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Journal articles on the topic 'Generalized uncertainty models'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Buckles, Billy P., and Frederick E. Petry. "Uncertainty models in information and database systems." Journal of Information Science 11, no. 2 (August 1985): 77–87. http://dx.doi.org/10.1177/016555158501100204.

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Information systems have evolved to the point where it is desirable to capture the vagueness and uncertainty of data that occurs in actuality. Approaches have been taken using various fuzzy set concepts such as degree of membership, similarity relations and possibility distributions. This leads to the concept of generalized information systems which are typically char acterized by heterogeneous data representations, weakly typed data domains and the requirement for semantic knowledge during query interpretation. A generalized information system is more likely to have a direct representation for larger classes of information at the cost of more complex data management and query processing. In general the various fuzzy database approaches that have been developed are overviewed in the paper and characterized with respect to the concept of a generalized information system.

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2

Koyak, Robert A., T. J. Hastie, and R. J. Tibshirani. "Generalized Additive Models." Journal of the American Statistical Association 86, no. 416 (December 1991): 1140. http://dx.doi.org/10.2307/2290538.

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3

Thompson, W. A., P. McCullagh, J. A. Nelder, and Annette J. Dobson. "Generalized Linear Models." Journal of the American Statistical Association 80, no. 392 (December 1985): 1066. http://dx.doi.org/10.2307/2288581.

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4

Moon, Graham, T. J. Hastie, and R. J. Tibshirani. "Generalized Additive Models." Applied Statistics 41, no. 1 (1992): 219. http://dx.doi.org/10.2307/2347636.

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5

Pukelsheim, F. "Generalized linear models." Metrika 33, no. 1 (December 1986): 290. http://dx.doi.org/10.1007/bf01894758.

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6

Belfaqih, I. H., H. Maulana, and A. Sulaksono. "White dwarfs and generalized uncertainty principle." International Journal of Modern Physics D 30, no. 09 (May 24, 2021): 2150064. http://dx.doi.org/10.1142/s0218271821500644.

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This work is motivated by the sign problem in a logarithmic parameter of black hole entropy and the existing more massive white dwarfs than the Chandrasekhar mass limit. We examine the quadratic, linear, and linear–quadratic generalized uncertainty principle (GUP) models within the virtue of recent masses and radii of white dwarfs. We consider the modification generated by introducing the minimal length on the degenerate Fermi gas equation of state (EoS) and the hydrostatic equation. For the latter, we applied Verlinde’s proposal regarding entropic gravity to derive the quantum corrected Newtonian gravity, which is responsible for modifying the hydrostatic equation. Through the models’ chi-square analysis, we have found that the observation data favor the quadratic than linear GUP models without mass limit. However, for the quadratic–linear GUP model, we can obtain the positive value of the free parameter [Formula: see text] as well as we can get mass limit more massive than the Chandrasekhar limit. In the linear–quadratic GUP model, the formation of stable massive white dwarfs than the Chandrasekhar limit is possible only if both parameters are not equal.

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7

Hilbe, Joseph M. "Generalized Linear Models." American Statistician 48, no. 3 (August 1994): 255. http://dx.doi.org/10.2307/2684732.

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8

Hu, X. Joan. "Generalized Linear Models." American Statistician 57, no. 1 (February 2003): 67–68. http://dx.doi.org/10.1198/tas.2003.s212.

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9

Müller, Hans-Georg, and Ulrich Stadtmüller. "Generalized functional linear models." Annals of Statistics 33, no. 2 (April 2005): 774–805. http://dx.doi.org/10.1214/009053604000001156.

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10

Burridge, Jim, P. McCullagh, and J. A. Nelder. "Generalized Linear Models." Journal of the Royal Statistical Society. Series A (Statistics in Society) 154, no. 2 (1991): 361. http://dx.doi.org/10.2307/2983054.

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11

Hilbe, Joseph M. "Generalized Additive Models Software." American Statistician 47, no. 1 (February 1993): 59. http://dx.doi.org/10.2307/2684787.

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12

Cordeiro, Gauss M., and Marinho G. de Andrade. "Transformed generalized linear models." Journal of Statistical Planning and Inference 139, no. 9 (September 2009): 2970–87. http://dx.doi.org/10.1016/j.jspi.2009.02.002.

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13

Brillinger, David R. "[Generalized Additive Models]: Comment." Statistical Science 1, no. 3 (August 1986): 310–12. http://dx.doi.org/10.1214/ss/1177013605.

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14

Nelder, J. A. "[Generalized Additive Models]: Comment." Statistical Science 1, no. 3 (August 1986): 312. http://dx.doi.org/10.1214/ss/1177013606.

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15

McCullagh, Peter. "[Generalized Additive Models]: Comment." Statistical Science 1, no. 3 (August 1986): 314. http://dx.doi.org/10.1214/ss/1177013608.

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16

Dou, Liyu, and Ulrich K. Müller. "Generalized Local‐to‐Unity Models." Econometrica 89, no. 4 (2021): 1825–54. http://dx.doi.org/10.3982/ecta17944.

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We introduce a generalization of the popular local‐to‐unity model of time series persistence by allowing for p autoregressive (AR) roots and p − 1 moving average (MA) roots close to unity. This generalized local‐to‐unity model, GLTU( p), induces convergence of the suitably scaled time series to a continuous time Gaussian ARMA( p, p − 1) process on the unit interval. Our main theoretical result establishes the richness of this model class, in the sense that it can well approximate a large class of processes with stationary Gaussian limits that are not entirely distinct from the unit root benchmark. We show that Campbell and Yogo's (2006) popular inference method for predictive regressions fails to control size in the GLTU(2) model with empirically plausible parameter values, and we propose a limited‐information Bayesian framework for inference in the GLTU( p) model and apply it to quantify the uncertainty about the half‐life of deviations from purchasing power parity.

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17

Therneau, Terry M., P. McCullagh, and J. A. Nelder. "Generalized Linear Models (2nd ed.)." Journal of the American Statistical Association 88, no. 422 (June 1993): 698. http://dx.doi.org/10.2307/2290358.

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18

Bonat, Wagner Hugo, and Bent Jørgensen. "Multivariate covariance generalized linear models." Journal of the Royal Statistical Society: Series C (Applied Statistics) 65, no. 5 (March 1, 2016): 649–75. http://dx.doi.org/10.1111/rssc.12145.

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19

Cowles, Mary Kathryn. "Generalized, Linear, and Mixed Models." Journal of the American Statistical Association 101, no. 476 (December 1, 2006): 1724. http://dx.doi.org/10.1198/jasa.2006.s145.

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20

Percy, David F. "Prediction for generalized linear models." Journal of Applied Statistics 20, no. 2 (January 1993): 285–91. http://dx.doi.org/10.1080/02664769300000023.

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21

Cheek, P. J., P. McCullagh, and J. A. Nelder. "Generalized Linear Models, 2nd Edn." Applied Statistics 39, no. 3 (1990): 385. http://dx.doi.org/10.2307/2347392.

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22

Rothe, Günter. "Bootstrap for generalized linear models." Statistical Papers 30, no. 1 (December 1989): 17–26. http://dx.doi.org/10.1007/bf02924305.

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23

Li, Zhijie, Qiuwen Chen, Qiang Xu, and Koen Blanckaert. "Generalized Likelihood Uncertainty Estimation Method in Uncertainty Analysis of Numerical Eutrophication Models: Take BLOOM as an Example." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/701923.

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Uncertainty analysis is of great importance to assess and quantify a model's reliability, which can improve decision making based on model results. Eutrophication and algal bloom are nowadays serious problems occurring on a worldwide scale. Numerical models offer an effective way to algal bloom prediction and management. Due to the complex processes of aquatic ecosystem, such numerical models usually contain a large number of parameters, which may lead to important uncertainty in the model results. This research investigates the applicability of generalized likelihood uncertainty estimation (GLUE) to analyze the uncertainty of numerical eutrophication models that have a large number of intercorrelated parameters. The 3-dimensional primary production model BLOOM, which has been broadly used in algal bloom simulations for both fresh and coastal waters, is used.

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24

Zhang, Xinyu, Dalei Yu, Guohua Zou, and Hua Liang. "Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models." Journal of the American Statistical Association 111, no. 516 (October 1, 2016): 1775–90. http://dx.doi.org/10.1080/01621459.2015.1115762.

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25

Woods, D. C., and S. M. Lewis. "Continuous Optimal Designs for Generalized Linear Models under Model Uncertainty." Journal of Statistical Theory and Practice 5, no. 1 (March 2011): 137–45. http://dx.doi.org/10.1080/15598608.2011.10412056.

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26

Alam, Md Moudud. "Likelihood Prediction for Generalized Linear Mixed Models under Covariate Uncertainty." Communications in Statistics - Theory and Methods 43, no. 2 (December 12, 2013): 219–34. http://dx.doi.org/10.1080/03610926.2012.657330.

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27

Chen, Cheng, Changle Peng, Hetao Hou, and Junjian Liang. "Comparison of Magnetorheological Damper Models through Parametric Uncertainty Analysis Using Generalized Likelihood Uncertainty Estimation." Journal of Engineering Mechanics 147, no. 2 (February 2021): 04020146. http://dx.doi.org/10.1061/(asce)em.1943-7889.0001885.

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28

Zhang, Zhongxin, and Holford Theodore. "Generalized conditionally linear models." Journal of Statistical Computation and Simulation 62, no. 1-2 (December 1998): 105–21. http://dx.doi.org/10.1080/00949659808811927.

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29

Albi, Giacomo, Lorenzo Pareschi, and Mattia Zanella. "Uncertainty Quantification in Control Problems for Flocking Models." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/850124.

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The optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC) approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits steering of the system towards the desired state even in unstable regimes.

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30

Wang, Ya Jun, and Wo Hua Zhang. "Super Gravity Dam Generalized Damage Study." Advanced Materials Research 479-481 (February 2012): 421–25. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.421.

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Fuzzy sub-space, with analysis on generalized uncertainty of damage, is setup in this paper when topological consistency of damage fuzzy and randomness on [0,1] scale being demonstrated deeply. Furthermore, deduced under fuzzy characteristics translation are three fuzzy analytical models of damage functional, namely, half depressed distribution, swing distribution, combined swing distribution, by which, fuzzy extension territory on damage evolution is formulated here. With the representation of damage variable ß probabilistic distribution as well as formulation on stochastic sub-space of damage variable, expended on the basis of extension criterion and fuzzy probability is damage model defined within generalized uncertain space, by which, introduced is fuzzy probabilistic integral algorithm of generalized uncertain damage variable that could be simulated by the forthcoming fuzzy stochastic damage constitution model based on three fuzzy functional models before. Moreover, in order to realize the joint of fuzzy input and output procedure on generalized uncertain damage variable calculation, fuzzy self-adapting stochastic damage reliability algorithm is, with the update on fuzzy stochastic finite element method within standard normal distribution probabilistic space by the help of foregoing fuzzy stochastic damage constitution model, offered in this paper on the basis of equivalent-normalization and orthogonal design theory. 3-dimension fuzzy stochastic damage mechanical status of numerical model of Longtan Rolled-Concrete Dam is researched here by fuzzy stochastic damage finite element method program under property authority. Random field parameters’ statistical dependence and non-normality are considered comprehensively in fuzzy stochastic damage model of this paper, by which, damage uncertainty’s proper development and conception expansion as well as fuzzy and randomness of mechanics are hybridized overall in fuzzy stochastic damage analysis process.

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31

Liu, Xuan, Melanie M. Wall, and James S. Hodges. "Generalized spatial structural equation models." Biostatistics 6, no. 4 (April 20, 2005): 539–57. http://dx.doi.org/10.1093/biostatistics/kxi026.

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32

Burman, Prabir. "Estimation of generalized additive models." Journal of Multivariate Analysis 32, no. 2 (February 1990): 230–55. http://dx.doi.org/10.1016/0047-259x(90)90083-t.

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33

Lefkovitch, L. P. "Seemingly Unrelated Generalized Linear Models." Biometrical Journal 33, no. 8 (1991): 899–912. http://dx.doi.org/10.1002/bimj.4710330802.

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34

Brant, Rollin. "Residual components in generalized linear models." Canadian Journal of Statistics 15, no. 2 (June 1987): 115–26. http://dx.doi.org/10.2307/3315200.

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35

Pupashenko, Daria, Peter Ruckdeschel, and Matthias Kohl. "L2 differentiability of generalized linear models." Statistics & Probability Letters 97 (February 2015): 155–64. http://dx.doi.org/10.1016/j.spl.2014.11.020.

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36

Chang, Potter C., and Annette J. Dobson. "An Introduction to Generalized Linear Models." Journal of the American Statistical Association 86, no. 416 (December 1991): 1149. http://dx.doi.org/10.2307/2290548.

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37

STUTE, WINFRIED, and LI-XING ZHU. "Model Checks for Generalized Linear Models." Scandinavian Journal of Statistics 29, no. 3 (September 2002): 535–45. http://dx.doi.org/10.1111/1467-9469.00304.

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38

Rabe, S., and K. V. Mardia. "Generalized Ising models and their applications." Journal of Applied Statistics 21, no. 5 (January 1994): 479–94. http://dx.doi.org/10.1080/757584022.

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39

Wells, Martin T. "Generalized Linear Models: A Bayesian Perspective." Journal of the American Statistical Association 96, no. 453 (March 2001): 339–55. http://dx.doi.org/10.1198/jasa.2001.s388.

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40

Lang, Joseph B. "An Introduction to Generalized Linear Models." Journal of the American Statistical Association 98, no. 464 (December 2003): 1086–87. http://dx.doi.org/10.1198/jasa.2003.s312.

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41

Bergesio, Andrea, and Victor J. Yohai. "Projection Estimators for Generalized Linear Models." Journal of the American Statistical Association 106, no. 494 (June 2011): 661–71. http://dx.doi.org/10.1198/jasa.2011.tm09774.

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42

Verbeek, A. "The compactification of generalized linear models." Statistica Neerlandica 46, no. 2-3 (July 1992): 107–42. http://dx.doi.org/10.1111/j.1467-9574.1992.tb01332.x.

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43

Meech, Robert, and A. J. Dobson. "An Introduction to Generalized Linear Models." Applied Statistics 41, no. 1 (1992): 217. http://dx.doi.org/10.2307/2347633.

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44

Nyquist, Hans. "Restricted Estimation of Generalized Linear Models." Applied Statistics 40, no. 1 (1991): 133. http://dx.doi.org/10.2307/2347912.

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45

Nielsen, Søren Feodor. "Generalized linear models for insurance data." Journal of Applied Statistics 37, no. 4 (March 15, 2010): 703. http://dx.doi.org/10.1080/02664760902811571.

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46

Benaïm, Michel, Sebastian J. Schreiber, and Pierre Tarrès. "Generalized URN models of evolutionary processes." Annals of Applied Probability 14, no. 3 (August 2004): 1455–78. http://dx.doi.org/10.1214/105051604000000422.

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47

Burridge, J., and P. Sebastiani. "Optimal designs for generalized linear models." Journal of the Italian Statistical Society 1, no. 2 (August 1992): 183–202. http://dx.doi.org/10.1007/bf02589030.

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48

Chikhaoui, Khaoula, Noureddine Bouhaddi, Najib Kacem, Mohamed Guedri, and Mohamed Soula. "Uncertainty quantification/propagation in nonlinear models." Engineering Computations 34, no. 4 (June 12, 2017): 1082–106. http://dx.doi.org/10.1108/ec-11-2015-0363.

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Purpose The purpose of this paper is to develop robust metamodels, which allow propagating parametric uncertainties, in the presence of localized nonlinearities, with reduced cost and without significant loss of accuracy. Design/methodology/approach The proposed metamodels combine the generalized polynomial chaos expansion (gPCE) for the uncertainty propagation and reduced order models (ROMs). Based on the computation of deterministic responses, the gPCE requires prohibitive computational time for large-size finite element models, large number of uncertain parameters and presence of nonlinearities. To overcome this issue, a first metamodel is created by combining the gPCE and a ROM based on the enrichment of the truncated Ritz basis using static residuals taking into account the stochastic and nonlinear effects. The extension to the Craig–Bampton approach leads to a second metamodel. Findings Implementing the metamodels to approximate the time responses of a frame and a coupled micro-beams structure containing localized nonlinearities and stochastic parameters permits to significantly reduce computation cost with acceptable loss of accuracy, with respect to the reference Latin Hypercube Sampling method. Originality/value The proposed combination of the gPCE and the ROMs leads to a computationally efficient and accurate tool for robust design in the presence of parametric uncertainties and localized nonlinearities.

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49

Doyen, Laurent, and Olivier Gaudoin. "Imperfect maintenance in a generalized competing risks framework." Journal of Applied Probability 43, no. 3 (September 2006): 825–39. http://dx.doi.org/10.1239/jap/1158784949.

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In this paper we present a general framework for the modelling of the process of corrective and condition-based preventive maintenance actions for complex repairable systems. A new class of models is proposed, the generalized virtual age models. On the one hand, these models generalize Kijima's virtual age models to the case where both preventive and corrective maintenances are present. On the other hand, they generalize the usual competing risks models to imperfect maintenance actions which do not renew the system. A generalized virtual age model is defined by both a sequence of effective ages which characterizes the effects of both types of maintenance according to a classical virtual age model, and a usual competing risks model which characterizes the dependency between the two types of maintenance. Several particular cases of the general model are derived.

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50

Tawfik, A., and A. Diab. "Generalized uncertainty principle: Approaches and applications." International Journal of Modern Physics D 23, no. 12 (October 2014): 1430025. http://dx.doi.org/10.1142/s0218271814300250.

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In this paper, we review some highlights from the String theory, the black hole physics and the doubly special relativity and some thought experiments which were suggested to probe the shortest distances and/or maximum momentum at the Planck scale. Furthermore, all models developed in order to implement the minimal length scale and/or the maximum momentum in different physical systems are analyzed and compared. They entered the literature as the generalized uncertainty principle (GUP) assuming modified dispersion relation, and therefore are allowed for a wide range of applications in estimating, for example, the inflationary parameters, Lorentz invariance violation, black hole thermodynamics, Saleker–Wigner inequalities, entropic nature of gravitational laws, Friedmann equations, minimal time measurement and thermodynamics of the high-energy collisions. One of the higher-order GUP approaches gives predictions for the minimal length uncertainty. A second one predicts a maximum momentum and a minimal length uncertainty, simultaneously. An extensive comparison between the different GUP approaches is summarized. We also discuss the GUP impacts on the equivalence principles including the universality of the gravitational redshift and the free fall and law of reciprocal action and on the kinetic energy of composite system. The existence of a minimal length and a maximum momentum accuracy is preferred by various physical observations. The concern about the compatibility with the equivalence principles, the universality of gravitational redshift and the free fall and law of reciprocal action should be addressed. We conclude that the value of the GUP parameters remain a puzzle to be verified.

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