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Journal articles on the topic 'Variable structure multiple model estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Xiao-Rong Li and Y. Bar-Shalom. "Multiple-model estimation with variable structure." IEEE Transactions on Automatic Control 41, no. 4 (1996): 478–93. http://dx.doi.org/10.1109/9.489270.

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2

Katsikas, S. K., S. D. Likothanassis, G. N. Beligiannis, K. G. Berkeris, and D. A. Fotakis. "Genetically determined variable structure multiple model estimation." IEEE Transactions on Signal Processing 49, no. 10 (2001): 2253–61. http://dx.doi.org/10.1109/78.950781.

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3

Lan, Jian, X. Rong Li, and Chundi Mu. "Best Model Augmentation for Variable-Structure Multiple-Model Estimation." IEEE Transactions on Aerospace and Electronic Systems 47, no. 3 (2011): 2008–25. http://dx.doi.org/10.1109/taes.2011.5937279.

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Lan, Jian, and X. Rong Li. "Equivalent-Model Augmentation for Variable-Structure Multiple-Model Estimation." IEEE Transactions on Aerospace and Electronic Systems 49, no. 4 (2013): 2615–30. http://dx.doi.org/10.1109/taes.2013.6621840.

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Lan, Jian, and X. Rong Li. "Equivalent-Model Augmentation for Variable-Structure Multiple-Model Estimation." IEEE Transactions on Aerospace and Electronic Systems 49, no. 4 (2014): 2615–30. http://dx.doi.org/10.1109/taes.2014.6619952.

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Rong Li, X. "Multiple-model estimation with variable structure. II. Model-set adaptation." IEEE Transactions on Automatic Control 45, no. 11 (2000): 2047–60. http://dx.doi.org/10.1109/9.887626.

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7

Shen-tu, Han, Anke Xue, and Dongliang Peng. "Geometrical entropy approach for variable structure multiple-model estimation." Chinese Journal of Aeronautics 28, no. 4 (2015): 1131–46. http://dx.doi.org/10.1016/j.cja.2015.06.006.

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8

X Rong Li, Xiaorong Zwi, and Youmin Zwang. "Multiple-model estimation with variable structure. III. Model-group switching algorithm." IEEE Transactions on Aerospace and Electronic Systems 35, no. 1 (1999): 225–41. http://dx.doi.org/10.1109/7.745694.

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Rong Li, X., and Youmin Zhang. "Multiple-model estimation with variable structure. V. Likely-model set algorithm." IEEE Transactions on Aerospace and Electronic Systems 36, no. 2 (2000): 448–66. http://dx.doi.org/10.1109/7.845222.

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10

Li, X. Rong, Vesselin P. Jilkov, Jifeng Ru, and Anwer Bashi. "EXPECTED-MODE AUGMENTATION ALGORITHMS FOR VARIABLE-STRUCTURE MULTIPLE-MODEL ESTIMATION." IFAC Proceedings Volumes 35, no. 1 (2002): 175–80. http://dx.doi.org/10.3182/20020721-6-es-1901.00440.

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Mousavi Firdeh, Seyed Reza, Mahdi Karrari, and Mohammad Bagher Menhaj. "Fast Variable-Structure Multiple-Model Estimation Using Modified Likely-Model Set Tracker." Iranian Journal of Science and Technology, Transactions of Electrical Engineering 43, S1 (2018): 109–20. http://dx.doi.org/10.1007/s40998-018-0111-3.

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Li, Zhen-Xing, Yun Wang, Jin-Mang Liu, Ni Peng, and Lin-Hai Gan. "Variable-structure interacting multiple-model estimation for group targets tracking with random matrices." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 7 (2017): 1201–11. http://dx.doi.org/10.1177/0954410016688123.

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In order to improve the estimation performance of interacting multiple model tracking algorithm for group targets, the expected-mode augmentation variable-structure interacting multiple model (EMA-VSIMM) and the best model augmentation variable-structure interacting multiple model (BMA-VSIMM) tracking algorithms are presented in this paper. First, by using the EMA method, a more proper expected-mode set has been chosen from the basic model set of group targets, which can make the selected tracking models better match up to the true mode. The BMA algorithm uses a fixed parameter model of differ

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13

X. Rong Li, V. P. Jilkov, and J. Ru. "Multiple-model estimation with variable structure- Part VI: expected-mode augmentation." IEEE Transactions on Aerospace and Electronic Systems 41, no. 3 (2005): 853–67. http://dx.doi.org/10.1109/taes.2005.1541435.

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14

Ru, J., and X. Rong Li. "Variable-Structure Multiple-Model Approach to Fault Detection, Identification, and Estimation." IEEE Transactions on Control Systems Technology 16, no. 5 (2008): 1029–38. http://dx.doi.org/10.1109/tcst.2007.916318.

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15

X. Rong Li, Youmin Zhang, and Xiaorong Zhi. "Multiple-model estimation with variable structure. IV. Design and evaluation of model-group switching algorithm." IEEE Transactions on Aerospace and Electronic Systems 35, no. 1 (1999): 242–54. http://dx.doi.org/10.1109/7.745695.

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AGUILERA, R. CARREÑO, WEN YU, J. C. TOVAR RODRÍGUEZ, M. ANTONIO ACEVEDO MOSQUEDA, and S. L. GOMEZ CORONEL. "A NONLINEAR ESTIMATION ACOUSTIC TRANSDUCER BASED ON A VARIABLE STRUCTURE CONTROL LAW." Fractals 25, no. 02 (2017): 1750018. http://dx.doi.org/10.1142/s0218348x17500189.

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Electronic sensor devices in geophysical processes are required to measure and automate different tasks. Throughout history, people have created multiple type devices, but acoustics have an important application such as the content form description in deep wells, watersheds, lakes, caves, among others. The acoustic signal is capable of reflecting where other types of signals cannot operate, either by drawbacks or where fluid is displaced. A mathematical model is presented in this paper described in state space as a basic acoustic sensor description. The objective is to adjust the parameters al

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17

Harring, Jeffrey R. "A Nonlinear Mixed Effects Model for Latent Variables." Journal of Educational and Behavioral Statistics 34, no. 3 (2009): 293–318. http://dx.doi.org/10.3102/1076998609332750.

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The nonlinear mixed effects model for continuous repeated measures data has become an increasingly popular and versatile tool for investigating nonlinear longitudinal change in observed variables. In practice, for each individual subject, multiple measurements are obtained on a single response variable over time or condition. This structure can be adapted to examine the change in latent variables rather than modeling change in manifest variables. This article considers a nonlinear mixed effects model for describing nonlinear change of a latent construct over time, where the latent construct of

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18

El Ouafi, A., R. Belanger, and M. Guillot. "A Comprehensive Approach for Resistance Spot Welding Quality Estimation Using Dynamic Resistance Based Model." Applied Mechanics and Materials 249-250 (December 2012): 732–38. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.732.

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On-line quality assessment becomes one of the most critical requirements for improving the efficiency of automatic resistance spot welding (RSW) processes. Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. Besides the usual welding parameters, various measured variables have been considered for quality estimation in RSW. Among these variables, dynamic resistance gives a relative clear picture of the welding nugget formation and presents a significant correlation withseveral RSW quality indicators. This paper presents a structured

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19

Huang, Biwei, Kun Zhang, Mingming Gong, and Clark Glymour. "Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 10153–61. http://dx.doi.org/10.1609/aaai.v34i06.6575.

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A number of approaches to causal discovery assume that there are no hidden confounders and are designed to learn a fixed causal model from a single data set. Over the last decade, with closer cooperation across laboratories, we are able to accumulate more variables and data for analysis, while each lab may only measure a subset of them, due to technical constraints or to save time and cost. This raises a question of how to handle causal discovery from multiple data sets with non-identical variable sets, and at the same time, it would be interesting to see how more recorded variables can help t

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Ping, Xianyao, Shuo Cheng, Wei Yue, Yongchang Du, Xiangyu Wang, and Liang Li. "Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 14 (2020): 3224–38. http://dx.doi.org/10.1177/0954407020941410.

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Vehicle dynamic states and parameters, such as the tyre–road friction coefficient and body’s sideslip angle especially, are crucial for vehicle dynamics control with close-loop feedback laws. Autonomous vehicles also have strict demands on real-time knowledge of those information to make reliable decisions. With consideration of the cost saving, some estimation methods employing high-resolution vision and position devices are not for the production vehicles. Meanwhile, the bad adaptability of traditional Kalman filters to variable system structure restricts their practical applications. This p

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Li, Zhuokai, Hai Liu, and Wanzhu Tu. "Model selection in multivariate semiparametric regression." Statistical Methods in Medical Research 27, no. 10 (2017): 3026–38. http://dx.doi.org/10.1177/0962280217690769.

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Variable selection in semiparametric mixed models for longitudinal data remains a challenge, especially in the presence of multiple correlated outcomes. In this paper, we propose a model selection procedure that simultaneously selects fixed and random effects using a maximum penalized likelihood method with the adaptive least absolute shrinkage and selection operator penalty. Through random effects selection, we determine the correlation structure among multiple outcomes and therefore address whether a joint model is necessary. Additionally, we include a bivariate nonparametric component, as a

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Leroux, Audrey J., and S. Natasha Beretvas. "Estimating a Three-Level Latent Variable Regression Model With Cross-Classified Multiple Membership Data." Methodology 14, no. 1 (2018): 30–44. http://dx.doi.org/10.1027/1614-2241/a000143.

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Abstract. The current study proposed a new model, termed the cross-classified multiple membership latent variable regression (CCMM-LVR) model that provides an extension to the three-level latent variable regression (HM3-LVR) model that can be used with cross-classified multiple membership data, for example, in the presence of student mobility across schools. The HM3-LVR model is beneficial for testing more flexible hypotheses about growth trajectory parameters and handles pure clustering of participants within higher-level (level-3) units. However, the HM3-LVR model involves the assumption tha

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23

Jewsbury, Paul A., and Peter W. van Rijn. "IRT and MIRT Models for Item Parameter Estimation With Multidimensional Multistage Tests." Journal of Educational and Behavioral Statistics 45, no. 4 (2019): 383–402. http://dx.doi.org/10.3102/1076998619881790.

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In large-scale educational assessment data consistent with a simple-structure multidimensional item response theory (MIRT) model, where every item measures only one latent variable, separate unidimensional item response theory (UIRT) models for each latent variable are often calibrated for practical reasons. While this approach can be valid for data from a linear test, unacceptable item parameter estimates are obtained when data arise from a multistage test (MST). We explore this situation from a missing data perspective and show mathematically that MST data will be problematic for calibrating

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24

Ser, Gazel, and Cafer Tayyar Bati. "Eksik Veri Analizinde Çoklu Atama Yönteminin Değerlendirilmesi: Hayvancılıkta Tekrarlı Ölçüm Verisi Üzerine Bir Uygulama." Turkish Journal of Agriculture - Food Science and Technology 3, no. 12 (2015): 926. http://dx.doi.org/10.24925/turjaf.v3i12.926-932.511.

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The purpose of this study was to evaluate the performance of multiple imputation method in case that missing observation structure is at random and completely at random from the approach of general linear mixed model. The application data of study was consisted of a total 77 heads of Norduz ram lambs at 7 months of age. After slaughtering, pH values measured at five different time points were determined as dependent variable. In addition, hot carcass weight, muscle glycogen level and fasting durations were included as independent variables in the model. In the dependent variable without missin

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25

Hu, Kaiyu, Wenhao Li, and Zian Cheng. "Fuzzy adaptive fault diagnosis and compensation for variable structure hypersonic vehicle with multiple faults." PLOS ONE 16, no. 8 (2021): e0256200. http://dx.doi.org/10.1371/journal.pone.0256200.

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Based on the type-II fuzzy logic, this paper proposes a robust adaptive fault diagnosis and fault-tolerant control (FTC) scheme for multisensor faults in the variable structure hypersonic vehicles with parameter uncertainties. Type-II fuzzy method approximates the original models while eliminating the parameter uncertainties. Hence the sensor faults are detected and isolated by the multiple output residuals and thresholds considering nonlinear approximation errors and disturbance. Based on the fuzzy adaptive augmented observer, the faults and disturbance are all estimated accurately by an impr

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26

Kim, Tae Han, Kyung Rok Moon, and Taek Lyul Song. "Variable-structured interacting multiple model algorithm for the ballistic coefficient estimation of a re-entry ballistic target." International Journal of Control, Automation and Systems 11, no. 6 (2013): 1204–13. http://dx.doi.org/10.1007/s12555-013-0003-2.

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27

Hoogendoorn, Serge P., and Piet H. L. Bovy. "Modeling Multiple User-Class Traffic." Transportation Research Record: Journal of the Transportation Research Board 1644, no. 1 (1998): 57–69. http://dx.doi.org/10.3141/1644-07.

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In contemporary traffic flow theory, the distinction between user classes is rarely made. However, we envisage that the accuracy and the descriptive power of the macroscopic traffic flow models can be improved significantly by separating user classes and their specific flow characteristics. As a consequence, the possibility of improved estimation and prediction of traffic flow conditions becomes available. Additionally, the availability of a realistic multiple user-class traffic flow model enables the automated generation of user-dedicated traffic control policies by means of mathematical opti

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28

Greenberg, Joshua A., Scott C. Matulich, and Ron C. Mittelhammer. "A System-of-Equations Approach to Modeling Age-Structured Fish Populations: The Case of Alaskan Red King Crab, Paralithodes camtschaticus." Canadian Journal of Fisheries and Aquatic Sciences 48, no. 9 (1991): 1613–22. http://dx.doi.org/10.1139/f91-191.

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This paper presents a simultaneous system-of-equations approach to modeling age-structured populations using trawl survey age/size frequency data. The analysis builds upon a Ricker spawner–recruit structure and provides a cohort-based estimation method that retains the underlying dynamic properties of a delay-difference model. The framework shares a common spawner–recruit function across age-class equations. This exploits the commonality among cohort members and serves as an instrumental variable, lessening the effect of measurement errors in estimation. The dynamic features of the underlying

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29

Xie, Kun, Shu Qiao, Chuan Fu, and Jun Sheng Qi. "Estimation of Solid Vapor Pressure of PCDD/Fs by Three-Dimensional Holographic Vector of Atomic Interaction Field." Advanced Materials Research 356-360 (October 2011): 89–94. http://dx.doi.org/10.4028/www.scientific.net/amr.356-360.89.

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Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) are a group of important persistent organic pollutants. In the present study, three-dimensional holographic vector of atomic interaction field (3D-HoVAIF) is used to describe the chemical structures of PCDD/Fs. After variable screening by stepwise multiple regression (SMR) technique, the liner relationships between solid vapor pressure (logPS) of PCDD/Fs and 3D-HoVAIF descriptors by partial least square (PLS) regression.The result shows that the 3D-HoVAIF descriptors can be well used to express the quantitative structure-property re

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Maier, Kimberly S. "A Rasch Hierarchical Measurement Model." Journal of Educational and Behavioral Statistics 26, no. 3 (2001): 307–30. http://dx.doi.org/10.3102/10769986026003307.

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In this article, a hierarchical measurement model is developed that enables researchers to measure a latent trait variable and model the error variance corresponding to multiple levels. The Rasch hierarchical measurement model (HMM) results when a Rasch IRT model and a one-way ANOVA with random effects are combined ( Bryk & Raudenbush, 1992 ; Goldstein, 1987 ; Rasch, 1960 ). This model is appropriate for modeling dichotomous response strings nested within a contextual level. Examples of this type of structure include responses from students nested within schools and multiple response strin

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Thomson, K. J., and K. G. Willis. "Errors in Variables: A Problem in Regression and its Solution." Environment and Planning A: Economy and Space 18, no. 5 (1986): 687–93. http://dx.doi.org/10.1068/a180687.

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It is not uncommon in socioeconomic analysis to measure variables with error, as in a 10% census. The estimation of linear regression coefficients using such ‘errors-in-variables’ models requires modification of the usual ordinary least squares techniques. The underlying theory both for simple and for multiple regression models is explained, and followed up with a numerical example based on a structure plan model of car ownership.

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32

Woodget, Amy S., James T. Dietrich, and Robin T. Wilson. "Quantifying Below-Water Fluvial Geomorphic Change: The Implications of Refraction Correction, Water Surface Elevations, and Spatially Variable Error." Remote Sensing 11, no. 20 (2019): 2415. http://dx.doi.org/10.3390/rs11202415.

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Much of the geomorphic work of rivers occurs underwater. As a result, high resolutionquantification of geomorphic change in these submerged areas is important. Currently, to quantify thischange, multiple methods are required to get high resolution data for both the exposed and submergedareas. Remote sensing methods are often limited to the exposed areas due to the challenges imposedby the water, and those remote sensing methods for below the water surface require the collection ofextensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimesprohibitive in dicul

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33

Feng, Wei Yuan, Xue Feng Dong, Xu Dong Yu, Shuang Wen Wang, and Zhong Jing Liu. "Impact Factor Estimation on Power Quality to Electric Power Market." Applied Mechanics and Materials 448-453 (October 2013): 2667–72. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2667.

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Power quality and power marketing are comprehensive concept contains multiple indexes. This paper builds the index system of the power quality promoting the power market development. And the structural equation modeling (SEM) be used to calculate the impact factor of power quality to marketing at different times and areas. This paper firstly puts power quality and marketing which are regarded as latent variables. And select the power quality index that includes the amplitude, frequency, spectrum, modulation, the notch depth and marketing index that includes market share, electricity consumptio

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34

El Ouafi, Abderrazak, R. Belanger, and Michel Guillot. "Dynamic Resistance Based Model for On-Line Resistance Spot Welding Quality Assessment." Materials Science Forum 706-709 (January 2012): 2925–30. http://dx.doi.org/10.4028/www.scientific.net/msf.706-709.2925.

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On-line quality assessment becomes one of the most critical requirements for improving the efficiency of automatic resistance spot welding (RSW) processes. Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. Besides the usual welding parameters, various measured variables have been considered for quality estimation in RSW. Among these variables, dynamic resistance (DR) gives a relative clear picture of the welding nugget formation and presents a significant correlation with the RSW quality indicators (QI). This paper presents a str

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35

Ghoul, Yamna, Kaouther Ibn Taarit, and Moufida Ksouri. "Identification of continuous-time hybrid ‘Box-Jenkins’ systems having multiple unknown time delays." Transactions of the Institute of Measurement and Control 41, no. 2 (2018): 366–77. http://dx.doi.org/10.1177/0142331218763011.

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For many years, various methods for the identification of parameters of continuous-time models have been available and implemented in widely. However, most methods apply models where the output are contaminated by a white noise or without noise in some others cases, which are unrealistic in most practical applications owing to their associated noise structure. Some other methods neglect the presence of time delays. Then it can be shown that the estimates are not statistically efficient. To cope with this issue, this paper deals with the identification of multi-input single-output continuous-ti

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36

Lee, Kyu Ha, Brent A. Coull, Anna-Barbara Moscicki, Bruce J. Paster, and Jacqueline R. Starr. "Bayesian variable selection for multivariate zero-inflated models: Application to microbiome count data." Biostatistics 21, no. 3 (2018): 499–517. http://dx.doi.org/10.1093/biostatistics/kxy067.

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Summary Microorganisms play critical roles in human health and disease. They live in diverse communities in which they interact synergistically or antagonistically. Thus for estimating microbial associations with clinical covariates, such as treatment effects, joint (multivariate) statistical models are preferred. Multivariate models allow one to estimate and exploit complex interdependencies among multiple taxa, yielding more powerful tests of exposure or treatment effects than application of taxon-specific univariate analyses. Analysis of microbial count data also requires special attention

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37

Hatefi, Armin, and Mohammad Jafari Jozani. "An improved procedure for estimation of malignant breast cancer prevalence using partially rank ordered set samples with multiple concomitants." Statistical Methods in Medical Research 26, no. 6 (2015): 2552–66. http://dx.doi.org/10.1177/0962280215601458.

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Rank-based sampling designs are widely used in situations where measuring the variable of interest is costly but a small number of sampling units (set) can be easily ranked prior to taking the final measurements on them and this can be done at little cost. When the variable of interest is binary, a common approach for ranking the sampling units is to estimate the probabilities of success through a logistic regression model. However, this requires training samples for model fitting. Also, in this approach once a sampling unit has been measured, the extra rank information obtained in the ranking

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38

Park, Jong Woong, Sung Han Sim, Hyung Jo Jung, and Billie F. Spencer. "Dynamic Displacement Estimation from Acceleration Measurements Using a Wireless Smart Sensor." Key Engineering Materials 558 (June 2013): 227–34. http://dx.doi.org/10.4028/www.scientific.net/kem.558.227.

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A displacement measurement provides useful information for structural health monitoring (SHM) as it is directly related to stiffness of the structure. Most existing methods of direct measurement such as the Laser Doppler Vibrometer (LDV) and the Liner Variable Differential Transformer (LVDT) are known to have accurate performance but have difficulties particularly in the use of large-scale civil structures as the methods rely on fixed reference points. Alternatively, indirect methods have been developed and widely used methods are Global Positioning System (GPS), vision-based displacement meas

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Vautier, Stéphane. "Measuring Change With Multiple Visual Analog Scales." European Journal of Psychological Assessment 27, no. 2 (2011): 111–20. http://dx.doi.org/10.1027/1015-5759/a000051.

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Although the visual analog scale (VAS) may be useful for measuring change on subjective and potentially transient phenomena, there is concern about the reliability and construct validity of the associated measurement variables. The present study reports evidence for tau-equivalence of change scores associated with VASs designed for assessing tense arousal with synonymous indicators. This psychometric property allows an estimation of the true-score structure of the cross-sectional measurement variables in a longitudinal SEM model, including method effects. Findings suggest that VASs associated

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Ahn, Kuk-Hyun. "Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas." Hydrology and Earth System Sciences 25, no. 8 (2021): 4319–33. http://dx.doi.org/10.5194/hess-25-4319-2021.

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Abstract. Reliable estimates of missing streamflow values are relevant for water resource planning and management. This study proposes a multiple-dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high-dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a va

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41

Zhang, Linjing, Zhenfeng Shao, Jianchen Liu, and Qimin Cheng. "Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data." Remote Sensing 11, no. 12 (2019): 1459. http://dx.doi.org/10.3390/rs11121459.

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Estimation of forest aboveground biomass (AGB) is crucial for various technical and scientific applications, ranging from regional carbon and bioenergy policies to sustainable forest management. However, passive optical remote sensing, which is the most widely used remote sensing data for retrieving vegetation parameters, is constrained by spectral saturation problems and cloud cover. On the other hand, LiDAR data, which have been extensively used to estimate forest structure attributes, cannot provide sufficient spectral information of vegetation canopies. Thus, this study aimed to develop a

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42

Lee, JungJun, SungHwan Kim, Jae-Hwan Jhong, and Ja-Yong Koo. "Variable Selection and Joint Estimation of Mean and Covariance Models with an Application to eQTL Data." Computational and Mathematical Methods in Medicine 2018 (June 25, 2018): 1–13. http://dx.doi.org/10.1155/2018/4626307.

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In genomic data analysis, it is commonplace that underlying regulatory relationship over multiple genes is hardly ascertained due to unknown genetic complexity and epigenetic regulations. In this paper, we consider a joint mean and constant covariance model (JMCCM) that elucidates conditional dependent structures of genes with controlling for potential genotype perturbations. To this end, the modified Cholesky decomposition is utilized to parametrize entries of a precision matrix. The JMCCM maximizes the likelihood function to estimate parameters involved in the model. We also develop a variab

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43

Ye, Fan, and Yong Wang. "Performance Evaluation of Various Missing Data Treatments in Crash Severity Modeling." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (2018): 149–59. http://dx.doi.org/10.1177/0361198118798485.

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Data quality, including record inaccuracy and missingness (incompletely recorded crashes and crash underreporting), has always been of concern in crash data analysis. Limited efforts have been made to handle some specific aspects of crash data quality problems, such as using weights in estimation to take care of unreported crash data and applying multiple imputation (MI) to fill in missing information of drivers’ status of attention before crashes. Yet, there lacks a general investigation of the performance of different statistical methods to handle missing crash data. This paper is intended t

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Cao, Jing, Md Hadiuzzaman, Tony Z. Qiu, and Dawei Hu. "Real-time queue estimation model development for uninterrupted freeway flow based on shockwave analysis." Canadian Journal of Civil Engineering 42, no. 3 (2015): 153–63. http://dx.doi.org/10.1139/cjce-2014-0218.

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In this study, the authors developed a time-space discrete macroscopic model based on the shockwave theory for real-time queue estimation in uninterrupted freeway flow, using fixed-location loop detector data. After investigating the queue characteristics both at an active bottleneck and within a variable speed limit control case, the proposed model was applied to these two cases on Whitemud Drive, a major freeway corridor in Edmonton, Alberta, Canada. Modified Highway Capacity Manual–based methods were used to determine queue density in uninterrupted freeway flow. The effect of time interval

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Kong, Liulin, Heng Li, Bo Zhang, and Hanbin Luo. "Estimation of High Structural Reliability Involving Nonlinear Dependencies Based on Linear Correlations." Advances in Civil Engineering 2021 (August 24, 2021): 1–18. http://dx.doi.org/10.1155/2021/8836330.

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Stochastic nonlinear dependencies have been reported extensively between different uncertain parameters or in their time or spatial variance. However, the description of dependency is commonly not provided except a linear correlation. The structural reliability incorporating nonlinear dependencies thus needs to be addressed based on the linear correlations. This paper first demonstrates the capture of nonlinear dependency by fitting various bivariate non-Gaussian copulas to limited data samples of structural material properties. The vine copula model is used to enable a flexible modeling of mu

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Zscheischler, Jakob, Erich M. Fischer, and Stefan Lange. "The effect of univariate bias adjustment on multivariate hazard estimates." Earth System Dynamics 10, no. 1 (2019): 31–43. http://dx.doi.org/10.5194/esd-10-31-2019.

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Abstract. Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depe

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Duncan, Terry E., Roy Oman, and Susan C. Duncan. "Modeling Incomplete Data in Exercise Behavior Research Using Structural Equation Methodology." Journal of Sport and Exercise Psychology 16, no. 2 (1994): 187–205. http://dx.doi.org/10.1123/jsep.16.2.187.

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Exercise behavior research typically suffers from attrition and other forms of missing data. In studies that suffer from this common malady, several researchers have demonstrated that correct maximum likelihood estimation with missing data can be obtained under mild assumptions concerning the missing data mechanism. Model estimation with distinct missing data patterns can, in many cases, be carried out utilizing existing structural equation modeling software that allow for the simultaneous analysis of mean and covariance structures for multiple groups. Findings are discussed in relation to the

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Romana, Manuel G., and Gemma Lépez. "Estimation of Percentage of Delayed Vehicles Based on Traffic Variables for Rural Highways." Transportation Research Record: Journal of the Transportation Research Board 1646, no. 1 (1998): 29–36. http://dx.doi.org/10.3141/1646-04.

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According to the Highway Capacity Manual (HCM), it is possible to quantify level of service for rural highways using a basic parameter: percent time delay. Since this variable cannot be measured, it is proposed in the HCM that the percentage of delayed vehicles (% DV) be used as a surrogate measure. The manual gives no means of predicting or estimating % DV other than a curve relating total % DV to total hourly volume. However, it is worth noting that the methodology proposed in the manual does not use this curve. In any case, there has been to this date no proposal as to the estimation of % D

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Colin, J., and R. Faivre. "Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe basin in China." Hydrology and Earth System Sciences 14, no. 12 (2010): 2661–69. http://dx.doi.org/10.5194/hess-14-2661-2010.

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Abstract. Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest in the estimation of the surface turbulent flux parameterisation from passive remote sensing leads to an increasing development of models, and the common use of simple semi-empirical formulations to estimate surface roughness. Over complex surface land cover, these approaches would benefit from the combined use of passive remote sensing and land surface structure measurements from Light Detection And Ranging (LIDAR) techniques. Following early studi

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Lee, Changsuk, Kyunghwa Lee, Sangmin Kim, Jinhyeok Yu, Seungtaek Jeong, and Jongmin Yeom. "Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning." Remote Sensing 13, no. 11 (2021): 2121. http://dx.doi.org/10.3390/rs13112121.

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This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was als

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