Relevant bibliographies by topics / Dynamic linear models (DLMs)

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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 linear models (DLMs)'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Dynamic linear models (DLMs)"

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Rodriguez, Yesid, Wilmer Pineda, and Oscar Diaz Olariaga. "AIR TRAFFIC FORECAST IN POST-LIBERALIZATION CONTEXT: A DYNAMIC LINEAR MODELS APPROACH." Aviation 24, no. 1 (April 10, 2020): 10–19. http://dx.doi.org/10.3846/aviation.2020.12273.

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The process of air transport liberalization in Colombia began in 1991. Liberalization entailed the entry of private capital into the airport sector which subsequently led, in several temporary phases, to the privatization of the country’s main airports. Simultaneously, new air operators entered the market. This new market situation, supported by the complete deregulation of airfares, generated a dynamic and sustained growth of air transport in Colombia for two decades. Within the context of post-liberalization, this article presents a forecast (medium-term – 5 years period) of air traffic in the country’s main airport using DLMs (Dynamic Linear Models). It has the following advantages vs. the usual forecast calculation methodologies: it detects stochastic tendencies that are hidden in the time series. It also detects structural changes that allow estimating the variable effect of exogenous shocks over time without increasing the number of parameters. From the results obtained, it should be noted that the application of DLMs presents MAPE (Mean Absolute Percentage Error) values below 1%, which guarantees predictions of higher accuracy and thus introduces a new alternative model to develop reliable forecasts in air transport, at least in the medium-term.
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Lamon III, E. Conrad, S. R. Carpenter, and C. A. Stow. "Rates of decrease of polychlorinated biphenyl concentrations in five species of Lake Michigan salmonids." Canadian Journal of Fisheries and Aquatic Sciences 56, no. 1 (January 1, 1999): 53–59. http://dx.doi.org/10.1139/f98-147.

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Dynamic linear models (DLM) were used to study time trends in annual average polychlorinated biphenyl (PCB) concentrations in five species of Lake Michigan salmonids using data collected from 1972 to 1994 by both the Michigan Department of Natural Resources and the Wisconsin Department of Natural Resources. DLMs use an adaptive fitting procedure to track changes over time in both the level (mean) of the series and the rate of increase or decline (growth rate), in contrast with other approaches that fit fixed parameters. We used DLMs to provide retrospective time series of estimates of rates of decline in PCB concentrations. Growth parameters indicate that PCB declines have slowed more than first-order models fit in the mid-1980s would predict. Growth parameters for brown trout (Salmo trutta) and rainbow trout (Oncorhynchus mykiss) increased only slightly, indicating the most consistency with first-order dynamics. Coho (Oncorhynchus kisutch) and chinook salmon (Oncorhynchus tshawytscha) showed a pattern of high rates of decline in the early to mid-1980s followed by a period of slower PCB concentration changes. The temporal pattern of rates of decline for lake trout (Salvelinus namaycush) stood apart from the other species, with a growth parameter that increased steadily during the entire period of record.
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Pitombeira-Neto, Anselmo, Carlos Loureiro, and Luis Carvalho. "Bayesian Inference on Dynamic Linear Models of Day-to-Day Origin-Destination Flows in Transportation Networks." Urban Science 2, no. 4 (December 10, 2018): 117. http://dx.doi.org/10.3390/urbansci2040117.

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Estimation of origin–destination (OD) demand plays a key role in successful transportation studies. In this paper, we consider the estimation of time-varying day-to-day OD flows given data on traffic volumes in a transportation network for a sequence of days. We propose a dynamic linear model (DLM) in order to represent the stochastic evolution of OD flows over time. DLMs are Bayesian state-space models which can capture non-stationarity. We take into account the hierarchical relationships between the distribution of OD flows among routes and the assignment of traffic volumes on links. Route choice probabilities are obtained through a utility model based on past route costs. We propose a Markov chain Monte Carlo algorithm, which integrates Gibbs sampling and a forward filtering backward sampling technique, in order to approximate the joint posterior distribution of mean OD flows and parameters of the route choice model. Our approach can be applied to congested networks and in the case when data are available on only a subset of links. We illustrate the application of our approach through simulated experiments on a test network from the literature.
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Zhou, De, Xiaohua Yu, and Thomas Herzfeld. "Dynamic food demand in urban China." China Agricultural Economic Review 7, no. 1 (February 2, 2015): 27–44. http://dx.doi.org/10.1108/caer-02-2014-0016.

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Purpose – The purpose of this paper is to investigate dynamic food demand in urban China, with use of a complete dynamic demand system – dynamic linear expenditure system-linear approximate dynamic almost ideal demand system (DLES-LA/DAIDS), which pushes forward the techniques of demand analysis. Design/methodology/approach – The authors employ a transitionary demand process and develop a new approach of complete demand system with a two-stage dynamic budgeting: a strongly separable DLES in the first stage and a LA/DAIDS in the second stage. Employing provincial aggregate data (1995-2010) from the China urban household surveys, The authors estimated the demand elasticities for primary food products in urban China. Findings – The results indicate that most primary food products are necessities and price inelastic for urban households in China. The authors also found that the dynamic model tends to yield relatively smaller expenditure elasticities in magnitude than the static models do due to the friction effect of dynamic adjusting costs, such as habit formation, switching costs, and learning process. However, the dynamic effects on own price elasticities are inconclusive due to the add-up restriction. Practical implications – The research contributes to the demand analysis methodologically, and can be used for better projections in policy simulation models. Originality/value – This paper methodologically relaxes the restrictive assumption of instant adjustment in static models and allows consumers to make a dynamic decision in food consumption. Empirically, the authors introduce a new complete dynamic demand model and carry out a case study with the use of urban household data in China.
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Bouassem, Karim, El Mahfoud El Bouatmani, Abdellatif El Assoudi, and El Hassane El Yaagoubi. "State and Unknown Input Simultaneous Estimation for a Class of Discrete-Time Linear Implicit Models : A Heat Exchanger Pilot Process Application." E3S Web of Conferences 297 (2021): 01011. http://dx.doi.org/10.1051/e3sconf/202129701011.

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In this paper, the design problem of simultaneous estimation of unmeasurable states and unknown inputs (UIs) is investigated for a class of discrete-time linear implicit models (DLIMs). The UIs affect both state and output of the system. The approach is based on the separation between dynamic and static relations in the considered DLDM. First, the method permitting to separate dynamic equations from static equations is exposed. Next, an augmented explicit model which contains the dynamic equations and the UIs is constructed. Then an unknown inputs observer (UIO) design in explicit structure is developed. The exponential convergence of the state estimation error is studied by using the Lyapunov theory and the stability condition is given in term of linear matrix inequality (LMI). Finally, an illustrative application of a heat exchanger pilot process is given to show the good performances of the proposed method.
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Loftis, Matt W., and Peter B. Mortensen. "A dynamic linear modelling approach to public policy change." Journal of Public Policy 38, no. 4 (October 16, 2017): 553–79. http://dx.doi.org/10.1017/s0143814x17000186.

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AbstractTheories of public policy change, despite their differences, converge on one point of strong agreement: the relationship between policy and its causes can and does change over time. This consensus yields numerous empirical implications, but our standard analytical tools are inadequate for testing them. As a result, the dynamic and transformative relationships predicted by policy theories have been left largely unexplored in time series analysis of public policy. This article introduces dynamic linear modelling (DLM) as a useful statistical tool for exploring time-varying relationships in public policy. The article offers a detailed exposition of the DLM approach and illustrates its usefulness with a time series analysis of United States defense policy from 1957 to 2010. The results point the way for a new attention to dynamics in the policy process, and the article concludes with a discussion of how this research programme can profit from applying DLMs.
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Shumway, R. H., and D. S. Stoffer. "Dynamic Linear Models with Switching." Journal of the American Statistical Association 86, no. 415 (September 1991): 763–69. http://dx.doi.org/10.1080/01621459.1991.10475107.

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Lin Shang, Han. "Dynamic linear models with R." Journal of Applied Statistics 38, no. 10 (October 2011): 2369–70. http://dx.doi.org/10.1080/02664763.2010.517938.

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Lenk, Peter J., and Chih-Ling Tsai. "Transformations and dynamic linear models." Journal of Forecasting 9, no. 3 (May 1990): 219–32. http://dx.doi.org/10.1002/for.3980090303.

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Deistler, M. "Linear dynamic errors-in-variables models." Journal of Applied Probability 23, A (1986): 23–39. http://dx.doi.org/10.2307/3214340.

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Linear dynamical systems where both inputs and outputs are contaminated by errors are considered. A characterization of the sets of all observationally equivalent transfer functions is given, the role of the causality assumption is investigated and conditions for identifiability in the case of Gaussian as well as non-Gaussian observations are derived.
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Dissertations / Theses on the topic "Dynamic linear models (DLMs)"

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Tongur, Can. "Seasonal Adjustment and Dynamic Linear Models." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-89496.

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Dynamic Linear Models are a state space model framework based on the Kalman filter. We use this framework to do seasonal adjustments of empirical and artificial data. A simple model and an extended model based on Gibbs sampling are used and the results are compared with the results of a standard seasonal adjustment method. The state space approach is then extended to discuss direct and indirect seasonal adjustments. This is achieved by applying a seasonal level model with no trend and some specific input variances that render different signal-to-noise ratios. This is illustrated for a system consisting of two artificial time series. Relative efficiencies between direct, indirect and multivariate, i.e. optimal, variances are then analyzed. In practice, standard seasonal adjustment packages do not support optimal/multivariate seasonal adjustments, so a univariate approach to simultaneous estimation is presented by specifying a Holt-Winters exponential smoothing method. This is applied to two sets of time series systems by defining a total loss function that is specified with a trade-off weight between the individual series’ loss functions and their aggregate loss function. The loss function is based on either the more conventional squared errors loss or on a robust Huber loss. The exponential decay parameters are then estimated by minimizing the total loss function for different trade-off weights. It is then concluded what approach, direct or indirect seasonal adjustment, is to be preferred for the two time series systems. The dynamic linear modeling approach is also applied to Swedish political opinion polls to assert the true underlying political opinion when there are several polls, with potential design effects and bias, observed at non-equidistant time points. A Wiener process model is used to model the change in the proportion of voters supporting either a specific party or a party block. Similar to stock market models, all available (political) information is assumed to be capitalized in the poll results and is incorporated in the model by assimilating opinion poll results with the model through Bayesian updating of the posterior distribution. Based on the results, we are able to assess the true underlying voter proportion and additionally predict the elections.

At the time of doctoral defence the following papers were unpublished and had a status as follows: Paper 3: Manuscript; Paper 4: Manuscripts

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Randell, David. "Bayes linear variance learning for mixed linear temporal models." Thesis, Durham University, 2012. http://etheses.dur.ac.uk/3646/.

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Modelling of complex corroding industrial systems is ritical to effective inspection and maintenance for ssurance of system integrity. Wall thickness and corrosion rate are modelled for multiple dependent corroding omponents, given observations of minimum wall thickness per component. At each inspection, partial observations of the system are considered. A Bayes Linear approach is adopted simplifying parameter estimation and avoiding often unrealistic distributional assumptions. Key system variances are modelled, making exchangeability assumptions to facilitate analysis for sparse inspection time-series. A utility based criterion is used to assess quality of inspection design and aid decision making. The model is applied to inspection data from pipework networks on a full-scale offshore platform.
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Frühwirth-Schnatter, Sylvia. "Data Augmentation and Dynamic Linear Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1992. http://epub.wu.ac.at/392/1/document.pdf.

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We define a subclass of dynamic linear models with unknown hyperparameters called d-inverse-gamma models. We then approximate the marginal p.d.f.s of the hyperparameter and the state vector by the data augmentation algorithm of Tanner/Wong. We prove that the regularity conditions for convergence hold. A sampling based scheme for practical implementation is discussed. Finally, we illustrate how to obtain an iterative importance sampling estimate of the model likelihood. (author's abstract)
Series: Forschungsberichte / Institut für Statistik
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Frankel, Joe. "Linear dynamic models for automatic speech recognition." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/1087.

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The majority of automatic speech recognition (ASR) systems rely on hidden Markov models (HMM), in which the output distribution associated with each state is modelled by a mixture of diagonal covariance Gaussians. Dynamic information is typically included by appending time-derivatives to feature vectors. This approach, whilst successful, makes the false assumption of framewise independence of the augmented feature vectors and ignores the spatial correlations in the parametrised speech signal. This dissertation seeks to address these shortcomings by exploring acoustic modelling for ASR with an application of a form of state-space model, the linear dynamic model (LDM). Rather than modelling individual frames of data, LDMs characterize entire segments of speech. An auto-regressive state evolution through a continuous space gives a Markovian model of the underlying dynamics, and spatial correlations between feature dimensions are absorbed into the structure of the observation process. LDMs have been applied to speech recognition before, however a smoothed Gauss-Markov form was used which ignored the potential for subspace modelling. The continuous dynamical state means that information is passed along the length of each segment. Furthermore, if the state is allowed to be continuous across segment boundaries, long range dependencies are built into the system and the assumption of independence of successive segments is loosened. The state provides an explicit model of temporal correlation which sets this approach apart from frame-based and some segment-based models where the ordering of the data is unimportant. The benefits of such a model are examined both within and between segments. LDMs are well suited to modelling smoothly varying, continuous, yet noisy trajectories such as found in measured articulatory data. Using speaker-dependent data from the MOCHA corpus, the performance of systems which model acoustic, articulatory, and combined acoustic-articulatory features are compared. As well as measured articulatory parameters, experiments use the output of neural networks trained to perform an articulatory inversion mapping. The speaker-independent TIMIT corpus provides the basis for larger scale acoustic-only experiments. Classification tasks provide an ideal means to compare modelling choices without the confounding influence of recognition search errors, and are used to explore issues such as choice of state dimension, front-end acoustic parametrization and parameter initialization. Recognition for segment models is typically more computationally expensive than for frame-based models. Unlike frame-level models, it is not always possible to share likelihood calculations for observation sequences which occur within hypothesized segments that have different start and end times. Furthermore, the Viterbi criterion is not necessarily applicable at the frame level. This work introduces a novel approach to decoding for segment models in the form of a stack decoder with A* search. Such a scheme allows flexibility in the choice of acoustic and language models since the Viterbi criterion is not integral to the search, and hypothesis generation is independent of the particular language model. Furthermore, the time-asynchronous ordering of the search means that only likely paths are extended, and so a minimum number of models are evaluated. The decoder is used to give full recognition results for feature-sets derived from the MOCHA and TIMIT corpora. Conventional train/test divisions and choice of language model are used so that results can be directly compared to those in other studies. The decoder is also used to implement Viterbi training, in which model parameters are alternately updated and then used to re-align the training data.
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Browne, Perry James. "The filtering of linear dynamic models with switching coefficients." Thesis, University of Sussex, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295975.

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Veerapen, Parmaseeven Pillay. "Recurrence relationships and model monitoring for Dynamic Linear Models." Thesis, University of Warwick, 1991. http://wrap.warwick.ac.uk/109386/.

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This thesis considers the incorporation and deletion of information in Dynamic Linear Models together with the detection of model changes and unusual values. General results are derived for the Normal Dynamic Linear Model which naturally also relate to second order modelling such as occurs with the Kalman Filter, linear least squares and linear Bayes estimation. The incorporation of new information, the assessment of its influence and the deletion of old or suspect information are important features of all sequential models. Many dynamic sequential models exhibit conditioned, independence properties. Important results concerning conditional independence in normal models are established which provide the framework and the tools necessary to develop neat procedures and to obtain appropriate recurrence relationships for data incorporation and deletion. These are demonstrated in the context of dynamic linear models, with particularly simple procedures for discount regression models. Appropriate model and forecast monitoring mechanisms are required to detect model changes and unusual values. Cumulative Sum (Cusum) techniques widely used in quality control and in model and forecast monitoring have been the source of inspiration in this context. Bearing in mind that a single sided Cusum may be regarded essentially as a sequence of sequential tests, such a Cusum is, in many cases, equivalent to a Sequence of Sequential Probability Ratio Tests in many cases, as for example in the case of the Exponential Family. A relationship between Cusums and Bayesian decision is established for a useful class of linear loss functions. It is found to apply to the Normal and other important practical cases. For V- mask Cusum graphs, a particularly interesting result which emerges is the interpretation of the distance of the V vertex from the latest plotted point as the prior precision in terms of a number of equivalent observations.
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Flury, Thomas. "Econometrics of dynamic non-linear models in macroeconomics and finance." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523095.

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Miyandoab, Sara Alizadeh. "Three essays on non-linear effects in dynamic macroeconomic models." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/39350.

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This thesis has aimed to analyse non-linearity in dynamic models. Attention has focused on the class of dynamic models that accommodate the possibility of distributional modification in the models. In chapter 1, I have studied the non-linear effects of policy shocks in the classical DSGE model. The analysis of such model is subject to two types of shocks, technology and monetary policy. I have extended the analysis of classical model by allowing for the distributional modification of monetary policy shock using WSN distribution. This study reveals the extent to which the distribution of macroeconomic variables may response to policy actions and outcomes involved. Moreover, in classical monetary model the long run behaviour of the level of inflation with respect to the inflation uncertainty has investigated. I have also analysed the dynamic model of AR-GARCH time series. I have investigated the possible non-linear and asymmetric effects of distributional assumptions on the behaviour of the QMLE of the parameters in AR(1)-GARCH(1,1) model. A Monte Carlo experiment is set up to evaluate the distributional misspecification in aforementioned model by applying both symmetric and asymmetric WSN distribution across a range of mean and volatility persistence. The other contribution in chapter 2 is computing the quantiles under distributional misspecification in AR-GARCH model. In terms of the accuracy of the estimated quantiles, I have implemented the bootstrap technique. In addition, in chapter 3 the attention has concentrated on the procedures with suitable technique for the analysis of unit root tests. The usefulness of bootstrap technique is investigated in the context of unit root test applying in stock indices and exchange rate series. I evaluate the popular unit root tests including Augmented Dickey Fuller(ADF) and Phillips Perron(PP) as well as DF-GLS. Furthermore, this chapter attempts to answer the question of how the difference in frequency of empirical data say, monthly, weekly, and daily might affect the unit root results.
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Dimopoulos, Konstantinos Panagiotis. "Non-linear control strategies using input-state network models." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340027.

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Karlon, Kathleen Mary. "Determining optimal architecture for dynamic linear models in time series applications /." Electronic version (PDF), 2006. http://dl.uncw.edu/etd/2006/karlonk/kathleenkarlon.pdf.

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Books on the topic "Dynamic linear models (DLMs)"

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Sonia, Petrone, Petris Giovanni, and SpringerLink (Online service), eds. Dynamic Linear Models with R. New York, NY: Springer-Verlag New York, 2009.

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Campagnoli, Patrizia, Sonia Petrone, and Giovanni Petris. Dynamic Linear Models with R. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b135794.

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Hansen, Lars Peter. Recursive linear models of dynamic economies. Cambridge, MA: National Bureau of Economic Research, 1990.

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Pesaran, Hashem. Dynamic linear models for heterogeneous panels. Cambridge: Department of Applied Economics, University of Cambridge, 1995.

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Chang-Jin, Kim. Dynamic linear models with Markov-switching. Toronto, Ont: York University, Dept. of Economics, 1991.

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Jeff, Harrison, ed. Bayesian forecasting and dynamic models. 2nd ed. New York: Springer, 1997.

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R, Prucha Ingmar, ed. Dynamic nonlinear econometric models: Asymptotic theory. New York: Springer-Verlag, 1997.

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Catrien C. J. H. Bijleveld. Exploratory linear dynamic systems analysis. Leiden, Netherlands: DSWO Press, University of Leiden, 1989.

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West, Mike. Bayesian forecasting and dynamic models. New York: Springer, 1989.

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

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Book chapters on the topic "Dynamic linear models (DLMs)"

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Petris, Giovanni, Sonia Petrone, and Patrizia Campagnoli. "Dynamic linear models." In Dynamic Linear Models with R, 31–84. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b135794_2.

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Sevestre, Patrick, and Alain Trognon. "Linear Dynamic Models." In Advanced Studies in Theoretical and Applied Econometrics, 95–117. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-009-0375-3_6.

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Broemeling, Lyle D. "Dynamic Linear Models." In Bayesian Analysis of Time Series, 179–220. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429488443-8.

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Prado, Raquel, Marco A. R. Ferreira, and Mike West. "Dynamic linear models." In Time Series, 131–68. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351259422-4.

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Sevestre, Patrick, and Alain Trognon. "Dynamic Linear Models." In Advanced Studies in Theoretical and Applied Econometrics, 120–44. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0137-7_7.

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West, Mike, and Jeff Harrison. "Non-Linear Dynamic Models." In Springer Series in Statistics, 511–46. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4757-9365-9_13.

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Christ, Steffen. "Self-Learning Linear Models." In Operationalizing Dynamic Pricing Models, 67–95. Wiesbaden: Gabler, 2011. http://dx.doi.org/10.1007/978-3-8349-6184-6_4.

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Simonovits, András. "Discrete-Time Linear Models." In Mathematical Methods in Dynamic Economics, 49–67. London: Palgrave Macmillan UK, 2000. http://dx.doi.org/10.1057/9780230513532_3.

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Boguslavskiy, Josif A. "Linear Estimators of a Random Parameter Vector." In Dynamic Systems Models, 1–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-04036-3_1.

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Petris, Giovanni, Sonia Petrone, and Patrizia Campagnoli. "Models with unknown parameters." In Dynamic Linear Models with R, 143–206. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b135794_4.

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Conference papers on the topic "Dynamic linear models (DLMs)"

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Lipowsky, Holger, Stephan Staudacher, Michael Bauer, and Klaus-Juergen Schmidt. "Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59447.

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This paper presents a novel technique for automatic change detection of the performance of gas turbines. In addition to change detection the proposed technique has the ability to perform a prognosis of measurement values. The proposed technique is deemed to be new in the field of gas turbine monitoring and forms the basic building block of a patent pending filed by the authors [1]. The technique used is called Bayesian Forecasting and is applied to Dynamic Linear Models (DLMs). The idea of Bayesian Forecasting is based on Bayes’ Theorem, which enables the calculation of conditional probabilities. In combination with DLMs (which break down the chronological sequence of the observed parameter into mathematical components like value, gradient, etc.) Bayesian Forecasting can be used to calculate probability density functions prior to the next observation, so called forecast distributions. The change detection is carried out by comparing the current model with an alternative model which mean value is shifted by a prescribed offset. If the forecast distribution of the alternative model better fits the actual observation, a potential change is detected. To determine whether the respective observation is a single outlier or the first observation of a significant change, a special logic is developed. Studies have shown that a confident change detection is possible for a change height of only 1.5 times the standard deviation of the observed signal. In terms of prognostic abilities the proposed technique not only estimates the point of time of a potential limit exceedance of respective parameters, but also calculates confidence bounds as well as probability density and cumulative distribution functions for the prognosis.
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Zhang, S., W. Zhou, S. Kariyawasam, and M. Al-Amin. "Characterization of the Growth of Corrosion Defects on Energy Pipelines Using Bayesian Dynamic Linear Model." In 2014 10th International Pipeline Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/ipc2014-33215.

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This paper describes the use of the second-order polynomial dynamic linear model (DLM) to characterize the growth of the depth of corrosion defects on energy pipelines using imperfect data obtained from multiple high-resolution in-line inspections (ILI). The growth model is formulated by incorporating the general form of the measurement error (including the biases and random scattering error) of the ILI tools as well as the correlations between the random scattering errors of different tools. The temporal variability of the corrosion growth is captured by allowing the average growth rate between two successive inspections to vary with time. The Markov Chain Monte Carlo simulation is employed to carry out the Bayesian updating of the growth model and evaluate the posterior distributions of the model parameters. An example involving real ILI data collected from an in-service natural gas pipeline is employed to illustrate and validate the growth model. The analysis results show that the defect depths predicted by the proposed model agree well with the actual depths and are more accurate than those predicted by the Gamma process-based growth models reported in the literature.
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Anderson, B. D. O., and M. Deistler. "Generalized linear dynamic factor models - a structure theory." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4739367.

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Yao, Hengshuai, Csaba Szepesvari, Bernardo Avila Pires, and Xinhua Zhang. "Pseudo-MDPs and factored linear action models." In 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). IEEE, 2014. http://dx.doi.org/10.1109/adprl.2014.7010633.

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GORDIS, JOSHUA. "SPATIAL, FREQUENCY DOMAIN UPDATING QF LINEAR, STRUCTURAL DYNAMIC MODELS." In 34th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-1652.

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Samdin, S. Balqis, Chee-Ming Ting, Sh-Hussain Salleh, A. K. Ariff, and A. B. Mohd Noor. "Linear dynamic models for classification of single-trial EEG." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610628.

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"DYNAMICALLY MIXING DYNAMIC LINEAR MODELS WITH APPLICATIONS IN FINANCE." In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003712602950302.

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Nezlobin, Alexander N. "Dynamic models of granular medium: Kandaurov standard linear medium." In SPIE Proceedings, edited by Alexander I. Melker. SPIE, 2003. http://dx.doi.org/10.1117/12.517967.

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Lu, Huiyan, Ruiqi Liu, Xiujuan Du, Haiqi Liu, Mei Lin, Long Jin, and Jiliang Zhang. "On RNN Models for Solving Dynamic System of Linear Equations." In 2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2019. http://dx.doi.org/10.1109/icicip47338.2019.9012192.

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Lee, Hung-Shin, Yu-Chin Shih, Hsin-Min Wang, and Shyh-Kang Jeng. "Subspace-based phonotactic language recognition using multivariate dynamic linear models." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638993.

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Reports on the topic "Dynamic linear models (DLMs)"

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Hansen, Lars Peter, and Thomas Sargent. Recursive Linear Models of Dynamic Economies. Cambridge, MA: National Bureau of Economic Research, October 1990. http://dx.doi.org/10.3386/w3479.

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Merl, D. M. Network Traffic Monitoring Using Poisson Dynamic Linear Models. Office of Scientific and Technical Information (OSTI), May 2011. http://dx.doi.org/10.2172/1122210.

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

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

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

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Trudnowski, D. J. Characteristics of identifying linear dynamic models from impulse response data using Prony analysis. Office of Scientific and Technical Information (OSTI), December 1992. http://dx.doi.org/10.2172/10114740.

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Trudnowski, D. J. Characteristics of identifying linear dynamic models from impulse response data using Prony analysis. Office of Scientific and Technical Information (OSTI), December 1992. http://dx.doi.org/10.2172/6843209.

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Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani, and Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, March 2019. http://dx.doi.org/10.34074/ocds.12019.

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Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performance of stock market prices. The purpose of this review is to investigate different techniques applied in stock market price prediction with special emphasis on hybrid models.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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