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Journal articles on the topic 'Gauss Markov model with constraints'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Larocca, Roger. "Reconciling Conflicting Gauss-Markov Conditions in the Classical Linear Regression Model." Political Analysis 13, no. 2 (2005): 188–207. http://dx.doi.org/10.1093/pan/mpi011.

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This article reconciles conflicting accounts of Gauss-Markov conditions, which specify when ordinary least squares (OLS) estimators are also best linear unbiased (BLU) estimators. We show that exogeneity constraints that are commonly assumed in econometric treatments of the Gauss-Markov theorem are unnecessary for OLS estimates of the classical linear regression model to be BLU. We also generalize a set of necessary and sufficient conditions first established by McElroy (1967, Journal of the American Statistical Association 62:1302–1304), but not yet generally recognized in the econometric lit

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2

Schaffrin, Burkhard, and Gerhard Navratil. "On Reproducing Linear Estimators within the Gauss–Markov Model with Stochastic Constraints." Communications in Statistics - Theory and Methods 41, no. 13-14 (2012): 2570–87. http://dx.doi.org/10.1080/03610926.2011.631073.

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3

Sluis, B., and C. Toth. "2-DIMENSIONAL GEOMETRIC ANALYSIS OF A SIMPLE FREE NETWORK." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2021 (June 17, 2021): 147–52. http://dx.doi.org/10.5194/isprs-annals-v-4-2021-147-2021.

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Abstract. This paper attempts to quantify geometric considerations in observations and observe trends in solutions to free network solutions. The method of investigation will be utilizing 2D observations to determine how each measurement affects the overall solution and the location of the observations relative to the other nodes. A local reference system will be determined using the Gauss-Markov model with constraints by fixing the largest range observation to the y-axis to give a relative orientation. Further solutions will be calculated by fixing additional points to generate multiple least

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4

Chen, Jinsong, Andreas Kemna, and Susan S. Hubbard. "A comparison between Gauss-Newton and Markov-chain Monte Carlo–based methods for inverting spectral induced-polarization data for Cole-Cole parameters." GEOPHYSICS 73, no. 6 (2008): F247—F259. http://dx.doi.org/10.1190/1.2976115.

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We have developed a Bayesian model to invert spectral induced-polarization (SIP) data for Cole-Cole parameters using Markov-chain Monte Carlo (MCMC) sampling methods. We compared the performance of the MCMC-based stochastic method with an iterative Gauss-Newton-based deterministic method for Cole-Cole parameter estimation through inversion of synthetic and laboratory SIP data. The Gauss-Newton-based method can provide an optimal solution for given objective functions under constraints, but the obtained optimal solution generally depends on the choice of initial values and the estimated uncerta

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5

Prószyński, Witold. "Investigating the links of internal and external reliability with the system conditionality in Gauss-Markov models with uncorrelated observations." Geodesy and Cartography 62, no. 2 (2013): 157–81. http://dx.doi.org/10.2478/geocart-2013-0010.

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Abstract The relationship between internal response-based reliability and conditionality is investigated for Gauss-Markov (GM) models with uncorrelated observations. The models with design matrices of full rank and of incomplete rank are taken into consideration. The formulas based on the Singular Value Decomposition (SVD) of the design matrix are derived which clearly indicate that the investigated concepts are independent of each other. The methods are presented of constructing for a given design matrix the matrices equivalent with respect to internal response-based reliability as well as th

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6

Dehbi, Y., J. H. Haunert, and L. Plümer. "STOCHASTIC AND GEOMETRIC REASONING FOR INDOOR BUILDING MODELS WITH ELECTRIC INSTALLATIONS – BRIDGING THE GAP BETWEEN GIS AND BIM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (October 23, 2017): 33–39. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-33-2017.

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3D city and building models according to CityGML encode the geometry, represent the structure and model semantically relevant building parts such as doors, windows and balconies. Building information models support the building design, construction and the facility management. In contrast to CityGML, they include also objects which cannot be observed from the outside. The three dimensional indoor models characterize a missing link between both worlds. Their derivation, however, is expensive. The semantic automatic interpretation of 3D point clouds of indoor environments is a methodically deman

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7

Werner, H. J., and C. Yapar. "On inequality constrained generalized least squares selections in the general possibly singular Gauss-Markov model: A projector theoretical approach." Linear Algebra and its Applications 237-238 (April 1996): 359–93. http://dx.doi.org/10.1016/0024-3795(94)00357-2.

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8

Omidalizarandi, Mohammad, Boris Kargoll, Jens-André Paffenholz, and Ingo Neumann. "Robust external calibration of terrestrial laser scanner and digital camera for structural monitoring." Journal of Applied Geodesy 13, no. 2 (2019): 105–34. http://dx.doi.org/10.1515/jag-2018-0038.

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Abstract In the last two decades, the integration of a terrestrial laser scanner (TLS) and digital photogrammetry, besides other sensors integration, has received considerable attention for deformation monitoring of natural or man-made structures. Typically, a TLS is used for an area-based deformation analysis. A high-resolution digital camera may be attached on top of the TLS to increase the accuracy and completeness of deformation analysis by optimally combining points or line features extracted both from three-dimensional (3D) point clouds and captured images at different epochs of time. Fo

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9

Alenazi, Mohammed J. F., Shatha O. Abbas, Saleh Almowuena, and Maazen Alsabaan. "RSSGM: Recurrent Self-Similar Gauss–Markov Mobility Model." Electronics 9, no. 12 (2020): 2089. http://dx.doi.org/10.3390/electronics9122089.

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Understanding node mobility is critical for the proper simulation of mobile devices in a wireless network. However, current mobility models often do not reflect the realistic movements of users within their environments. They also do not provide the freedom to adjust their degrees of randomness or adequately mimic human movements by injecting possible crossing points and adding recurrent patterns. In this paper, we propose the recurrent self-similar Gauss–Markov mobility (RSSGM) model, a novel mobility model that is suitable for applications in which nodes exhibit recurrent visits to selected

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10

Jun Zhang. "Parameter reduction for the compound Gauss-Markov model." IEEE Transactions on Image Processing 4, no. 3 (1995): 382–86. http://dx.doi.org/10.1109/83.366485.

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11

Zha, Hong Yuan, and Per Christian Hansen. "Regularization and the general Gauss-Markov linear model." Mathematics of Computation 55, no. 192 (1990): 613. http://dx.doi.org/10.1090/s0025-5718-1990-1035946-9.

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12

Qing-ming, Gui, Li Guo-zhong, and Ou Ji-kuen. "ROBUST-TYPE BIASED ESTIMATION IN GAUSS-MARKOV MODEL." Survey Review 38, no. 298 (2005): 299–307. http://dx.doi.org/10.1179/sre.2005.38.298.299.

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13

Liu, Jinshan. "Linear sufficiency in the general Gauss-Markov model." Applied Mathematics-A Journal of Chinese Universities 17, no. 1 (2002): 85–92. http://dx.doi.org/10.1007/s11766-002-0030-x.

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14

Taillefumier, Thibaud, and Jonathan Touboul. "Multiresolution Hilbert Approach to Multidimensional Gauss-Markov Processes." International Journal of Stochastic Analysis 2011 (2011): 1–89. http://dx.doi.org/10.1155/2011/247329.

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The study of the multidimensional stochastic processes involves complex computations in intricate functional spaces. In particular, the diffusion processes, which include the practically important Gauss-Markov processes, are ordinarily defined through the theory of stochastic integration. Here, inspired by the Lévy-Ciesielski construction of the Wiener process, we propose an alternative representation of multidimensional Gauss-Markov processes as expansions on well-chosen Schauder bases, with independent random coefficients of normal law with zero mean and unit variance. We thereby offer a nat

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15

Caliński, Tadeusz. "On the General Gauss-Markov Model for Experiments in Block Designs." Biometrical Letters 49, no. 1 (2012): 1–36. http://dx.doi.org/10.2478/bile-2013-0001.

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Summary The main estimation and hypothesis testing results related to the Gauss- Markov model, in its general form, are recalled and the application of these results to the analysis of experiments in block designs is considered. Special attention is given to the randomization-derived model for a general block design, and for a proper block design in particular. The question whether the randomization-derived model can be considered as a particular general Gauss-Markov model is discussed. It is indicated that the former, as a mixed model, is in fact an extension of the general Gauss-Markov model

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16

Ižvoltová, Jana, and Peter Pisca. "Combinatorial Algorithm In Linear Model." MATEC Web of Conferences 196 (2018): 03017. http://dx.doi.org/10.1051/matecconf/201819603017.

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Gauss-jacobi combinatorial algorithm is an alternative approach to traditional iterative numerical methods, which is primary oriented for parameter estimation in nonlinear models. The combinatorial algorithm is often exploited for outlier diagnosis in nonlinear models, where the other parameter estimation methods lose their efficiency. The paper describes comparison of both of gauss-jacobi combinatorial and gauss-markov models executed on parameter estimation process of levelling network for the reason to find the efficiency of combinatorial algorithm in simply linear model.

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17

Ibarrola, P., and A. Pérez-Palomares. "Linear completeness in a continuous time Gauss–Markov model." Statistics & Probability Letters 69, no. 2 (2004): 143–49. http://dx.doi.org/10.1016/j.spl.2004.06.014.

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18

Schaffrin, Burkhard. "Softly unbiased estimation, part 1: The Gauss-Markov model." Linear Algebra and its Applications 289, no. 1-3 (1999): 285–96. http://dx.doi.org/10.1016/s0024-3795(98)10208-2.

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19

Gleich, D., and M. Datcu. "Gauss–Markov Model for Wavelet-Based SAR Image Despeckling." IEEE Signal Processing Letters 13, no. 6 (2006): 365–68. http://dx.doi.org/10.1109/lsp.2006.871712.

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20

Baksalary, Jerzy K., and Augustyn Markiewicz. "Admissible linear estimators in the general Gauss-Markov model." Journal of Statistical Planning and Inference 19, no. 3 (1988): 349–59. http://dx.doi.org/10.1016/0378-3758(88)90042-0.

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21

Baksalary, Jerzy K., and PawełR Pordzik. "Implied linear restrictions in the general Gauss-Markov model." Journal of Statistical Planning and Inference 30, no. 2 (1992): 237–48. http://dx.doi.org/10.1016/0378-3758(92)90084-6.

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22

Fuglsig, Andreas Jonas, and Jan Østergaard. "Zero-Delay Multiple Descriptions of Stationary Scalar Gauss-Markov Sources." Entropy 21, no. 12 (2019): 1185. http://dx.doi.org/10.3390/e21121185.

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In this paper, we introduce the zero-delay multiple-description problem, where an encoder constructs two descriptions and the decoders receive a subset of these descriptions. The encoder and decoders are causal and operate under the restriction of zero delay, which implies that at each time instance, the encoder must generate codewords that can be decoded by the decoders using only the current and past codewords. For the case of discrete-time stationary scalar Gauss—Markov sources and quadratic distortion constraints, we present information-theoretic lower bounds on the average sum-rate in ter

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23

Benetti, Micol, Simony Santos da Costa, Salvatore Capozziello, Jailson S. Alcaniz, and Mariafelicia De Laurentis. "Observational constraints on Gauss–Bonnet cosmology." International Journal of Modern Physics D 27, no. 08 (2018): 1850084. http://dx.doi.org/10.1142/s0218271818500840.

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We analyze a fully geometric approach to dark energy in the framework of [Formula: see text] theories of gravity, where [Formula: see text] is the Ricci curvature scalar and [Formula: see text] is the Gauss–Bonnet topological invariant. The latter invariant naturally exhausts, together with [Formula: see text], the whole curvature content related to curvature invariants coming from the Riemann tensor. In particular, we study a class of [Formula: see text] models with power law solutions and find that, depending on the value of the geometrical parameter, a shift in the anisotropy peaks position

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24

Tian, Yongge, M. Beisiegel, E. Dagenais, and C. Haines. "On the natural restrictions in the singular Gauss–Markov model." Statistical Papers 49, no. 3 (2006): 553–64. http://dx.doi.org/10.1007/s00362-006-0032-5.

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25

Torigoe, Norio, and Katsumi Ujiie. "On the Restricted Liu Estimator in the Gauss–Markov Model." Communications in Statistics - Theory and Methods 35, no. 9 (2006): 1713–22. http://dx.doi.org/10.1080/03610920600683754.

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26

Hekimoğlu, Şerif. "Reliabilities of χ2- and F Tests in Gauss-Markov Model". Journal of Surveying Engineering 125, № 3 (1999): 109–35. http://dx.doi.org/10.1061/(asce)0733-9453(1999)125:3(109).

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27

Groß, Jürgen. "The general Gauss-Markov model with possibly singular dispersion matrix." Statistical Papers 45, no. 3 (2004): 311–36. http://dx.doi.org/10.1007/bf02777575.

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28

Ascione, Giacomo, and Bruno Toaldo. "A Semi-Markov Leaky Integrate-and-Fire Model." Mathematics 7, no. 11 (2019): 1022. http://dx.doi.org/10.3390/math7111022.

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In this paper, a Leaky Integrate-and-Fire (LIF) model for the membrane potential of a neuron is considered, in case the potential process is a semi-Markov process. Semi-Markov property is obtained here by means of the time-change of a Gauss-Markov process. This model has some merits, including heavy-tailed distribution of the waiting times between spikes. This and other properties of the process, such as the mean, variance and autocovariance, are discussed.

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29

ZHANG, Heng-Yang. "A Smooth Gauss-Semi-Markov Mobility Model for Wireless Sensor Networks." Journal of Software 19, no. 7 (2008): 1707–15. http://dx.doi.org/10.3724/sp.j.1001.2008.01707.

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30

Groß, Jürgen. "On a class of estimators in the general gauss-markov model." Communications in Statistics - Theory and Methods 25, no. 2 (1996): 381–88. http://dx.doi.org/10.1080/03610929608831701.

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31

Oktaba, Wiktor. "The General Multivariate Gauss-Markov Model of the Incomplete Block Design." Australian New Zealand Journal of Statistics 45, no. 2 (2003): 195–205. http://dx.doi.org/10.1111/1467-842x.00275.

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32

Cheng, Ke‐Sheng, Irene Hueter, En‐Ching Hsu, and Hui‐Chung Yeh. "A SCALE‐INVARIANT GAUSS‐MARKOV MODEL FOR DESIGN STORM HYETOGRAPHS 1." JAWRA Journal of the American Water Resources Association 37, no. 3 (2001): 723–35. http://dx.doi.org/10.1111/j.1752-1688.2001.tb05506.x.

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33

Goldman, Geoffrey H. "Gauss-Markov model for scattering of acoustic signals from rough surfaces." Journal of the Acoustical Society of America 149, no. 4 (2021): A139. http://dx.doi.org/10.1121/10.0005336.

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34

Kala, Radoslaw, and Krzysztof Kłaczyńaski. "Recursive improvement of estimates in a Gauss-Markov model with linear restrictions." Canadian Journal of Statistics 16, no. 3 (1988): 301–5. http://dx.doi.org/10.2307/3314736.

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35

Kumar, R. Sathish, and S. Pariselvam. "Formative Impact of Gauss Markov Mobility Model on Data Availability in MANET." Asian Journal of Information Technology 11, no. 3 (2012): 108–16. http://dx.doi.org/10.3923/ajit.2012.108.116.

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36

Haupt, Harry, and Walter Oberhofer. "Best affine unbiased representations of the fully restricted general Gauss–Markov model." Journal of Multivariate Analysis 97, no. 3 (2006): 759–64. http://dx.doi.org/10.1016/j.jmva.2005.04.006.

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37

Ibarrola, P., and A. Pérez-Palomares. "Linear sufficiency and linear admissibility in a continuous time Gauss–Markov model." Journal of Multivariate Analysis 87, no. 2 (2003): 315–27. http://dx.doi.org/10.1016/s0047-259x(03)00128-3.

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38

Kornacki, Andrzej. "Stability of invariant linearly sufficient statistics in the general Gauss-Markov model." Applications of Mathematics 42, no. 1 (1997): 71–77. http://dx.doi.org/10.1023/a:1022244727376.

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39

Perdana, Doan, Rendy Munadi, and Robby C. Manurung. "Performance Evaluation of Gauss-Markov Mobility Model in Hybrid LTE-VANET Networks." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 2 (2017): 606. http://dx.doi.org/10.12928/telkomnika.v15i2.5913.

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40

Isotalo, Jarkko, and Simo Puntanen. "Linear Prediction Sufficiency for New Observations in the General Gauss–Markov Model." Communications in Statistics - Theory and Methods 35, no. 6 (2006): 1011–23. http://dx.doi.org/10.1080/03610920600672146.

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41

Liu, Yi, Danpu Liu, and Guangxin Yue. "BGMM: a body gauss-markov based mobility model for body area networks." Tsinghua Science and Technology 23, no. 3 (2018): 277–87. http://dx.doi.org/10.26599/tst.2018.9010005.

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42

Breipohl, A. M., F. N. Lee, D. Zhai, and R. Adapa. "Gauss-Markov Load Model for Application in Risk Evaluation and Production Simulation." IEEE Power Engineering Review 12, no. 11 (1992): 47. http://dx.doi.org/10.1109/mper.1992.161440.

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43

Rellier, G., X. Descombes, F. Falzon, and J. Zerubia. "Texture feature analysis using a gauss-Markov model in hyperspectral image classification." IEEE Transactions on Geoscience and Remote Sensing 42, no. 7 (2004): 1543–51. http://dx.doi.org/10.1109/tgrs.2004.830170.

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44

Moura, Márcio das Chagas, and Enrique López Droguett. "A continuous-time semi-markov bayesian belief network model for availability measure estimation of fault tolerant systems." Pesquisa Operacional 28, no. 2 (2008): 355–75. http://dx.doi.org/10.1590/s0101-74382008000200011.

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In this work it is proposed a model for the assessment of availability measure of fault tolerant systems based on the integration of continuous time semi-Markov processes and Bayesian belief networks. This integration results in a hybrid stochastic model that is able to represent the dynamic characteristics of a system as well as to deal with cause-effect relationships among external factors such as environmental and operational conditions. The hybrid model also allows for uncertainty propagation on the system availability. It is also proposed a numerical procedure for the solution of the stat

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45

Zhang, Bao-Xue, and Xian-Hai Zhu. "Gauss–Markov and weighted least-squares estimation under a general growth curve model." Linear Algebra and its Applications 321, no. 1-3 (2000): 387–98. http://dx.doi.org/10.1016/s0024-3795(00)00197-x.

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46

Breipohl, A. M., F. N. Lee, D. Zhai, and R. Adapa. "A Gauss-Markov load model for application in risk evaluation and production simulation." IEEE Transactions on Power Systems 7, no. 4 (1992): 1493–99. http://dx.doi.org/10.1109/59.207373.

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47

Baksalary, Jerzy K., and PawełR Pordzik. "Inverse-partitioned-matrix method for the general Gauss-Markov model with linear restrictions." Journal of Statistical Planning and Inference 23, no. 2 (1989): 133–43. http://dx.doi.org/10.1016/0378-3758(89)90084-0.

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48

Kornacki, Andrzej. "The linear sufficiency and invariant linear sufficiency in a multivariate Gauss-Markov model." Metrika 42, no. 1 (1995): 240–41. http://dx.doi.org/10.1007/bf01894308.

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49

Jing, Junfeng, Qi Li, Pengfei Li, Hongwei Zhang, and Lei Zhang. "Image Segmentation of Printed Fabrics with Hierarchical Improved Markov Random Field in the Wavelet Domain." Journal of Engineered Fibers and Fabrics 11, no. 3 (2016): 155892501601100. http://dx.doi.org/10.1177/155892501601100305.

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An improved MRF algorithm–hierarchical Gauss Markov Random Field model in the wavelet domain is presented for fabric image segmentation in this paper, which obtains the relation of inter-scale dependency from the feature field modeling and label field modeling. The Gauss-Markov random field modeling is usually adopted to feature field modeling. The label field modeling employs the inter-scale causal MRF model and the intra-scale non-causal MRF model. After that, parameter estimation is the essential section in the inter-scale, enhancing modeling capabilities of the pixels partial dependency. S

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50

Jin, Hao, Chunguang Duan, Yang Liu, and Pingping Lu. "Gauss mixture hidden Markov model to characterise and model discretionary lane-change behaviours for autonomous vehicles." IET Intelligent Transport Systems 14, no. 5 (2020): 401–11. http://dx.doi.org/10.1049/iet-its.2019.0446.

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