Relevant bibliographies by topics / Dynamic regression models

Contents

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

Academic literature on the topic 'Dynamic regression models'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Dynamic regression models"

1

Harvey, Andrew, and Andrew Scott. "Seasonality in Dynamic Regression Models." Economic Journal 104, no. 427 (1994): 1324. http://dx.doi.org/10.2307/2235451.

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Shumway, Robert H., and Alan Pankratz. "Forecasting With Dynamic Regression Models." Journal of the American Statistical Association 88, no. 422 (1993): 705. http://dx.doi.org/10.2307/2290369.

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Wincek, Michael A. "Forecasting With Dynamic Regression Models." Technometrics 35, no. 1 (1993): 87–88. http://dx.doi.org/10.1080/00401706.1993.10484999.

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Kennedy, Peter. "Forecasting with dynamic regression models." International Journal of Forecasting 8, no. 4 (1992): 647–48. http://dx.doi.org/10.1016/0169-2070(92)90081-j.

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Stanciu, Ioana. "Regression Models that Describe Sunflower Oil Dynamic Viscosity of Temperature Absolute." International Journal of Scientific Research 3, no. 4 (2012): 53–54. http://dx.doi.org/10.15373/22778179/apr2014/21.

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Peña, Daniel, and Daniel Pena. "Measuring Influence in Dynamic Regression Models." Technometrics 33, no. 1 (1991): 93. http://dx.doi.org/10.2307/1269010.

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Pefia, Daniel. "Measuring Influence in Dynamic Regression Models." Technometrics 33, no. 1 (1991): 93–101. http://dx.doi.org/10.1080/00401706.1991.10484772.

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Sinha, Debajyoti. "Dynamic Regression Models for Survival Data." Journal of the American Statistical Association 102, no. 480 (2007): 1474. http://dx.doi.org/10.1198/jasa.2007.s230.

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Pollock, D. S. G., and Evangelia Pitta. "The misspecification of dynamic regression models." Journal of Statistical Planning and Inference 49, no. 2 (1996): 223–39. http://dx.doi.org/10.1016/0378-3758(94)00038-7.

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Popkov, Yuri S., Alexey Yu Popkov, Yuri A. Dubnov, and Dimitri Solomatine. "Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models." Mathematics 8, no. 7 (2020): 1119. http://dx.doi.org/10.3390/math8071119.

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We propose a new forecasting procedure that includes randomized hierarchical dynamic regression models with random parameters, measurement noises and random input. We developed the technology of entropy-randomized machine learning, which includes the estimation of characteristics of a dynamic regression model and its testing by generating ensembles of predicted trajectories through the sampling of the entropy-optimal probability density functions of the model parameters and measurement noises. The density functions are determined at the learning stage by solving the constrained maximization problem of an information entropy functional subject to the empirical balances with real data. The proposed procedure is applied to the randomized forecasting of the daily electrical load in a regional power system. We construct a two-layer dynamic model of the daily electrical load. One of the layers describes the dependence of electrical load on ambient temperature while the other simulates the stochastic quasi-fluctuating temperature dynamics.
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Dissertations / Theses on the topic "Dynamic regression models"

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Song, Song. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16341.

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In vielen Anwendungen ist es notwendig, die stochastische Schwankungen der maximalen Abweichungen der nichtparametrischen Schätzer von Quantil zu wissen, zB um die verschiedene parametrische Modelle zu überprüfen. Einheitliche Konfidenzbänder sind daher für nichtparametrische Quantil Schätzungen der Regressionsfunktionen gebaut. Die erste Methode basiert auf der starken Approximation der empirischen Verfahren und Extremwert-Theorie. Die starke gleichmäßige Konsistenz liegt auch unter allgemeinen Bedingungen etabliert. Die zweite Methode beruht auf der Bootstrap Resampling-Verfahren. Es ist bewiesen, dass die Bootstrap-Approximation eine wesentliche Verbesserung ergibt. Der Fall von mehrdimensionalen und diskrete Regressorvariablen wird mit Hilfe einer partiellen linearen Modell behandelt. Das Verfahren wird mithilfe der Arbeitsmarktanalysebeispiel erklärt. Hoch-dimensionale Zeitreihen, die nichtstationäre und eventuell periodische Verhalten zeigen, sind häufig in vielen Bereichen der Wissenschaft, zB Makroökonomie, Meteorologie, Medizin und Financial Engineering, getroffen. Der typische Modelierungsansatz ist die Modellierung von hochdimensionalen Zeitreihen in Zeit Ausbreitung der niedrig dimensionalen Zeitreihen und hoch-dimensionale zeitinvarianten Funktionen über dynamische Faktorenanalyse zu teilen. Wir schlagen ein zweistufiges Schätzverfahren. Im ersten Schritt entfernen wir den Langzeittrend der Zeitreihen durch Einbeziehung Zeitbasis von der Gruppe Lasso-Technik und wählen den Raumbasis mithilfe der funktionalen Hauptkomponentenanalyse aus. Wir zeigen die Eigenschaften dieser Schätzer unter den abhängigen Szenario. Im zweiten Schritt erhalten wir den trendbereinigten niedrig-dimensionalen stochastischen Prozess (stationär).<br>In many applications it is necessary to know the stochastic fluctuation of the maximal deviations of the nonparametric quantile estimates, e.g. for various parametric models check. Uniform confidence bands are therefore constructed for nonparametric quantile estimates of regression functions. The first method is based on the strong approximations of the empirical process and extreme value theory. The strong uniform consistency rate is also established under general conditions. The second method is based on the bootstrap resampling method. It is proved that the bootstrap approximation provides a substantial improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. A labor market analysis is provided to illustrate the method. High dimensional time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science, e.g. macroeconomics, meteorology, medicine and financial engineering. One of the common approach is to separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via dynamic factor analysis. We propose a two-step estimation procedure. At the first step, we detrend the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under the dependent scenario. At the second step, we obtain the detrended low dimensional stochastic process (stationary).
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Rodrigues, Antonio Jose Lopes. "Dynamic regression and supervised learning methods in time series modelling and forecasting." Thesis, Lancaster University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364365.

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Borak, Szymon. "Dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2008. http://dx.doi.org/10.18452/15802.

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Hochdimensionale Regressionsprobleme, die sich dynamisch entwickeln, sind in zahlreichen Bereichen der Wissenschaft anzutreffen. Die Dynamik eines solchen komplexen Systems wird typischerweise mittels der Zeitreiheneigenschaften einer geringen Anzahl von Faktoren analysiert. Diese Faktoren wiederum sind mit zeitinvarianten Funktionen von explikativen Variablen bewichtet. Diese Doktorarbeit beschäftigt sich mit einem dynamischen semiparametrischen Faktormodell, dass nichtparametrische Bewichtungsfunktionen benutzt. Zu Beginn sollen kurz die wichtigsten statistischen Methoden diskutiert werden um dann auf die Eigenschaften des verwendeten Modells einzugehen. Im Anschluss folgt die Diskussion einiger Anwendungen des Modellrahmens auf verschiedene Datensätze. Besondere Aufmerksamkeit wird auf die Dynamik der so genannten Implizierten Volatilität und das daraus resultierende Faktor-Hedging von Barrier Optionen gerichtet.<br>High-dimensional regression problems which reveal dynamic behavior occur frequently in many different fields of science. The dynamics of the whole complex system is typically analyzed by time propagation of few number of factors, which are loaded with time invariant functions of exploratory variables. In this thesis we consider dynamic semiparametric factor model, which assumes nonparametric loading functions. We start with a short discussion of related statistical techniques and present the properties of the model. Additionally real data applications are discussed with particular focus on implied volatility dynamics and resulting factor hedging of barrier options.
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Paczkowski, Remi. "Monte Carlo Examination of Static and Dynamic Student t Regression Models." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/38691.

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This dissertation examines a number of issues related to Static and Dynamic Student t Regression Models. The Static Student t Regression Model is derived and transformed to an operational form. The operational form is then examined in a series of Monte Carlo experiments. The model is judged based on its usefulness for estimation and testing and its ability to model the heteroskedastic conditional variance. It is also compared with the traditional Normal Linear Regression Model. Subsequently the analysis is broadened to a dynamic setup. The Student t Autoregressive Model is derived and a number of its operational forms are considered. Three forms are selected for a detailed examination in a series of Monte Carlo experiments. The models’ usefulness for estimation and testing is evaluated, as well as their ability to model the conditional variance. The models are also compared with the traditional Dynamic Linear Regression Model.<br>Ph. D.
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Zimmer, Zachary. "Predicting NFL Games Using a Seasonal Dynamic Logistic Regression Model." VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd_retro/97.

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The article offers a dynamic approach for predicting the outcomes of NFL games using the NFL games from 2002-2005. A logistic regression model is used to predict the probability that one team defeats another. The parameters of this model are the strengths of the teams and a home field advantage factor. Since it assumed that a team's strength is time dependent, the strength parameters were assigned a seasonal time series process. The best model was selected using all the data from 2002 through the first seven weeks of 2005. The last weeks of 2005 were used for prediction estimates.
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Rivers, Derick Lorenzo. "Dynamic Bayesian Approaches to the Statistical Calibration Problem." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3599.

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The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used to monitor the evolution of the measures, thus introducing a dynamic approach to statistical calibration. The research presented employs the use of Bayesian methodology to perform statistical calibration. The DLM framework is used to capture the time-varying parameters that may be changing or drifting over time. Dynamic based approaches to the linear, nonlinear, and multivariate calibration problem are presented in this dissertation. Simulation studies are conducted where the dynamic models are compared to some well known "static'" calibration approaches in the literature from both the frequentist and Bayesian perspectives. Applications to microwave radiometry are given.
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Pemmanaboina, Rajashekar. "Assessing Crash Occurrence On Urban Freeways Using Static And Dynamic Factors By Applying A System Of Interrelated Equations." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2617.

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Traffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models for different categories of crashes divided based on type of crash, or condition in which they occur, 3) safety models to determine the probability of crash occurrence, including a rainfall index that has been estimated using a logistic regression model. The study corridor is a 36.25 mile stretch of Interstate 4 in Central Florida. For the first two sections, crash cases from 1999 through 2002 were considered. Conventionally most of the crash frequency analysis model all crashes, instead of dividing them based on type of crash, peaking conditions, availability of light, severity, or pavement condition, etc. Also researchers traditionally used AADT to represent traffic volumes in their models. These two cases are examples of macroscopic crash frequency modeling. To investigate the microscopic models, and to identify the significant factors related to crash occurrence, a preliminary study (first analysis) explored the use of microscopic traffic volumes related to crash occurrence by comparing AADT/VMT with five to twenty minute volumes immediately preceding the crash. It was found that the volumes just before the time of crash occurrence proved to be a better predictor of crash frequency than AADT. The results also showed that road curvature, median type, number of lanes, pavement surface type and presence of on/off-ramps are among the significant factors that contribute to crash occurrence. In the second analysis various possible crash categories were prepared to exactly identify the factors related to them, using various roadway, geometric, and microscopic traffic variables. Five different categories are prepared based on a common platform, e.g. type of crash. They are: 1) Multiple and Single vehicle crashes, 2) Peak and Off-peak crashes, 3) Dry and Wet pavement crashes, 4) Daytime and Dark hour crashes, and 5) Property Damage Only (PDO) and Injury crashes. Each of the above mentioned models in each category are estimated separately. To account for the correlation between the disturbance terms arising from omitted variables between any two models in a category, seemingly unrelated negative binomial (SUNB) regression was used, and then the models in each category were estimated simultaneously. SUNB estimation proved to be advantageous for two categories: Category 1, and Category 4. Road curvature and presence of On-ramps/Off-ramps were found to be the important factors, which can be related to every crash category. AADT was also found to be significant in all the models except for the single vehicle crash model. Median type and pavement surface type were among the other important factors causing crashes. It can be stated that the group of factors found in the model considering all crashes is a superset of the factors that were found in individual crash categories. The third analysis dealt with the development of a logistic regression model to obtain the weather condition at a given time and location on I-4 in Central Florida so that this information can be used in traffic safety analyses, because of the lack of weather monitoring stations in the study area. To prove the worthiness of the weather information obtained form the analysis, the same weather information was used in a safety model developed by Abdel-Aty et al., 2004. It was also proved that the inclusion of weather information actually improved the safety model with better prediction accuracy.<br>M.S.C.E.<br>Department of Civil and Environmental Engineering<br>Engineering and Computer Science<br>Civil Engineering
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Song, Song [Verfasser], Wolfgang [Akademischer Betreuer] Härdle, and Ya'acov [Akademischer Betreuer] Ritov. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models / Song Song. Gutachter: Wolfgang Karl Härdle ; Ya’acov Ritov." Berlin : Humboldt Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://d-nb.info/1015129803/34.

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Picco, Robert C. "A comparative study of flow forecasting in the Humber River Basin using a deterministic hydrologic model and a dynamic regression statistical model." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ34219.pdf.

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Fox, David. "Dynamic demand modelling and pricing decision support systems for petroleum." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/dynamic-demand-modelling-and-pricing-decision-support-systems-for-petroleum(2ce6efed-a7eb-4d10-b325-4d4590ba57ad).html.

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Pricing decision support systems have been developed in order to help retail companies optimise the prices they set when selling their goods and services. This research aims to enhance the essential forecasting and optimisation techniques that underlie these systems. This is first done by applying the method of Dynamic Linear Models in order to provide sales forecasts of a higher accuracy compared with current methods. Secondly, the method of Support Vector Regression is used to forecast future competitor prices. This new technique aims to produce forecasts of greater accuracy compared with the assumption currentlyused in pricing decision support systems that each competitor's price will simply remain unchanged. Thirdly, when competitor prices aren't forecasted, a new pricing optimisation technique is presented which provides the highest guaranteed profit. Existing pricing decision support systems optimise price assuming that competitor prices will remain unchanged but this optimisation can't be trusted since competitor prices are never actually forecasted. Finally, when competitor prices are forecasted, an exhaustive search of a game-tree is presented as a new way to optimise a retailer's price. This optimisation incorporates future competitor price moves, something which is vital when analysing the success of a pricing strategy but is absent from current pricing decision support systems. Each approach is applied to the forecasting and optimisation of daily retail vehicle fuel pricing using real commercial data, showing the improved results in each case.
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Books on the topic "Dynamic regression models"

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Harvey, A. C. Seasonality in dynamic regression models. Suntory-Toyota International Centre for Economics and Related Disciplines, London School of Economics, 1993.

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Forecasting with dynamic regression models. Wiley, 1991.

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Harvey, Andrew. Seasonality in dynamic regression models. London School of Economics Centre for Economic Performance, 1994.

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Pankratz, Alan. Forecasting with Dynamic Regression Models. John Wiley & Sons, Inc., 1991. http://dx.doi.org/10.1002/9781118150528.

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Pollock, S. The misspecification of dynamic regression models. London University, Queen Mary and WestfieldCollege, Department of Economics, 1993.

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Pollock, D. S. G. The misspecification of dynamic regression models. University of London. Queen Mary and Westfield College. Department of Economics, 1993.

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L, Koul H., ed. Weighted empirical processes in dynamic nonlinear models. 2nd ed. Springer, 2002.

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Wickens, M. R. Dynamic specification, the long run and the estimation of transformed regression models. University of Southampton, Dept. of Economics, 1986.

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Kiviet, Jan F. Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models. University of Bristol, Department of Economics, 1996.

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Wooldridge, Jeffrey M. Regression-based inference in linear time series models with incomplete dynamics. Dept. of Economics, Massachusetts Institute of Technology, 1990.

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Book chapters on the topic "Dynamic regression models"

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Fahrmeir, Ludwig, and Leonhard Knorr-Held. "Dynamic and Semiparametric Models." In Smoothing and Regression. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118150658.ch18.

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Lecca, Paola. "Dynamic Models." In Identifiability and Regression Analysis of Biological Systems Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41255-5_2.

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Rooney, Niall, David Patterson, Sarab Anand, and Alexey Tsymbal. "Dynamic Integration of Regression Models." In Multiple Classifier Systems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25966-4_16.

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Koul, Hira L. "Goodness-of-fit Tests in Regression." In Weighted Empirical Processes in Dynamic Nonlinear Models. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4613-0055-7_6.

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Escribano, Alvaro, and Oscar Jordá. "Improved Testing and Specification of Smooth Transition Regression Models." In Dynamic Modeling and Econometrics in Economics and Finance. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5129-4_14.

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Kozerski, Jakub, and Marek Kurzynski. "On a New Method of Dynamic Integration of Fuzzy Linear Regression Models." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59162-9_19.

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Mantovan, Pietro, and Andrea Pastore. "Flexible Dynamic Regression Models for Real-time Forecasting of Air Pollutant Concentration." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17111-6_22.

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Saeed, Fauzia, Mujib Rahman, and Maher Mahmood. "Multiple Linear Regression Models for Predicting Surface Damage Due to Repeated Dynamic Loading on Submerged Asphalt Pavement." In Lecture Notes in Civil Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48679-2_91.

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Krasotkina, Olga, Vadim Mottl, Michael Markov, Elena Chernousova, and Dmitry Malakhov. "Methods of Hyperparameter Estimation in Time-Varying Regression Models with Application to Dynamic Style Analysis of Investment Portfolios." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62416-7_31.

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Lu, Yu-zhen, Ming-hui Qu, and Min Zhang. "An Application of Dynamic Regression Model and Residual Auto-Regressive Model in Time Series." In Advances in Neural Networks – ISNN 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12436-0_25.

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Conference papers on the topic "Dynamic regression models"

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Banerjee, B., D. Jayaweera, and S. M. Islam. "Wind power forecasting with dynamic regression models and ageing considerations." In 2011 IEEE PES Innovative Smart Grid Technologies (ISGT Australia). IEEE, 2011. http://dx.doi.org/10.1109/isgt-asia.2011.6167131.

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Judd, Mark A., and Mark B. Colton. "Higher-Order Experimental Haptic Force Models of Mechanical Devices Using Moving Ridge Regression." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2221.

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Accurate modeling of physical devices is one of the keys to improving the realism of haptic simulations. This paper presents general, locally linear haptic force models to describe the feel of complex mechanical systems that exhibit nonlinear static and dynamic behavior. The parameters of these models are estimated from experimental data using moving ridge regression. Nonlinear variations of the locally linear model are presented and analyzed, and the goodness-of-fit of these models is compared. Initial results suggest that higher-order terms do little to improve the quality of this class of haptic models, and that reduced-order models should be further investigated.
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Wehbe, Bilal, and Mario Michael Krell. "Learning coupled dynamic models of underwater vehicles using Support Vector Regression." In OCEANS 2017 - Aberdeen. IEEE, 2017. http://dx.doi.org/10.1109/oceanse.2017.8084596.

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Kolomiets, S. F., A. L. Gavrik, and L. A. Lukanina. "Evaluation of the Dynamic Structure of Turbulent Flows Using Regression Models." In 2020 IEEE Ukrainian Microwave Week (UkrMW). IEEE, 2020. http://dx.doi.org/10.1109/ukrmw49653.2020.9252792.

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Benedict, Shajulin, R. S. Rejitha, and Suja A. Alex. "Energy and Performance Prediction of CUDA Applications using Dynamic Regression Models." In ISEC '16: 9th India Software Engineering Conference. ACM, 2016. http://dx.doi.org/10.1145/2856636.2856643.

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Narváez Cueva, Ricardo, R. Blanchard, G. Guerrón, Diego Chulde, and R. Dixon. "Dynamic Modelling of Updraft Gasifiers: Incidence of Feedstock Quality and Operational Variables in the Transient Model Structure." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5142.

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This paper describes the definition of the transient model structure for an updraft gasifier and the input variables related to the process and the feedstock quality with the most significant influence on the dynamic models and the transient behaviour. For such purpose, a set of open-loop dynamics experiments were carried out in the gasifier. Moreover, the output variables performance was recorded together with the composition analysis of the municipal solid waste batch (MSW). The output and operational variables record was used as base information for performing regressions of transient models with the purpose of determining the model type choice that achieves the largest occurrence frequency of fitting percentage figures above 50%. In addition, the dataset of regression parameters is analysed through feature selection in order to establish the influence of feedstock quality parameters and independent dynamic operational variables in dynamic changes. The model structure selection determined that underdamped, second order with one zero transfer function (P2ZU) is the most accurate case for updraft gasifiers. Regarding the influence of feedstock-related information, feature selection results show that ultimate composition is the group of quality parameters with the most significant influence on transient behaviour. Results also show that recirculation flow rate is the operational variable whose effect in the output variables is the most likely to be predicted and potentially controlled. The results for this variable show that 64.3% of the performed regressions achieved a fitting percentage value above 50%.
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Pandey, Manish, Rituparna Datta, Rajarshi Dey, and Bishakh Bhattacharya. "Multi-objective Optimisation of Dynamic Responses for a Rail Freight Wagon using Regression Models." In 2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2019. http://dx.doi.org/10.1109/iccicc46617.2019.9146044.

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Khoury, Mehdi, Frank Guerin, and George M. Coghill. "Learning dynamic models of compartment systems by combining symbolic regression with fuzzy vector envisionment." In the 2007 GECCO conference companion. ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274050.

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Al Kalbani, Fahad, and Jie Zhang. "Inferential Active Disturbance Rejection Control of a Distillation Column using Dynamic Principal Component Regression Models." In 12th International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005516703580364.

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Prakash, Niket, Jason B. Martz, and Anna G. Stefanopoulou. "A Phenomenological Model for Predicting the Combustion Phasing and Variability of Spark Assisted Compression Ignition (SACI) Engines." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9883.

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An advanced combustion mode, Spark Assisted Compression Ignition (SACI) has shown the ability to extend loads relative to Homogenous Charge Compression Ignition (HCCI) combustion but at reduced fuel conversion efficiency. SACI combustion is initiated by a spark, with flame propagation followed by a rapid autoignition of the remaining end-gas fuel fraction. Extending upon previous work [1,2], the Wiebe function coefficients used to fit the two combustion phases are regressed here as functions of the air path variables and actuator settings. The parameterized regression model enables mean-value modeling and model-based combustion phasing control. SACI combustion however, exhibits high cyclic variability with random characteristics. Thus, combustion phasing feedback control needs to account for the cyclic variability to correctly filter the phasing data. This paper documents the success in regressing the cyclic variability (defined as the standard deviation in combustion phasing) at various operating conditions, again as a function of air path variables and actuator settings. The combination of the regressed mean and standard deviation models is a breakthrough in predicting the mean-value engine behavior and the random statistics of the cycle-to-cycle variability.
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Reports on the topic "Dynamic regression models"

1

Moon, Hyungsik Roger, and Martin Weidner. Dynamic linear panel regression models with interactive fixed effects. IFS, 2013. http://dx.doi.org/10.1920/wp.cem.2013.6313.

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2

Moon, Hyungsik Roger, and Martin Weidner. Dynamic linear panel regression models with interactive fixed effects. IFS, 2014. http://dx.doi.org/10.1920/wp.cem.2014.4714.

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3

Kalouptsidi, Myrto, Paul Scott, and Eduardo Souza-Rodrigues. Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w25134.

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4

Mukhamadeeva, O. R., S. A. Gorbatkov, S. A. Farkhieva, and N. H. Sharafutdinova. Algorithm for constructing a nonlinear dynamic predictive regression model with panel data. OFERNIO, 2020. http://dx.doi.org/10.12731/ofernio.2020.24646.

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