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Thematische Bibliographien / Statistical model

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Auswahl der wissenschaftlichen Literatur zum Thema „Statistical model“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 6. Juli 2024

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Zeitschriftenartikel zum Thema "Statistical model"

1

Li, Ken W. „A Model of Teaching Statistical Computing“. International Journal of Information and Education Technology 6, Nr. 2 (2016): 143–47. http://dx.doi.org/10.7763/ijiet.2016.v6.674.

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Iwasaki, Atsushi, Yoshinobu Shimamura und Akira Todoroki. „OS17-3-6 Optimization of the statistical model for the statistical damage diagnostic method“. Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2007.6 (2007): _OS17–3–6——_OS17–3–6—. http://dx.doi.org/10.1299/jsmeatem.2007.6._os17-3-6-.

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3

Choden, Sonam, und Suntaree Unhapipat. „Statistical Model for Personal Loan Prediction in Bhutan“. Journal of Advanced Research in Dynamical and Control Systems 11, Nr. 0009-SPECIAL ISSUE (25.09.2019): 416–22. http://dx.doi.org/10.5373/jardcs/v11/20192587.

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4

Claeskens, Gerda. „Statistical Model Choice“. Annual Review of Statistics and Its Application 3, Nr. 1 (Juni 2016): 233–56. http://dx.doi.org/10.1146/annurev-statistics-041715-033413.

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Ahmadi-Hadad, Armia. „Statistical model error“. Kidney International 103, Nr. 6 (Juni 2023): 1199. http://dx.doi.org/10.1016/j.kint.2023.03.001.

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Ahmadi-Hadad, Armia. „Statistical model error“. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 17, Nr. 4 (April 2023): 102755. http://dx.doi.org/10.1016/j.dsx.2023.102755.

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7

Xie, Yi, Xiaojie Lei und Peiai Zhang. „Statistical Model for the Trend of Prevalent Languages Speakers“. International Journal of Languages, Literature and Linguistics 6, Nr. 1 (März 2020): 24–30. http://dx.doi.org/10.18178/ijlll.2020.6.1.245.

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8

Kozmenko, Olga, und Viktor Oliynyk. „Statistical model of risk assessment of insurance company’s functioning“. Investment Management and Financial Innovations 12, Nr. 2-1 (07.08.2015): 189–94. http://dx.doi.org/10.21511/imfi.12(2-1).2015.01.

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Komatsu, Kanji. „Statistical Models for Model-Based Drug Development“. Japanese Journal of Biometrics 32, Special_Issue_2 (2011): 179–93. http://dx.doi.org/10.5691/jjb.32.179.

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Jafari, Ali, Ehsan Khamespanah, Haukur Kristinsson, Marjan Sirjani und Brynjar Magnusson. „Statistical model checking of Timed Rebeca models“. Computer Languages, Systems & Structures 45 (April 2016): 53–79. http://dx.doi.org/10.1016/j.cl.2016.01.004.

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Dissertationen zum Thema "Statistical model"

1

Rackham, Edward J. „Statistical model of reaction dynamics“. Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408683.

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Sassoon, Isabel Karen. „Argumentation for statistical model selection“. Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/argumentation-for-statistical-model-selection(79168e3a-2903-43dc-ac60-97a7c87f94f0).html.

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The increased availability of clinical data, in particular case data collected routinely, provides a valuable opportunity for analysis with a view to support evidence based decision making. In order to con dently leverage this data in support of decision making, it is essential to analyse it with rigour by employing the most appropriate statistical method. It can be dicult for a clinician to choose the appropriate statistical method and indeed the choice is not always straight forward, even for a statistician. The considerations as to what model to use depend on the research question, data and at times background information from the clinician, and will vary from model to model. This thesis develops an intelligent decision support method that supports the clinician by recommending the most appropriate statistical model approach given the research question and the available data. The main contributions of this thesis are: identi cation of the requirements from realworld collaboration with clinicians; development of an argumentation based approach to recommend statistical models based on a research question and data features; an argumentation scheme for proposing possible models; a statistical knowledge base designed to support the argumentation scheme, critical questions and preferences; a method of reasoning with the generated arguments and preference arguments. The approach is evaluated through case studies and a prototype.

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Barbot, Benoît. „Acceleration for statistical model checking“. Thesis, Cachan, Ecole normale supérieure, 2014. http://www.theses.fr/2014DENS0041/document.

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Ces dernières années, l'analyse de systèmes complexes critiques est devenue de plus en plus importante. En particulier, l'analyse quantitative de tels systèmes est nécessaire afin de pouvoir garantir que leur probabilité d'échec est très faible. La difficulté de l'analyse de ces systèmes réside dans le fait que leur espace d’état est très grand et que la probabilité recherchée est extrêmement petite, de l'ordre d'une chance sur un milliard, ce qui rend les méthodes usuelles inopérantes. Les algorithmes de Model Checking quantitatif sont les algorithmes classiques pour l'analyse de systèmes probabilistes. Ils prennent en entrée le système et son comportement attendu et calculent la probabilité avec laquelle les trajectoires du système correspondent à ce comportement. Ces algorithmes de Model Checking ont été largement étudié depuis leurs créations. Deux familles d'algorithme existent : - le Model Checking numérique qui réduit le problème à la résolution d'un système d'équations. Il permet de calculer précisément des petites probabilités mais soufre du problème d'explosion combinatoire- - le Model Checking statistique basé sur la méthode de Monte-Carlo qui se prête bien à l'analyse de très gros systèmes mais qui ne permet pas de calculer de petite probabilités. La contribution principale de cette thèse est le développement d'une méthode combinant les avantages des deux approches et qui renvoie un résultat sous forme d'intervalles de confiance. Cette méthode s'applique à la fois aux systèmes discrets et continus pour des propriétés bornées ou non bornées temporellement. Cette méthode est basée sur une abstraction du modèle qui est analysée à l'aide de méthodes numériques, puis le résultat de cette analyse est utilisé pour guider une simulation du modèle initial. Ce modèle abstrait doit à la fois être suffisamment petit pour être analysé par des méthodes numériques et suffisamment précis pour guider efficacement la simulation. Dans le cas général, cette abstraction doit être construite par le modélisateur. Cependant, une classe de systèmes probabilistes a été identifiée dans laquelle le modèle abstrait peut être calculé automatiquement. Cette approche a été implémentée dans l'outil Cosmos et des expériences sur des modèles de référence ainsi que sur une étude de cas ont été effectuées, qui montrent l'efficacité de la méthode. Cette approche à été implanté dans l'outils Cosmos et des expériences sur des modèles de référence ainsi que sur une étude de cas on été effectué, qui montre l'efficacité de la méthode
In the past decades, the analysis of complex critical systems subject to uncertainty has become more and more important. In particular the quantitative analysis of these systems is necessary to guarantee that their probability of failure is very small. As their state space is extremly large and the probability of interest is very small, typically less than one in a billion, classical methods do not apply for such systems. Model Checking algorithms are used for the analysis of probabilistic systems, they take as input the system and its expected behaviour, and compute the probability with which the system behaves as expected. These algorithms have been broadly studied. They can be divided into two main families: Numerical Model Checking and Statistical Model Checking. The former computes small probabilities accurately by solving linear equation systems, but does not scale to very large systems due to the space size explosion problem. The latter is based on Monte Carlo Simulation and scales well to big systems, but cannot deal with small probabilities. The main contribution of this thesis is the design and implementation of a method combining the two approaches and returning a confidence interval of the probability of interest. This method applies to systems with both continuous and discrete time settings for time-bounded and time-unbounded properties. All the variants of this method rely on an abstraction of the model, this abstraction is analysed by a numerical model checker and the result is used to steer Monte Carlo simulations on the initial model. This abstraction should be small enough to be analysed by numerical methods and precise enough to improve the simulation. This abstraction can be build by the modeller, or alternatively a class of systems can be identified in which an abstraction can be automatically computed. This approach has been implemented in the tool Cosmos, and this method was successfully applied on classical benchmarks and a case study

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Crampton, Raymond J. „A nonlinear statistical MESFET model using low order statistics of equivalent circuit model parameter sets“. Thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-03032009-040420/.

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Chang, Chia-Jung. „Statistical and engineering methods for model enhancement“. Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44766.

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Models which describe the performance of physical process are essential for quality prediction, experimental planning, process control and optimization. Engineering models developed based on the underlying physics/mechanics of the process such as analytic models or finite element models are widely used to capture the deterministic trend of the process. However, there usually exists stochastic randomness in the system which may introduce the discrepancy between physics-based model predictions and observations in reality. Alternatively, statistical models can be used to develop models to obtain predictions purely based on the data generated from the process. However, such models tend to perform poorly when predictions are made away from the observed data points. This dissertation contributes to model enhancement research by integrating physics-based model and statistical model to mitigate the individual drawbacks and provide models with better accuracy by combining the strengths of both models. The proposed model enhancement methodologies including the following two streams: (1) data-driven enhancement approach and (2) engineering-driven enhancement approach. Through these efforts, more adequate models are obtained, which leads to better performance in system forecasting, process monitoring and decision optimization. Among different data-driven enhancement approaches, Gaussian Process (GP) model provides a powerful methodology for calibrating a physical model in the presence of model uncertainties. However, if the data contain systematic experimental errors, the GP model can lead to an unnecessarily complex adjustment of the physical model. In Chapter 2, we proposed a novel enhancement procedure, named as "Minimal Adjustment", which brings the physical model closer to the data by making minimal changes to it. This is achieved by approximating the GP model by a linear regression model and then applying a simultaneous variable selection of the model and experimental bias terms. Two real examples and simulations are presented to demonstrate the advantages of the proposed approach. Different from enhancing the model based on data-driven perspective, an alternative approach is to focus on adjusting the model by incorporating the additional domain or engineering knowledge when available. This often leads to models that are very simple and easy to interpret. The concepts of engineering-driven enhancement are carried out through two applications to demonstrate the proposed methodologies. In the first application where polymer composite quality is focused, nanoparticle dispersion has been identified as a crucial factor affecting the mechanical properties. Transmission Electron Microscopy (TEM) images are commonly used to represent nanoparticle dispersion without further quantifications on its characteristics. In Chapter 3, we developed the engineering-driven nonhomogeneous Poisson random field modeling strategy to characterize nanoparticle dispersion status of nanocomposite polymer, which quantitatively represents the nanomaterial quality presented through image data. The model parameters are estimated through the Bayesian MCMC technique to overcome the challenge of limited amount of accessible data due to the time consuming sampling schemes. The second application is to calibrate the engineering-driven force models of laser-assisted micro milling (LAMM) process statistically, which facilitates a systematic understanding and optimization of targeted processes. In Chapter 4, the force prediction interval has been derived by incorporating the variability in the runout parameters as well as the variability in the measured cutting forces. The experimental results indicate that the model predicts the cutting force profile with good accuracy using a 95% confidence interval. To conclude, this dissertation is the research drawing attention to model enhancement, which has considerable impacts on modeling, design, and optimization of various processes and systems. The fundamental methodologies of model enhancement are developed and further applied to various applications. These research activities developed engineering compliant models for adequate system predictions based on observational data with complex variable relationships and uncertainty, which facilitate process planning, monitoring, and real-time control.

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Shi, Jianqiang. „A trust model with statistical foundation“. Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27038.

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The widespread use of the Internet signals the need for a better understanding of trust as a basis for secure on-line interaction. In the face of increasing uncertainty and risk, users and machines must be allowed to reason effectively about the trustworthiness of other entities. In this thesis, we propose a trust model that assists users and machines with decision-making in online interactions by using past behavior as a predictor of likely future behavior. We develop a general method to automatically compute trust based on self-experience and the recommendations of others. Our trust model solves the problem of recommendation combination and detection of unfair recommendations. Our approach involves data analysis methods (Bayesian estimation, Dirichlet distribution), and machine learning methods (Weighted Majority Algorithm). Furthermore, we apply our trust model to several utility models to increase the accuracy of decision-making in different contexts of Web Services. We describe simulation experiments to illustrate its effectiveness, robustness and the evolution of trust.

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Jabbari, Sanaz. „A Statistical Model of Lexical Context“. Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.521960.

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屠烈偉 und Lit-wai Tao. „Statistical inference on a mixture model“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977480.

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Keogh-Brown, Marcus R. „A statistical model of internet traffic“. Thesis, Queen Mary, University of London, 2003. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1811.

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We present a method to extract a time series (Number of Active Requests (NAR)) from web cache logs which serves as a transport level measurement of internet traffic. This series also reflects the performance or Quality of Service of a web cache. Using time series modelling, we interpret the properties of this kind of internet traffic and its effect on the performance perceived by the cache user. Our preliminary analysis of NAR concludes that this dataset is suggestive of a long-memory self-similar process but is not heavy-tailed. Having carried out more in-depth analysis, we propose a three stage modelling process of the time series: (i) a power transformation to normalise the data, (ii) a polynomial fit to approximate the general trend and (iii) a modelling of the residuals from the polynomial fit. We analyse the polynomial and show that the residual dataset may be modelled as a FARIMA(p, d, q) process. Finally, we use Canonical Variate Analysis to determine the most significant defining properties of our measurements and draw conclusions to categorise the differences in traffic properties between the various caches studied. We show that the strongest illustration of differences between the caches is shown by the short memory parameters of the FARIMA fit. We compare the differences revealed between our studied caches and draw conclusions on them. Several programs have been written in Perl and S programming languages for this analysis including totalqd.pl for NAR calculation, fullanalysis for general statistical analysis of the data and armamodel for FARIMA modelling.

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Predoehl, Andrew. „A Statistical Model of Recreational Trails“. Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/612599.

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We present a statistical model of recreational trails, and a method to infer trail routes from geophysical data, namely aerial imagery and terrain elevation. We learn a set of textures (textons) that characterize the imagery, and use the textons to segment each image into super-pixels. We also model each texton's probability of generating trail pixels, and the direction of such trails. From terrain elevation, we model the magnitude and direction of terrain gradient on-trail and off-trail. These models lead to a likelihood function for image and elevation. Consistent with Bayesian reasoning, we combine the likelihood with a prior model of trail length and smoothness, yielding a posterior distribution for trails, given an image. We search for good values of this posterior using both a novel stochastic variation of Dijkstra's algorithm, and an MCMC-inspired sampler. Our experiments, on trail images and groundtruth collected in the western continental USA, show substantial improvement over those of the previous best trail-finding methods.

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Bücher zum Thema "Statistical model"

1

Fienberg, Stephen E., David C. Hoaglin, William H. Kruskal und Judith M. Tanur, Hrsg. A Statistical Model. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3384-8.

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Bunke, Helga, und Olaf Bunke, Hrsg. Statistical Methods of Model Building: V1. Statistical Inference in Linear Models. Chichester, New York, USA: John Wiley & Sons Ltd, 1986.

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Model selection and model averaging. Cambridge: Cambridge university press, 2008.

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United States. National Telecommunications and Information Administration, Hrsg. Wireless link statistical bit error model. [Washington, D.C.]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 2002.

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Raghem-Moayed, Amir. A mechano-statistical model of fragmentation. Ottawa: National Library of Canada, 1993.

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Dijkstra, Theo K., Hrsg. On Model Uncertainty and its Statistical Implications. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-61564-1.

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United States. National Aeronautics and Space Administration., Hrsg. Asymptotic model analysis and statistical energy analysis. [Washington, DC: National Aeronautics and Space Administration, 1992.

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E, Fienberg Stephen, Hrsg. A Statistical model: Frederick Mosteller's contributions to statistics, science, and public policy. New York: Springer-Verlag, 1990.

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Meijerink, Frits. A nonlinear structural relations model. Leiden: DSWO Press, 1995.

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Lai, Ivan Chung Hang. Aspects of statistical process control and model monitoring. [s.l.]: typescript, 1999.

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Buchteile zum Thema "Statistical model"

1

Dickinson, Jennet Elizabeth. „Statistical Model“. In ATLAS Measurements of the Higgs Boson Coupling to the Top Quark in the Higgs to Diphoton Decay Channel, 65–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86368-5_6.

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Burnham, Kenneth P., und David R. Anderson. „Statistical Theory“. In Model Selection and Inference, 230–314. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2917-7_6.

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Braun, Oleg M., und Yuri S. Kivshar. „Statistical Mechanics“. In The Frenkel-Kontorova Model, 195–242. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-10331-9_6.

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Legay, Axel, Anna Lukina, Louis Marie Traonouez, Junxing Yang, Scott A. Smolka und Radu Grosu. „Statistical Model Checking“. In Lecture Notes in Computer Science, 478–504. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91908-9_23.

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Dawid, Philip, und Stephen Senn. „Statistical Model Selection“. In Simplicity, Complexity and Modelling, 11–33. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119951445.ch2.

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Wu, Jun. „Statistical language model“. In The Beauty of Mathematics in Computer Science, 23–33. Boca Raton, FL : Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315169491-3.

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Yee, Janice G. „The Statistical Model“. In Determinants of Intra-Industry Trade, 63–79. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003249191-4.

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Michael, Christopher, und Mohammed Ismail. „Statistical MOS Model“. In The Kluwer International Series in Engineering and Computer Science, 15–49. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3150-0_3.

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Dorlas, Teunis C. „The Ising Model“. In Statistical Mechanics, 181–94. 2. Aufl. Second edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003037170-34.

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Pardo, Scott. „Models, Models Everywhere…Model Selection“. In Statistical Analysis of Empirical Data, 121–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43328-4_11.

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Konferenzberichte zum Thema "Statistical model"

1

Rodriguez-Echeverria, Roberto, und Fernando Macias. „A statistical analysis approach to assist model transformation evolution“. In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2015. http://dx.doi.org/10.1109/models.2015.7338253.

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Cicirelli, Franco, Christian Nigro und Libero Nigro. „Statistical Model Checking of GSPN Models“. In 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005506700690076.

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Ngo, Van Chan, Axel Legay und Jean Quilbeuf. „Statistical Model Checking for SystemC Models“. In 2016 IEEE 17th International Symposium on High Assurance Systems Engineering (HASE). IEEE, 2016. http://dx.doi.org/10.1109/hase.2016.24.

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Atchariyachanvanich, Kanokwan, Warune Kruaklai, Nattanan Chaipatchareekorn, Nuttavadee Sukteab und Soemsak Yooyen. „A statistical model for estimating statistical contingency fuel“. In 2022 20th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2022. http://dx.doi.org/10.1109/ictke55848.2022.9983084.

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Hansen, Mads Fogtmann, Michael Sass Hansen und Rasmus Larsen. „Conditional statistical model building“. In Medical Imaging, herausgegeben von Joseph M. Reinhardt und Josien P. W. Pluim. SPIE, 2008. http://dx.doi.org/10.1117/12.771079.

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Jamoussi, Salma, David Langlois, Jean-Paul Haton und Kamel Smaili. „Statistical feature language model“. In Interspeech 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-488.

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Reiter, K., und O. Heidbach. „Statistical Stress Model Calibration“. In Second EAGE Workshop on Geomechanics and Energy. Netherlands: EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201414322.

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Eguizabal, Alma, Peter J. Schreier und David Ramirez. „MODEL-ORDER SELECTION IN STATISTICAL SHAPE MODELS“. In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2018. http://dx.doi.org/10.1109/mlsp.2018.8516941.

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Dhar, Deepak. „The abelian sandpile model of self-organized criticality“. In Computer-aided statistical physics. AIP, 1992. http://dx.doi.org/10.1063/1.41946.

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Ito, Nobuyasu. „Monte Carlo analysis of the three-dimensional Ising model“. In Computer-aided statistical physics. AIP, 1992. http://dx.doi.org/10.1063/1.41940.

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Berichte der Organisationen zum Thema "Statistical model"

1

Bass, J. N., N. Grossbard und E. C. Robinson. Statistical Parameters for Describing Model Accuracy. Fort Belvoir, VA: Defense Technical Information Center, März 1989. http://dx.doi.org/10.21236/ada209933.

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Rafanelli, M., und A. Shoshani. STORM: A STatistical Object Representation Model. Office of Scientific and Technical Information (OSTI), November 1989. http://dx.doi.org/10.2172/7055018.

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EASTERLING, ROBERT GENE. Statistical Foundations for Model Validation: Two Papers. Office of Scientific and Technical Information (OSTI), Februar 2003. http://dx.doi.org/10.2172/809985.

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Hansen, Jeffery P., Lutz Wrage, Sagar Chaki, Dionisio de Niz und Mark Klein. Semantic Importance Sampling for Statistical Model Checking. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2014. http://dx.doi.org/10.21236/ada613893.

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Hansen, Jeffery P., Lutz Wrage, Sagar Chaki, Dionisio de Niz und Mark Klein. Semantic Importance Sampling for Statistical Model Checking. Fort Belvoir, VA: Defense Technical Information Center, Januar 2015. http://dx.doi.org/10.21236/ada614107.

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Attia, A. V. A review of the kinetic statistical strength model. Office of Scientific and Technical Information (OSTI), März 1996. http://dx.doi.org/10.2172/221881.

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Larson, Edward C., B. E. Parker, Poor Jr. und H. V. Multivariate Nonparametric Statistical Techniques for Simulation Model Validation. Fort Belvoir, VA: Defense Technical Information Center, Oktober 1997. http://dx.doi.org/10.21236/ada330896.

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Tillmann, Christopher. A Unigram Orientation Model for Statistical Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, Januar 2004. http://dx.doi.org/10.21236/ada460258.

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Blair, Peter, und Bobby Chung. A Model of Occupational Licensing and Statistical Discrimination. Cambridge, MA: National Bureau of Economic Research, Dezember 2020. http://dx.doi.org/10.3386/w28227.

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Shibata, K., und C. Y. Fu. Recent improvements of the TNG statistical model code. Office of Scientific and Technical Information (OSTI), August 1986. http://dx.doi.org/10.2172/5411068.

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