific equation of state. It is shown that it possesses Hamiltonian structure and can be derived from a variational principle. Its solution at low Rossby numbers should obey the thermo-geostrophic equilibrium, replacing the standard geostrophic equilibrium. The wave spectrum of the model is analysed, and the appearance of a whole new class of vortex instabilities of convective type, resembling asymmetric centrifugal instability and leading to a strong mixing at nonlinear stage, is demonstrated.
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Asymptotic Methods in Resonance Analytical Dynamics: Stability and Control: Theory, Methods and Applications; Volume 21. CRC, 2004.

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-H, Shih T., and United States. National Aeronautics and Space Administration., eds. An NPARC turbulence module with wall functions: Under cooperative agreement NCC3-370. National Aeronautics and Space Administration, 1997.

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L, Rigby D., and NASA Glenn Research Center, eds. A numerical analysis of heat transfer and effectiveness on film cooled turbine blade tip models. National Aeronautics and Space Administration, Glenn Research Center, 1999.

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United States. National Aeronautics and Space Administration., ed. An experimental and numerical investigation of turbulent vortex breakdown and aircraft wakes: Final report, contract NAG 1-1775. National Aeronautics and Space Administration, 1996.

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B, Lakshminarayana, and NASA Glenn Research Center, eds. Numerical simulation of complex turbomachinery flows. National Aeronautics and Space Administration, Glenn Research Center, 1999.

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Numerical simulation of complex turbomachinery flows. National Aeronautics and Space Administration, Glenn Research Center, 1999.

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V, Wilson Robert, and Langley Research Center, eds. Streamwise vorticity generation in laminar and turbulent jets. National Aeronautics and Space Administration, Langley Research Center, 1999.

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Book chapters on the topic "Dynamic model averaging"

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Reichl, Jan, and Kamil Dedecius. "Likelihood Tempering in Dynamic Model Averaging." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54084-9_7.

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Kamp, Michael, Linara Adilova, Joachim Sicking, et al. "Efficient Decentralized Deep Learning by Dynamic Model Averaging." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10925-7_24.

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Kliber, Paweł. "Determinants of the Spread Between POLONIA Rate and the Reference Rate: Dynamic Model Averaging Approach." In Contemporary Trends and Challenges in Finance. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54885-2_3.

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Sundaram, Tessa A., Brian B. Avants, and James C. Gee. "A Dynamic Model of Average Lung Deformation Using Capacity-Based Reparameterization and Shape Averaging of Lung MR Images." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30136-3_121.

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Kislovski, André S., Richard Redl, and Nathan O. Sokal. "State-Variables-Averaging Method." In Dynamic Analysis of Switching-Mode DC/DC Converters. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-7849-5_4.

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Rödenbeck, Christian, Christian Beck, and Holger Kantz. "Dynamical systems with time scale separation: averaging, stochastic modelling, and central limit theorems." In Stochastic Climate Models. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8287-3_8.

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Meneveau, Charles, and Thomas Lund. "Lagrangian averaging for dynamic Eddy-viscosity subgrid models for filtered Navier-Stokes equation." In CRM Proceedings and Lecture Notes. American Mathematical Society, 1999. http://dx.doi.org/10.1090/crmp/020/06.

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Chowdhury, Mohammad Ashraful Ferdous. "The Nexus Between Institutional Quality and Foreign Direct Investments (FDI) in South Asia." In Outward Foreign Direct Investment (FDI) in Emerging Market Economies. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2345-1.ch015.

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South Asia is one of the world's fastest growing regions, averaging 6.7 percent annual increases in real GDP over the past decade. However, South Asia's FDI inflows as a share of GDP are the lowest of all developing regions, averaging less than 2 percent in 2000-11(World Bank, 2013). Institutional quality is one of the factors that determine the volume of FDI inflow in any country. This study covers the data of three sampled countries of South Asia provided by the World Bank for the period 2003-2014. By using both static and dynamic models, this study reveals that regulatory quality and the political stability have significantly positive impact on the FDI inflow into each of the three countries. For Robustness, this study also employs dynamic heterogeneous panel approaches like Pool Mean Group (PMG) and found that institutional quality factors are significantly relevant to the FDI. As a policy implication, the regression results indicate that during the process of reform, the relation between FDI and institutional quality warrants a certain amount of attention.
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Chowdhury, Mohammad Ashraful Ferdous. "The Nexus Between Institutional Quality and Foreign Direct Investments (FDI) in South Asia." In Foreign Direct Investments. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2448-0.ch029.

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South Asia is one of the world's fastest growing regions, averaging 6.7 percent annual increases in real GDP over the past decade. However, South Asia's FDI inflows as a share of GDP are the lowest of all developing regions, averaging less than 2 percent in 2000-11(World Bank, 2013). Institutional quality is one of the factors that determine the volume of FDI inflow in any country. This study covers the data of three sampled countries of South Asia provided by the World Bank for the period 2003-2014. By using both static and dynamic models, this study reveals that regulatory quality and the political stability have significantly positive impact on the FDI inflow into each of the three countries. For Robustness, this study also employs dynamic heterogeneous panel approaches like Pool Mean Group (PMG) and found that institutional quality factors are significantly relevant to the FDI. As a policy implication, the regression results indicate that during the process of reform, the relation between FDI and institutional quality warrants a certain amount of attention.
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Lakatos, Béla G. "Multilevel Dynamical Models for Polydisperse Systems: a Volume Averaging Approach." In European Symposium on Computer Aided Process Engineering-12, 35th European Symposium of the Working Party on Computer Aided Process Engineering. Elsevier, 2002. http://dx.doi.org/10.1016/s1570-7946(02)80168-7.

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Conference papers on the topic "Dynamic model averaging"

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Taveeapiradeecharoen, Paponpat, and Nattapol Aunsri. "Dynamic model averaging for daily forex prediction: A comparative study." In 2018 International Conference on Digital Arts, Media and Technology (ICDAMT). IEEE, 2018. http://dx.doi.org/10.1109/icdamt.2018.8376549.

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Zheng, Xianghan, and Hao Tian. "House Price Forecast Based on Dynamic Model Averaging Model Combined With Web Search Index." In 2018 International Conference on Cloud Computing, Big Data and Blockchain (ICCBB). IEEE, 2018. http://dx.doi.org/10.1109/iccbb.2018.8756418.

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Xiong, Tao. "Forecasting soybean futures price using dynamic model averaging and particle swarm optimization." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3208761.

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Mazher, A. K., and Changki Mo. "Dynamic Modeling of Turbulence." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-62330.

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This paper presents a new systematic and generalized approach to model turbulence dynamically. The suggested approach is based on the variational technique to solve a system of equations where the number of unknowns is larger than the number of equations. Turbulence closure problem results when averaging the Navier-Stokes (N-S) equations. Averaging transforms the N-S equations from a determinate set of equations describing turbulent flow field to an indeterminate set of equations that need additional information. Unknown terms, Reynolds stresses, appear as a results of averaging; and the solution of the averaged N-S equations depends on the proper selection of Reynolds stresses. In the dynamic modeling formulation of turbulence, the Reynolds stresses are selected to produce a best solution of the averaged N-S equations. The Reynolds stresses are computed via optimizing a performance index ‘I’. In the optimization process the averaged N-S equations are considered as constraints. The performance index ‘I’ is defined as a measure of the quality of solution. Averaging can be considered as a process by which we lose some information about the flow field. The lost information appears partially in the unknown terms “Reynolds stresses”. Hence, the performance index should include some measure of information losses which occur as the result of averaging. Classical approach does not rely on the N-S equations, itself as a complete description of turbulence, to derive a suitable turbulence models. The new concept will use the N-S equations, combined with the physics of turbulence, for an optimal selection of turbulence model through ‘I’. In this approach the model is not specified in advance, but it will be developed dynamically with the solution.
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Sun, L., N. Tai, and Y. Pang. "State Averaging Model based on Dynamic Processes Analysis of Marine Pulsed Load System." In The 10th Renewable Power Generation Conference (RPG 2021). Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/icp.2021.2384.

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Cheung, Sai Hung, and James L. Beck. "Algorithms for Bayesian Model Class Selection of Higher-Dimensional Dynamic Systems." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35858.

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In recent years, Bayesian model updating techniques based on measured data have been applied in structural health monitoring. Often we are faced with the problem of how to select the ‘best’ model from a set of competing candidate model classes for the system based on data. To tackle this problem, Bayesian model class selection is used, which provides a rigorous Bayesian updating procedure to give the probability of different candidate classes for a system, based on the data from the system. There may be cases where more than one model class has significant probability and each of these will give different predictions. Bayesian model class averaging provides a coherent mechanism to incorporate all the considered model classes in the probabilistic predictions for the system. However, both Bayesian model class selection and Bayesian model class averaging require the calculation of the evidence of the model class which requires the nontrivial computation of a multi-dimensional integral. In this paper, several methods for solving this computationally challenging problem of model class selection are presented, proposed and compared. The efficiency of the proposed methods is illustrated by an example involving a structural dynamic system.
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Sihun Yang, Kenta Goto, Yasutaka Imamura, and Masahito Shoyama. "Dynamic characteristics model of bi-directional DC-DC converter using state-space averaging method." In INTELEC 2012 - 2012 IEEE International Telecommunications Energy Conference. IEEE, 2012. http://dx.doi.org/10.1109/intlec.2012.6374537.

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Li, Gong, Jing Shi, and Junyi Zhou. "Short Term Wind Speed Forecasting Based on Bayesian Model Averaging Method." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-13055.

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Wind energy has been the world’s fastest growing source of clean and renewable energy in the past decade. One of the fundamental difficulties faced by power system operators, however, is the unpredictability and variability of wind power generation, which is closely connected with the continuous fluctuations of the wind resource. Good short-term wind speed forecasting methods and techniques are urgently needed since it is important for wind energy conversion systems in terms of the relevant issues associated with the dynamic control of the wind turbine and the integration of wind energy into the power system. This paper proposes the application of Bayesian Model Averaging (BMA) method in combining the one-hour-ahead short-term wind speed forecasts from different statistical models. Based on the hourly wind speed observations from one representative site within North Dakota, four statistical models are built and the corresponding forecast time series are obtained. These data are then analyzed by using BMA method. The goodness-of-fit test results show that the BMA method is superior to its component models by providing a more reliable and accurate description of the total predictive uncertainty than the original elements, leading to a sharper probability density function for the probabilistic wind speed predictions.
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Bou-Zeid, Elie, Charles Meneveau, and Marc B. Parlange. "Applications of the Lagrangian Dynamic Model in LES of Turbulent Flow Over Surfaces With Heterogeneous Roughness Distributions." In ASME 2004 Heat Transfer/Fluids Engineering Summer Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ht-fed2004-56127.

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We study turbulent flow over surfaces with varying roughness scales, using large eddy simulation (LES). The goal is to use LES results to formulate effective boundary conditions in terms of effective roughness height and blending height, to be used for RANS. The LES are implemented with the dynamic Smagorinsky model based on the Germano identity. However, as is well-known, when this identity is applied locally, it yields a coefficient with unphysically strong fluctuations and averaging is needed for better realism and numerical stability. The traditional approach consists of averaging over homogeneous directions, for example horizontal planes in channel flow. This requirement for homogeneous directions in the flow field and the concomitant inability to handle complex geometries renders the use of this model questionable in studying the effect of surface heterogeneity. Instead, a new version of the Lagrangian dynamic subgrid-scale (SGS) model [1] is implemented. A systematic set of simulations of flow over patches of differing roughness is performed, covering a wide range of patch length scales and surface roughness values. The simulated mean velocity profiles are analyzed to identify the height of the blending layer and used to measure the effective roughness length. Extending ideas introduced by Miyake [2] and Claussen [3], we have proposed a simple expression for effective surface roughness and blending height knowing local surface patch roughness values and their lengths [4]. Results of the model agreed well with the LES results when the heterogeneous surface consisted of patches of equal sizes. The model is tested here for surfaces with patches of different sizes.
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Broderick, John A., Dawn M. Tilbury, and Ella M. Atkins. "Modeling and Scheduling of Multiple Power Sources for a Ground Robot." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6111.

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Energy storage is a major limiting factor for small unmanned ground vehicle endurance. This paper presents a hybrid model of a robot power system and a method to optimize power production and limit power loss for extended UGV operation. The optimization is based on a hybrid automaton model of the power system and produces the optimal controls for the different power components. An abstraction of power use and averaging of dynamics within a state can model the system with sufficient accuracy for power system optimization. Simulation studies of a Packbot equipped with a fuel cell and a battery are presented. The optimized power system is shown to require less energy over the mission compared to a baseline controller.
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Reports on the topic "Dynamic model averaging"

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Raftery, Adrian E., Miroslav Karny, Josef Andrysek, and Pavel Ettler. Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada478617.

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