Journal articles: 'Hierarchical variables'

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Shneiberg, I. Ya. "Hierarchical Sequences of Random Variables." Theory of Probability & Its Applications 31, no. 1 (March 1987): 137–41. http://dx.doi.org/10.1137/1131018.

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Yoshimura, Masataka, and Kazuhiro Izui. "Smart Optimization of Machine Systems Using Hierarchical Genotype Representations." Journal of Mechanical Design 124, no. 3 (August 6, 2002): 375–84. http://dx.doi.org/10.1115/1.1486013.

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Design problems for machine products are generally hierarchically expressed. With conventional product optimization methods, however, it is difficult to concurrently optimize all design variables of portions within such hierarchical structures. This paper proposes a design optimization method using genetic algorithms containing hierarchical genotype representations, so that the hierarchical structures of machine system designs are exactly expressed through genotype coding, and optimization can be concurrently conducted for all of the hierarchical structures. Crossover and mutation operations for manipulating the hierarchical genotype representations are also developed. The proposed method is applied to a machine-tool structural design and a 2 DOF robot arm design to demonstrate its effectiveness.

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Lopera Gonzalez, Luis I., and Oliver Amft. "Mining hierarchical relations in building management variables." Pervasive and Mobile Computing 26 (February 2016): 91–101. http://dx.doi.org/10.1016/j.pmcj.2015.10.009.

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Camiz, S., J. Denimal, and V. Pillar. "Hierarchical factor classification of variables in ecology." Community Ecology 7, no. 2 (December 2006): 165–79. http://dx.doi.org/10.1556/comec.7.2006.2.4.

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Wang, Jie, and Xiao Dong Zhu. "Analysis and Application of a Kind of Hierarchical Fuzzy Systems." Advanced Materials Research 219-220 (March 2011): 1097–100. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1097.

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In this paper a kind of hierarchical fuzzy systems was introduced. The characteristics and structural relation of this hierarchical fuzzy system were analyzed. The sensitivity between the input variables and the output variables and the position of variables in the hierarchical fuzzy system were given according to the importance of variables. The weight coefficient of variables was confirmed applying the methods of analytic hierarchical process (AHP). Then the structural analysis and the weight coefficient were applied to the forewarning system of oil drilling.

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Pulido-Valdeolivas, I., D. Gómez-Andrés, J. A. Martin, J. López, E. Gómez-Barrena, and E. Rausell. "P6.14 Hierarchical clustering of Gillette Gait Index variables." Clinical Neurophysiology 122 (June 2011): S87. http://dx.doi.org/10.1016/s1388-2457(11)60303-9.

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Kristan, William B. "Sources and Expectations for Hierarchical Structure in Bird-habitat Associations." Condor 108, no. 1 (February 1, 2006): 5–12. http://dx.doi.org/10.1093/condor/108.1.5.

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Abstract Hierarchical structure in bird-habitat associations can arise from hierarchical structure in environmental variables and from the scale-dependent responses of birds to habitat. Hierarchical structure in environmental variables is expected to result from interactions between variables that differ in grain size (spatial resolution) and frequency, and should occur commonly. Birds cannot accurately sample habitat characteristics at all spatial scales simultaneously, and the habitat chosen for a given purpose may differ depending on whether a bird samples from high above the ground (which is best for sampling coarse-grained variables) or from ground level (which is best for sampling fine-grained variables). Additionally, birds may exhibit an absolute response to a habitat variable, if it is unsuitable beyond some threshold level, or a relative response, if all available habitat is suitable but some is preferred. Models that can represent hierarchical structure in habitat, as well as hierarchical, scale-dependent responses by birds, should provide researchers the best chance of understanding avian habitat associations.

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JIA, WEIJIA, and ZHIBIN SUN. "ON COMPUTATIONAL COMPLEXITY OF HIERARCHICAL OPTIMIZATION." International Journal of Foundations of Computer Science 13, no. 05 (October 2002): 667–70. http://dx.doi.org/10.1142/s0129054102001369.

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In this work, the computational complexity of a hierarchic optimization problem involving in several players is studied. Each player is assigned with a linear objective function. The set of variables is partitioned such that each subset corresponds to one player as its decision variables. All the players jointly make a decision on the values of these variables such that a set of linear constraints should be satisfied. One special player, called the leader, makes decision on its decision variables before of all the other players. The rest, after learnt of the decision of the leader, make their choices so that their decisions form a Nash Equilibrium for them, breaking tie by maximizing the objective function of player. We show that the exact complexity of the problem is FPNP-complete.

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Asfaw Dagne, Getachew. "Bayesian analysis of hierarchical poisson models with latent variables." Communications in Statistics - Theory and Methods 28, no. 1 (1999): 119–36. http://dx.doi.org/10.1080/03610929908832286.

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Hajnal, Istvan, and Geert Loosveldt. "The Sensitivity of Hierarchical Clustering Solutions to Irrelevant Variables." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 50, no. 1 (March 1996): 56–70. http://dx.doi.org/10.1177/075910639605000105.

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You, Heecheon, and Taebeum Ryu. "Development of a Hierarchical Estimation Method for Anthropometric Variables." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 6 (September 2004): 961–65. http://dx.doi.org/10.1177/154193120404800609.

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Gallizo, José L., and Manuel Salvador. "Share prices and accounting variables: a hierarchical Bayesian analysis." Review of Accounting and Finance 5, no. 3 (July 2006): 268–78. http://dx.doi.org/10.1108/14757700610686813.

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Carlson, Kevin, Hanko K. Zeitzmann, and Jerry Flynn. "Add Artifact Control Variables Last in Hierarchical Regression Analyses." Academy of Management Proceedings 2012, no. 1 (July 2012): 16952. http://dx.doi.org/10.5465/ambpp.2012.16952abstract.

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Rockova, Veronika, Emmanuel Lesaffre, Jolanda Luime, and Bob Löwenberg. "Hierarchical Bayesian formulations for selecting variables in regression models." Statistics in Medicine 31, no. 11-12 (January 25, 2012): 1221–37. http://dx.doi.org/10.1002/sim.4439.

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You, Heecheon, and Taebeum Ryu. "Development of a hierarchical estimation method for anthropometric variables." International Journal of Industrial Ergonomics 35, no. 4 (April 2005): 331–43. http://dx.doi.org/10.1016/j.ergon.2004.09.007.

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Xia, Lu, Meihua Yang, Lang Li, and Xin Zhang. "Aerodynamic Design Based on Global Sensitivity Analysis Method." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 1 (February 2018): 49–56. http://dx.doi.org/10.1051/jnwpu/20183610049.

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To deal with the problem of the difficult optimization search and expensive computational cost caused by large-scale design variables, the hierarchical optimization design system based on the global sensitivity analysis method is established in this paper. The M-OAT method is used to analyze the global sensitivity of the design variables, according to the sensitivity information to layer design variables, then optimize the design variables in each hierarchy. Through the study of the hierarchical optimization design of airfoils and wings, compared with the normal parameter optimization design system, the hierarchical optimization design system based on the global sensitivity analysis method can reduce effectively the number of design variables in a single optimization, reduce the difficulty of the optimization search, improve the convergence speed of the optimization, gain better optimization results at the same time. For optimization design with large-scale design variables, the hierarchical optimization design system based on the global sensitivity analysis method is a sort of effective ways of design.

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Fukuda, Toshio, Yasuhisa Hasegawa, and Koji Shimojima. "Structure Organization of Hierarchical Fuzzy Model Using Genetic Algorithm." Journal of Robotics and Mechatronics 7, no. 1 (February 20, 1995): 29–35. http://dx.doi.org/10.20965/jrm.1995.p0029.

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This paper proposes a method to organize the hierarchical structure of fuzzy model using the Genetic Algorithm and back-propagation method. The number of fuzzy rules increases exponentially with the number of input variables. Thus, a fuzzy system with many input variables has an extremely large number of fuzzy rules. Hierarchical structure of fuzzy reasoning is one of the methods to reduce the number of fuzzy rules and membership functions. However, it is very difficult to organize the hierarchical structure because the hierarchical structure cannot be constructed without considering the relationship among input and output variables. The proposed method can organize the suitable hierarchical structure for the relationship among input and output variables in teaching numerical data. It is based on the Genetic Algorithm with an evaluation function as a strategy that adopts a system with fewer fuzzy rules and more accurate outputs. The proposed method is applied to the approximation problems of multi-dimensional nonlinear functions in order to demonstrate its effectiveness.

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Zhao, Kun, Hongwei Ding, Kai Ye, and Xiaohui Cui. "A Transformer-Based Hierarchical Variational AutoEncoder Combined Hidden Markov Model for Long Text Generation." Entropy 23, no. 10 (September 29, 2021): 1277. http://dx.doi.org/10.3390/e23101277.

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The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences. There is a particular relationship between each sentence, especially between the latent variables that control the generation of the sentences. The relationships between these latent variables help in generating continuous and logically connected long texts. There exist very few studies on the relationships between these latent variables. We proposed a method for combining the Transformer-Based Hierarchical Variational AutoEncoder and Hidden Markov Model (HT-HVAE) to learn multiple hierarchical latent variables and their relationships. This application improves long text generation. We use a hierarchical Transformer encoder to encode the long texts in order to obtain better hierarchical information of the long text. HT-HVAE’s generation network uses HMM to learn the relationship between latent variables. We also proposed a method for calculating the perplexity for the multiple hierarchical latent variable structure. The experimental results show that our model is more effective in the dataset with strong logic, alleviates the notorious posterior collapse problem, and generates more continuous and logically connected long text.

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Shang, Zengqiang, Peiyang Shi, Pengyuan Zhang, Li Wang, and Guangying Zhao. "HierTTS: Expressive End-to-End Text-to-Waveform Using a Multi-Scale Hierarchical Variational Auto-Encoder." Applied Sciences 13, no. 2 (January 8, 2023): 868. http://dx.doi.org/10.3390/app13020868.

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End-to-end text-to-speech (TTS) models that directly generate waveforms from text are gaining popularity. However, existing end-to-end models are still not natural enough in their prosodic expressiveness. Additionally, previous studies on improving the expressiveness of TTS have mainly focused on acoustic models. There is a lack of research on enhancing expressiveness in an end-to-end framework. Therefore, we propose HierTTS, a highly expressive end-to-end text-to-waveform generation model. It deeply couples the hierarchical properties of speech with hierarchical variational auto-encoders and models multi-scale latent variables, at the frame, phone, subword, word, and sentence levels. The hierarchical encoder encodes the speech signal from fine-grained features into coarse-grained latent variables. In contrast, the hierarchical decoder generates fine-grained features conditioned on the coarse-grained latent variables. We propose a staged KL-weighted annealing strategy to prevent hierarchical posterior collapse. Furthermore, we employ a hierarchical text encoder to extract linguistic information at different levels and act on both the encoder and the decoder. Experiments show that our model performs closer to natural speech in prosody expressiveness and has better generative diversity.

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Fuchs, Sebastian, F. Marta L. Di Lascio, and Fabrizio Durante. "Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables." Computational Statistics & Data Analysis 159 (July 2021): 107201. http://dx.doi.org/10.1016/j.csda.2021.107201.

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Romero-Medrano, Lorena, Pablo Moreno-Muñoz, and Antonio Artés-Rodríguez. "Multinomial Sampling of Latent Variables for Hierarchical Change-Point Detection." Journal of Signal Processing Systems 94, no. 2 (October 8, 2021): 215–27. http://dx.doi.org/10.1007/s11265-021-01705-8.

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AbstractBayesian change-point detection, with latent variable models, allows to perform segmentation of high-dimensional time-series with heterogeneous statistical nature. We assume that change-points lie on a lower-dimensional manifold where we aim to infer a discrete representation via subsets of latent variables. For this particular model, full inference is computationally unfeasible and pseudo-observations based on point-estimates of latent variables are used instead. However, if their estimation is not certain enough, change-point detection gets affected. To circumvent this problem, we propose a multinomial sampling methodology that improves the detection rate and reduces the delay while keeping complexity stable and inference analytically tractable. Our experiments show results that outperform the baseline method and we also provide an example oriented to a human behavioral study.

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Joo, Moon-G. "Hierarchical Fuzzy System with only system variables for IF-part." Journal of Korean Institute of Intelligent Systems 14, no. 2 (April 1, 2004): 178–83. http://dx.doi.org/10.5391/jkiis.2004.14.2.178.

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Commons, Michael Lamport, Sagun Giri, and William Joseph Harrigan. "The small effects of non-hierarchical complexity variables on performance." Behavioral Development Bulletin 19, no. 4 (December 2014): 31–36. http://dx.doi.org/10.1037/h0101079.

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Soffritti, Gabriele. "Hierarchical clustering of variables: a comparison among strategies of analysis." Communications in Statistics - Simulation and Computation 28, no. 4 (January 1999): 977–99. http://dx.doi.org/10.1080/03610919908813588.

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Kojadinovic, Ivan. "Agglomerative hierarchical clustering of continuous variables based on mutual information." Computational Statistics & Data Analysis 46, no. 2 (June 2004): 269–94. http://dx.doi.org/10.1016/s0167-9473(03)00153-1.

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Mandozzi, Jacopo, and Peter Bühlmann. "Hierarchical Testing in the High-Dimensional Setting With Correlated Variables." Journal of the American Statistical Association 111, no. 513 (January 2, 2016): 331–43. http://dx.doi.org/10.1080/01621459.2015.1007209.

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Giunta, Gaetano, Salim Belouettar, Olivier Polit, Laurent Gallimard, Philippe Vidal, and Michele D’ottavio. "Hierarchical Beam Finite Elements Based Upon a Variables Separation Method." International Journal of Applied Mechanics 08, no. 02 (March 2016): 1650026. http://dx.doi.org/10.1142/s1758825116500265.

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A family of hierarchical one-dimensional beam finite elements developed within a variables separation framework is presented. A Proper Generalized Decomposition (PGD) is used to divide the global three-dimensional problem into two coupled ones: one defined on the cross-section space (beam modeling kinematic approximation) and one belonging to the axis space (finite element solution). The displacements over the cross-section are approximated via a Unified Formulation (UF). A Lagrangian approximation is used along the beam axis. The resulting problems size is smaller than that of the classical equivalent finite element solution. The approach is, then, particularly attractive for higher-order beam models and refined axial meshes. The numerical investigations show that the proposed method yields accurate yet computationally affordable three-dimensional displacement and stress fields solutions.

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Kang, Mincheol. "A proposed improvement to the multilevel theory for hierarchical decision-making teams." Journal of Management & Organization 16, no. 1 (March 2010): 151–67. http://dx.doi.org/10.1017/s1833367200002339.

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AbstractThe multilevel theory proposed by Hollenbeck et al. identified a set of core variables that are central to accuracy in decision-making in hierarchical teams with distributed expertise. Following the identification of the limitations of the original core variables, a new set of core variables is proposed: (a) member validity, which represents the overall predictability of team members with regard to the correct decision and (b) hierarchical sensitivity, which represents the effectiveness of the leader's weightings of members' recommendations. To test the revised theory, a computational model called Team-Soar is used. The simulation results show that the small set of new core variables explains a large portion of the variance in the team decision accuracy and mediates the effects of other variables on the accuracy. The revised theory can be used as a conceptual vehicle to parsimoniously explain the performance of hierarchical decision-making teams. The theory could also be used to diagnose and train real teams in terms of the core variables.

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Kang, Mincheol. "A proposed improvement to the multilevel theory for hierarchical decision-making teams." Journal of Management & Organization 16, no. 1 (March 2010): 151–67. http://dx.doi.org/10.5172/jmo.16.1.151.

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AbstractThe multilevel theory proposed by Hollenbeck et al. identified a set of core variables that are central to accuracy in decision-making in hierarchical teams with distributed expertise. Following the identification of the limitations of the original core variables, a new set of core variables is proposed: (a) member validity, which represents the overall predictability of team members with regard to the correct decision and (b) hierarchical sensitivity, which represents the effectiveness of the leader's weightings of members' recommendations. To test the revised theory, a computational model called Team-Soar is used. The simulation results show that the small set of new core variables explains a large portion of the variance in the team decision accuracy and mediates the effects of other variables on the accuracy. The revised theory can be used as a conceptual vehicle to parsimoniously explain the performance of hierarchical decision-making teams. The theory could also be used to diagnose and train real teams in terms of the core variables.

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Deliu, M., S. Yavuz, M. Sperrin, D. Belgrave, C. Sackesen, U. Sahiner, A. Custovic, and O. Kalayci. "P120 Challenges in using hierarchical clustering to identify asthma subtypes: choosing the variables and variable transformation." Thorax 71, Suppl 3 (November 15, 2016): A148.1—A148. http://dx.doi.org/10.1136/thoraxjnl-2016-209333.263.

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Krenke, A. N., Yu G. Puzachenko, and M. Yu Puzachenko. "Spatial organization of regional mesoclimate." Izvestiya Rossiiskoi akademii nauk. Seriya geograficheskaya, no. 3 (June 25, 2019): 116–30. http://dx.doi.org/10.31857/s2587-556620193116-130.

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In this article the method of derivation of the hierarchical levels of organization of climatic variables or regional scale is considered. Based on the Worldclim database, the main integral factors reflecting the variation of climatic variables are identified, and then decomposed into hierarchical levels with different linear dimensions of oscillations. Hierarchical levels are distinguished through the study of the fractal dimensions of different parts of the spectrum of the obtained factors and the isolation of subharmonics on the basis of an analysis of the residues of the fractal model. The analysis shows the existence of a complex hierarchical organization of the region's mesoclimate. The approach makes it possible to identify the most significant scales and amplitudes of fluctuations in climatic variables, both for natural and for agricultural ecosystems. Differentiation of the variation of climatic variables at different spatial scales and the influence of these elements on a specific ecosystem object creates a basis for constructing statistical models of ecosystem processes or yield patterns of various agricultural crops. The possibilities of visualization of climate variation at different hierarchical levels and reflection of equilibrium (normative) relations between the studied ecosystem processes and the current state of climate in the region are shown.

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Reise, Steven P., and Naihua Duan. "Multilevel Modeling and its Application in Counseling Psychology Research." Counseling Psychologist 27, no. 4 (July 1999): 528–51. http://dx.doi.org/10.1177/0011000099274003.

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Multilevel modeling (MLM) should be used when a researcher has collected hierarchical data. For example, when a researcher investigates an outcome variable (e.g., depression) with several clients drawn from different clinicians, the data set has a hierarchical structure. Herein, we describe the use of MLM in counseling research. The goals include the following: (a) to specify research contexts where MLM may be applied, (b) to describe how to conduct data analyses using MLM, and (c) to highlight key statistical and design issues encountered when analyzing hierarchical data. We also highlight how MLM can be used (a) to provide valid statistical inference in the presence of hierarchical data structure, (b) to separate the within-group effects from between-group effects for predictor variables, and (c) to study the interactions among predictor variables drawn from different levels (e.g., variables drawn from both clients and their clinicians).

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Veltmeyer, Johan, and Sherif Mohamed. "Investigation into the hierarchical nature of TQM variables using structural modelling." International Journal of Quality & Reliability Management 34, no. 4 (April 3, 2017): 462–77. http://dx.doi.org/10.1108/ijqrm-04-2015-0052.

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Purpose The purpose of this paper is to provide the empirical evidence supporting the existence of a multi-level hierarchical TQM model showing the structural inter-relationships among a total of 16 TQM variables (i.e. drivers, enablers and outcomes). Design/methodology/approach The set of identified TQM variables is the product of an in-depth review of the literature, and a robust reiterative process of verification and validation. Inter-relationships among the TQM variables were subjected to the scrutiny of a panel of experts, and were used as a basis for developing a web-based survey to explore the existence as well as strength of the structural relationship between each and every pair of the identified variables using interpretive structural modelling and MICMAC (Impact Matrix Cross-Reference Multiplication Applied to a Classification). Findings TQM variables were classified and clustered based on their influence and dependence on each other. Variables such as commitment by top management and customer satisfaction appear to have a strong chance to affect change, whereas variables such suppliers and competitors are very dependent on, and sensitive to, the evolution of the influent variables. Originality/value The paper demonstrates a multi-level TQM model encompassing all identified TQM drivers, enablers, and outcomes. The paper not only addresses a gap in the relevant literature (reduces the evidence scarcity about the hierarchical nature of TQM variables), but also gives insights into the variables with most driving power needing greater management attention.

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Bolnokin, V. E., S. A. Sorokin, D. I. Mutin, E. I. Mutina, S. V. Storozhev, V. I. Storozhev, and O. Yu Zaslavskaya. "Mathematical model of intelligent decision support based on hierarchical logical constructions." Journal of Physics: Conference Series 2373, no. 5 (December 1, 2022): 052020. http://dx.doi.org/10.1088/1742-6596/2373/5/052020.

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Abstract The paper is devoted to the investigation of mathematical model of intelligent decision support based on a hierarchical logical construction. Hierarchical logical constructions and forecasting variables are described. The goal of hierarchical logical construction analysis is to get the most accurate forecast possible. Models and algorithms of an intelligent decision support system using a hierarchical logical construction are described.

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DATTA, AJOY K., JERRY L. DERBY, JAMES E. LAWRENCE, and SÉBASTIEN TIXEUIL. "STABILIZING HIERARCHICAL ROUTING." Journal of Interconnection Networks 01, no. 04 (December 2000): 283–302. http://dx.doi.org/10.1142/s0219265900000172.

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Hierarchical routing provides a less expensive algorithm compared to the traditional all-pairs routing algorithms. We present an algorithm in this paper which benefits from the lower memory requirement, faster routing table lookup, and less costly broadcast exemplified by hierarchical routing, and yet maintains routing capability of all pairs of connected nodes even in the presence of faults, such as link/node failures and repairs, and corruption of program variables. Additionally, this algorithm solves the problem of cluster partitioning where nodes that are supposed to be in the same subset of the network, become isolated due to link or node failures. Being self-stabilizing, starting from an arbitrary state (with possibly corrupted routing tables), the protocol is guaranteed to reach a configuration with routing tables containing valid entries in a finite time. The protocol automatically updates the shortest paths in the face of dynamically changing link weights. The proposed protocol also dynamically allocates/deallocates storage for the routing information as the network size changes. The algorithm works on an arbitrary topology and under a distributed daemon model.

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MEURICE, YANNICK. "REMARKS CONCERNING POLYAKOV’S CONJECTURE FOR THE 3D ISING MODEL AND THE HIERARCHICAL APPROXIMATION." Modern Physics Letters A 07, no. 35 (November 20, 1992): 3331–36. http://dx.doi.org/10.1142/s0217732392002718.

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We consider the possibility of using the hierarchical approximation to understand the continuum limit of a reformulation of the 3D Ising model initiated by Polyakov. We introduce several new formulations of the hierarchical model using dual or fermionic variables. We discuss several aspects of the renormalization group transformation in terms of these new variables. We mention a reformulation of the model closely related to string models proposed by Zabrodin.

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Raykov, Tenko, and George A. Marcoulides. "Intraclass Correlation Coefficients in Hierarchical Design Studies With Discrete Response Variables." Educational and Psychological Measurement 75, no. 6 (January 7, 2015): 1063–70. http://dx.doi.org/10.1177/0013164414564052.

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Wang, Di, Xiao-Jun Zeng, and John A. Keane. "Hierarchical hybrid fuzzy-neural networks for approximation with mixed input variables." Neurocomputing 70, no. 16-18 (October 2007): 3019–33. http://dx.doi.org/10.1016/j.neucom.2006.07.015.

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Yap, Dorcas Fen‐Fung, Nasriah Nasir, Karen S. M. Tan, and Lily H. S. Lau. "Variables which predict maternal self‐efficacy: A hierarchical linear regression analysis." Journal of Applied Research in Intellectual Disabilities 32, no. 4 (February 4, 2019): 841–48. http://dx.doi.org/10.1111/jar.12575.

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Schäfer, Martin, Hans-Ulrich Klein, and Holger Schwender. "Integrative analysis of multiple genomic variables using a hierarchical Bayesian model." Bioinformatics 33, no. 20 (June 5, 2017): 3220–27. http://dx.doi.org/10.1093/bioinformatics/btx356.

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Cook, J., and B. Derrida. "Polymers on disordered hierarchical lattices: A nonlinear combination of random variables." Journal of Statistical Physics 57, no. 1-2 (October 1989): 89–139. http://dx.doi.org/10.1007/bf01023636.

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Gibert, Karina, Aïda Valls, and Montserrat Batet. "Introducing semantic variables in mixed distance measures: Impact on hierarchical clustering." Knowledge and Information Systems 40, no. 3 (June 16, 2013): 559–93. http://dx.doi.org/10.1007/s10115-013-0663-5.

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Peterson, H. M., J. L. Nieber, R. Kanivetsky, and B. Shmagin. "Regionalization of landscape characteristics to map hydrologic variables." Journal of Hydroinformatics 16, no. 3 (October 29, 2013): 633–48. http://dx.doi.org/10.2166/hydro.2013.051.

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By integrating groundwater, surface water and vadose zone systems, the terrestrial hydrologic system can be used to spatially map water balance characteristics spanning local to global scales, even when long-term stream gauge data are unavailable. The Watershed Characteristics Approach (WCA) is a hydrologic estimation model developed using a system-based approach focused on the regionalization of landscape characteristics to define unique hierarchical hydrogeological units (HHUs) and establish their link to hydrologic characteristics. Although the WCA can be used to map any hydrologic variable, its validity is demonstrated by summarizing results generated by applying the methodology to quantify the renewable groundwater flux at a spatial scale lacking long-term stream gauge monitoring data. Landscape components for 97 East-Central Minnesota (ECM) watersheds were summarized and used to identify which unique combinations of characteristics statistically influenced mean annual minimum groundwater recharge. These resulting combinations of landscape characteristics defined each HHU; as additional characteristics were applied, units were refined to create a hierarchical organization. Results were mapped to spatially represent the renewable groundwater flux for ECM, demonstrating how hydrologic regionalization can address knowledge gaps in multi-scale processes and aid in quantifying water balance components, an essential key to sustainable water resources management.

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Myrseth, Inge. "Hierarchical Ensemble Kalman Filter." SPE Journal 15, no. 02 (March 11, 2010): 569–80. http://dx.doi.org/10.2118/125851-pa.

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Summary This paper presents the hierarchical ensemble Kalman filter (HEnKF) as a robust extension of the ensemble Kalman filter (EnKF). The HEnKF is developed to be robust against features like estimation uncertainty and rank deficiency related to covariance estimation in EnKF. The HEnKF imposes a hierarchical model on the state variables and uses prior distributions from the Gauss conjugate family of distributions to obtain more-robust estimates. An empirical study demonstrates that the HEnKF provides more-reliable results than the traditional EnKF approach. Better predictions and more-realistic prediction intervals are provided. The latter is caused by model-parameter uncertainty being an integral part of the HEnKF approach, while this effect is ignored in traditional EnKF. The two versions of the ensemble Kalman filter are also compared on a synthetic-reservoir study. The HEnKF appears as significantly better.

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Lefèvre, Claude, and Sergey Utev. "Comparing sums of exchangeable Bernoulli random variables." Journal of Applied Probability 33, no. 2 (September 1996): 285–310. http://dx.doi.org/10.2307/3215055.

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The paper is first concerned with a comparison of the partial sums associated with two sequences of n exchangeable Bernoulli random variables. It then considers a situation where such partial sums are obtained through an iterative procedure of branching type stopped at the first-passage time in a linearly decreasing upper barrier. These comparison results are illustrated with applications to certain urn models, sampling schemes and epidemic processes. A key tool is a non-standard hierarchical class of stochastic orderings between discrete random variables valued in {0, 1,· ··, n}.

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Lefèvre, Claude, and Sergey Utev. "Comparing sums of exchangeable Bernoulli random variables." Journal of Applied Probability 33, no. 02 (June 1996): 285–310. http://dx.doi.org/10.1017/s0021900200099733.

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Abstract:

The paper is first concerned with a comparison of the partial sums associated with two sequences of n exchangeable Bernoulli random variables. It then considers a situation where such partial sums are obtained through an iterative procedure of branching type stopped at the first-passage time in a linearly decreasing upper barrier. These comparison results are illustrated with applications to certain urn models, sampling schemes and epidemic processes. A key tool is a non-standard hierarchical class of stochastic orderings between discrete random variables valued in {0, 1,· ··, n}.

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ALONSO, SERAFÍN, ANTONIO MORÁN, MIGUEL A. PRADA, PABLO BARRIENTOS, and MANUEL DOMÍNGUEZ. "MONITORING POWER CONSUMPTION USING A GENERALIZED VARIANT OF SELF-ORGANIZING MAP (SOM)." International Journal of Modern Physics B 26, no. 25 (September 10, 2012): 1246005. http://dx.doi.org/10.1142/s0217979212460058.

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In this paper, we present a new approach for monitoring power consumption in several processes. The generalization of the envSOM algorithm, a variant of Self-Organizing Map (SOM), is used to build an electrical model and visualize the information. The envSOM extended to n hierarchical phases allows us to obtain a more accurate model from real past data. The model is conditioned hierarchically on environmental variables. In this way, time variables can be used to consider seasonality and weekday/hour periodicity. Time variable maps and electrical component planes make it possible to visualize and analyze power consumption. The representation of the Best Matching Unit (BMU) or its trajectory on these maps enables the on-line monitoring.

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Normington, James P., Eric F. Lock, Thomas A. Murray, and Caroline S. Carlin. "Bayesian variable selection in hierarchical difference-in-differences models." Statistical Methods in Medical Research 31, no. 1 (November 29, 2021): 169–83. http://dx.doi.org/10.1177/09622802211051087.

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A popular method for estimating a causal treatment effect with observational data is the difference-in-differences model. In this work, we consider an extension of the classical difference-in-differences setting to the hierarchical context in which data cannot be matched at the most granular level. Our motivating example is an application to assess the impact of primary care redesign policy on diabetes outcomes in Minnesota, in which the policy is administered at the clinic level and individual outcomes are not matched from pre- to post-intervention. We propose a Bayesian hierarchical difference-in-differences model, which estimates the policy effect by regressing the treatment on a latent variable representing the mean change in group-level outcome. We present theoretical and empirical results showing a hierarchical difference-in-differences model that fails to adjust for a particular class of confounding variables, biases the policy effect estimate. Using a structured Bayesian spike-and-slab model that leverages the temporal structure of the difference-in-differences context, we propose and implement variable selection approaches that target sets of confounding variables leading to unbiased and efficient estimation of the policy effect. We evaluate the methods’ properties through simulation, and we use them to assess the impact of primary care redesign of clinics in Minnesota on the management of diabetes outcomes from 2008 to 2017.

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Jacko, Julie A., and Gavriel Salvendy. "Hierarchical Menu Design: Breadth, Depth, and Task Complexity." Perceptual and Motor Skills 82, no. 3_suppl (June 1996): 1187–201. http://dx.doi.org/10.2466/pms.1996.82.3c.1187.

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In this research a relationship between an hierarchical menu's depth and the perceived complexity of a task involving menu retrieval was proposed and validated. 12 subjects were asked to use six different hierarchical menus of varying breadth and depth. The dependent variables were response time and accuracy. The independent variables were depth and breadth of the hierarchy. Subsequent to experimentation, the subjects were asked to complete a questionnaire on users' perceptions of the complexity of the different menu structures. As depth increased, perceived complexity of the menus increased significantly. These phenomena are linked to an existing theory of task complexity. We suggest that the cognitive component influencing users' perceptions of task complexity was short-term memory load.

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Liu, Ze yuan, and Xin long Li. "Hierarchical ensemble learning method in diversified dataset analysis." Journal of Physics: Conference Series 2078, no. 1 (November 1, 2021): 012027. http://dx.doi.org/10.1088/1742-6596/2078/1/012027.

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Abstract The remarkable advances in ensemble machine learning methods have led to a significant analysis in large data, such as random forest algorithms. However, the algorithms only use the current features during the process of learning, which caused the initial upper accuracy’s limit no matter how well the algorithms are. Moreover, the low classification accuracy happened especially when one type of observation’s proportion is much lower than the other types in training datasets. The aim of the present study is to design a hierarchical classifier which try to extract new features by ensemble machine learning regressors and statistical methods inside the whole machine learning process. In stage 1, all the categorical variables will be characterized by random forest algorithm to create a new variable through regression analysis while the numerical variables left will serve as the sample of factor analysis (FA) process to calculate the factors value of each observation. Then, all the features will be learned by random forest classifier in stage 2. Diversified datasets consist of categorical and numerical variables will be used in the method. The experiment results show that the classification accuracy increased by 8.61%. Meanwhile, it also improves the classification accuracy of observations with low proportion in the training dataset significantly.

Journal articles on the topic 'Hierarchical variables'

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