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

Author: Grafiati

Published: 19 February 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Shumway, R. H., and D. S. Stoffer. "Correction: Dynamic Linear Models With Switching." Journal of the American Statistical Association 87, no. 419 (September 1992): 913. http://dx.doi.org/10.2307/2290252.

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12

Eremina, Victoria Vladimirovna, Svetlana Yurevna Lanina, and Olga Sergeevna Kosolapova. "LINEAR DYNAMIC MODELS IONIC POLARIZATION WATER." V mire nauchnykh otkrytiy, no. 12 (December 23, 2014): 173. http://dx.doi.org/10.12731/wsd-2014-12-14.

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13

Frühwirth-Schnatter, Sylvia. "DATA AUGMENTATION AND DYNAMIC LINEAR MODELS." Journal of Time Series Analysis 15, no. 2 (March 1994): 183–202. http://dx.doi.org/10.1111/j.1467-9892.1994.tb00184.x.

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14

Deistler, M. "Linear dynamic errors-in-variables models." Journal of Applied Probability 23, A (1986): 23–39. http://dx.doi.org/10.1017/s002190020011695x.

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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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15

Forni, Mario, and Marco Lippi. "Aggregation of linear dynamic microeconomic models." Journal of Mathematical Economics 31, no. 1 (February 1999): 131–58. http://dx.doi.org/10.1016/s0304-4068(98)00060-3.

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16

Deistler, M., and B. D. O. Anderson. "Linear dynamic errors-in-variables models." Journal of Econometrics 41, no. 1 (May 1989): 39–63. http://dx.doi.org/10.1016/0304-4076(89)90042-0.

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17

Govaerts, Bernadette, David F. Hendry, and Jean-François Richard. "Encompassing in stationary linear dynamic models." Journal of Econometrics 63, no. 1 (July 1994): 245–70. http://dx.doi.org/10.1016/0304-4076(93)01567-6.

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18

Kim, Chang-Jin. "Dynamic linear models with Markov-switching." Journal of Econometrics 60, no. 1-2 (January 1994): 1–22. http://dx.doi.org/10.1016/0304-4076(94)90036-1.

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19

Frankel, Joe, and Simon King. "Speech Recognition Using Linear Dynamic Models." IEEE Transactions on Audio, Speech and Language Processing 15, no. 1 (January 2007): 246–56. http://dx.doi.org/10.1109/tasl.2006.876766.

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20

Snyder, R. D. "Recursive Estimation of Dynamic Linear Models." Journal of the Royal Statistical Society: Series B (Methodological) 47, no. 2 (January 1985): 272–76. http://dx.doi.org/10.1111/j.2517-6161.1985.tb01355.x.

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21

H�skuldsson, Agnar. "Stable solutions of linear dynamic models." Journal of Chemometrics 14, no. 5-6 (2000): 401–21. http://dx.doi.org/10.1002/1099-128x(200009/12)14:5/6<401::aid-cem642>3.0.co;2-p.

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22

Schmidt, Alexandra Mello, and Dani Gamerman. "Temporal Aggregation in Dynamic Linear Models." Journal of Forecasting 16, no. 5 (September 1997): 293–310. http://dx.doi.org/10.1002/(sici)1099-131x(199709)16:5<293::aid-for662>3.0.co;2-q.

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23

Kim, Sihyeon, and Byeongchan Seong. "Grouping stocks using dynamic linear models." Communications for Statistical Applications and Methods 29, no. 6 (November 30, 2022): 695–708. http://dx.doi.org/10.29220/csam.2022.29.6.695.

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24

Lin, Chin, Yung-Tsai Lee, Feng-Jen Wu, Shing-An Lin, Chia-Jung Hsu, Chia-Cheng Lee, Dung-Jang Tsai, and Wen-Hui Fang. "The Application of Projection Word Embeddings on Medical Records Scoring System." Healthcare 9, no. 10 (September 29, 2021): 1298. http://dx.doi.org/10.3390/healthcare9101298.

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Medical records scoring is important in a health care system. Artificial intelligence (AI) with projection word embeddings has been validated in its performance disease coding tasks, which maintain the vocabulary diversity of open internet databases and the medical terminology understanding of electronic health records (EHRs). We considered that an AI-enhanced system might be also applied to automatically score medical records. This study aimed to develop a series of deep learning models (DLMs) and validated their performance in medical records scoring task. We also analyzed the practical value of the best model. We used the admission medical records from the Tri-Services General Hospital during January 2016 to May 2020, which were scored by our visiting staffs with different levels from different departments. The medical records were scored ranged 0 to 10. All samples were divided into a training set (n = 74,959) and testing set (n = 152,730) based on time, which were used to train and validate the DLMs, respectively. The mean absolute error (MAE) was used to evaluate each DLM performance. In original AI medical record scoring, the predicted score by BERT architecture is closer to the actual reviewer score than the projection word embedding and LSTM architecture. The original MAE is 0.84 ± 0.27 using the BERT model, and the MAE is 1.00 ± 0.32 using the LSTM model. Linear mixed model can be used to improve the model performance, and the adjusted predicted score was closer compared to the original score. However, the project word embedding with the LSTM model (0.66 ± 0.39) provided better performance compared to BERT (0.70 ± 0.33) after linear mixed model enhancement (p < 0.001). In addition to comparing different architectures to score the medical records, this study further uses a mixed linear model to successfully adjust the AI medical record score to make it closer to the actual physician’s score.

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25

Oguchi, Noriyoshi, and Takao Fukuchi. "On Temporal Aggregation of Linear Dynamic Models." International Economic Review 31, no. 1 (February 1990): 187. http://dx.doi.org/10.2307/2526636.

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26

Young, P. C., and M. Ratto. "Statistical Emulation of Large Linear Dynamic Models." Technometrics 53, no. 1 (February 2011): 29–43. http://dx.doi.org/10.1198/tech.2010.07151.

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27

West, Mike, P. Jeff Harrison, and Helio S. Migon. "Dynamic Generalized Linear Models and Bayesian Forecasting." Journal of the American Statistical Association 80, no. 389 (March 1985): 73–83. http://dx.doi.org/10.1080/01621459.1985.10477131.

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28

Guo, Guangbao, Wei Shao, Lu Lin, and Xuehu Zhu. "Parallel tempering for dynamic generalized linear models." Communications in Statistics - Theory and Methods 45, no. 21 (January 14, 2016): 6299–310. http://dx.doi.org/10.1080/03610926.2014.960586.

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29

Glynn, Chris, Surya T. Tokdar, Brian Howard, and David L. Banks. "Bayesian Analysis of Dynamic Linear Topic Models." Bayesian Analysis 14, no. 1 (March 2019): 53–80. http://dx.doi.org/10.1214/18-ba1100.

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30

Soudant, D., B. Beliaeff, and G. Thomas. "Dynamic linear Bayesian models in phytoplankton ecology." Ecological Modelling 99, no. 2-3 (June 1997): 161–69. http://dx.doi.org/10.1016/s0304-3800(97)01949-2.

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31

ODOLPHIN, E. J. G., and S. E. JOHNSON. "Decomposition of Time Series Dynamic Linear Models." Journal of Time Series Analysis 24, no. 5 (September 2003): 513–27. http://dx.doi.org/10.1111/1467-9892.00319.

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32

Ma, Tao, Sundararajan Srinivasan, Georgios Lazarou, and Joseph Picone. "Continuous speech recognition using linear dynamic models." International Journal of Speech Technology 17, no. 1 (June 6, 2013): 11–16. http://dx.doi.org/10.1007/s10772-013-9200-x.

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33

Lindsey, J. K., and P. Lambert. "Dynamic generalized linear models and repeated measurements." Journal of Statistical Planning and Inference 47, no. 1-2 (October 1995): 129–39. http://dx.doi.org/10.1016/0378-3758(94)00126-g.

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34

Hendry, David F. "Typologies of linear dynamic systems and models." Journal of Statistical Planning and Inference 49, no. 2 (January 1996): 177–201. http://dx.doi.org/10.1016/0378-3758(95)00036-4.

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35

Dagum, Estela Bee, and Benoît Quenneville. "Dynamic linear models for time series components." Journal of Econometrics 55, no. 1-2 (January 1993): 333–51. http://dx.doi.org/10.1016/0304-4076(93)90020-6.

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36

Findley, David F. "Dynamic linear models for time series components." Journal of Econometrics 55, no. 1-2 (January 1993): 353–56. http://dx.doi.org/10.1016/0304-4076(93)90021-v.

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37

Frankel, Joe, and Simon King. "Observation process adaptation for linear dynamic models." Speech Communication 48, no. 9 (September 2006): 1192–99. http://dx.doi.org/10.1016/j.specom.2006.05.001.

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38

Alves, Mariane B., Dani Gamerman, and Marco AR Ferreira. "Transfer functions in dynamic generalized linear models." Statistical Modelling: An International Journal 10, no. 1 (April 2010): 03–40. http://dx.doi.org/10.1177/1471082x0801000102.

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39

Gonçalves, Kelly C. M., Hélio S. Migon, and Leonardo S. Bastos. "Dynamic Quantile Linear Models: A Bayesian Approach." Bayesian Analysis 15, no. 2 (June 2020): 335–62. http://dx.doi.org/10.1214/19-ba1156.

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40

Salvador, Manuel, and Pilar Gargallo. "Automatic selective intervention in dynamic linear models." Journal of Applied Statistics 30, no. 10 (December 2003): 1161–84. http://dx.doi.org/10.1080/0266476032000107178.

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41

Gargallo, Pilar, and Manuel Salvador. "Monitoring Residual Autocorrelations in Dynamic Linear Models." Communications in Statistics - Simulation and Computation 32, no. 4 (January 11, 2003): 1079–104. http://dx.doi.org/10.1081/sac-120023879.

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42

Barbosa, Emanuel, and Jeff Harrison. "Variance estimation for multivariate dynamic linear models." Journal of Forecasting 11, no. 7 (November 1992): 621–28. http://dx.doi.org/10.1002/for.3980110704.

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43

Das, Sourish, and Dipak K. Dey. "On Dynamic Generalized Linear Models with Applications." Methodology and Computing in Applied Probability 15, no. 2 (October 5, 2011): 407–21. http://dx.doi.org/10.1007/s11009-011-9255-6.

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44

Anderson, Brian D. O., Manfred Deistler, and Marco Lippi. "Linear System Challenges of Dynamic Factor Models." Econometrics 10, no. 4 (December 6, 2022): 35. http://dx.doi.org/10.3390/econometrics10040035.

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A survey is provided dealing with the formulation of modelling problems for dynamic factor models, and the various algorithm possibilities for solving these modelling problems. Emphasis is placed on understanding requirements for the handling of errors, noting the relevance of the proposed application of the model, be it for example prediction or business cycle determination. Mixed frequency problems are also considered, in which certain entries of an underlying vector process are only available for measurement at a submultiple frequency of the original process. Certain classes of processes are shown to be generically identifiable, and others not to have this property.

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45

Sedykh, I. A., and M. Yu Kikin. "LINEAR AND QUADRATIC COMPLEX-VALUED DYNAMIC NEIGHBORHOOD MODELS." Vestnik LSTU, no. 2 (2020): 14–19. http://dx.doi.org/10.53015/23049235_2020_2_14.

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46

Evans, Lewis, and Graeme Wells. "Confidence Regions for Multipliers in Linear Dynamic Models." Econometrica 54, no. 3 (May 1986): 699. http://dx.doi.org/10.2307/1911316.

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47

Delgado, Miguel A., Javier Hidalgo, and Carlos Velasco. "Distribution-free specification tests for dynamic linear models." Econometrics Journal 12 (January 2009): S105—S134. http://dx.doi.org/10.1111/j.1368-423x.2009.00280.x.

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48

Pourahmadi, M., and M. J. Daniels. "Dynamic Conditionally Linear Mixed Models for Longitudinal Data." Biometrics 58, no. 1 (March 2002): 225–31. http://dx.doi.org/10.1111/j.0006-341x.2002.00225.x.

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49

Erdemir, Aytac. "Book Review: Recursive Models of Dynamic Linear Economies." American Economist 59, no. 2 (November 2014): 197–99. http://dx.doi.org/10.1177/056943451405900211.

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50

Bolfarine, Heleno. "Finite population prediction under dynamic generalized linear models." Communications in Statistics - Simulation and Computation 17, no. 1 (January 1988): 187–207. http://dx.doi.org/10.1080/03610918808812656.

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