Tesis sobre el tema "Model selection"
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Selén, Yngve. "Model selection /". Uppsala : Univ. : Dept. of Information Technology, Univ, 2004. http://www.it.uu.se/research/reports/lic/2004-003/.
Texto completoSelén, Yngve. "Model Selection". Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-86308.
Texto completoEvers, Ludger. "Model fitting and model selection for 'mixture of experts' models". Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445776.
Texto completoBillah, Baki 1965. "Model selection for time series forecasting models". Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8840.
Texto completoYoshimura, Arihiro. "Essays on Semiparametric Model Selection and Model Averaging". Kyoto University, 2015. http://hdl.handle.net/2433/199059.
Texto completoPENG, SISI. "Evaluating Automatic Model Selection". Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154449.
Texto completoBello, Bernardo. "PROCESS MANUFACTURING SELECTION MODEL". Thesis, KTH, Industriell produktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218031.
Texto completoAasberg, Pipirs Freddy y Patrik Svensson. "Tenancy Model Selection Guidelines". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235716.
Texto completoSoftware as a Service (SaaS) är en delmängd av molntjänster där en tjänsteleverantör tillgodoser mjukvara som en tjänst åt kunder. SaaS-applikationen installeras på SaaS-leverantörens servrar, och åtkomsten till applikationen sker oftast via webbläsaren. I sammanhanget av SaaS kallas en kund för ten-ant, vilket oftast består av en organisation, eller i vissa fall enbart av en användare. En SaaS-applikation kan delas in i tenancy-modeller. En tenancymodell beskriver hur en tenant:s data är associerad till lagringsutrymmet på SaaS-leverantörens server.Efter att ha gjort en förstudie kunde författarna dra slutsatsen att det råder guidningsbrist för val av tenancy-modeller. Syftet med denna tes är att tillgodose vägledning för val av tenancy-modeller. Kortsiktsmålet är att skapa en guide för val av tenancy-modeller. Långsiktsmålet är att tillgodose forskare och studenter med forskningsmaterial. Denna tes tillgodoser en modell för guidning av val för tenancy-modeller. Namnet på denna guide är textitTenancy Model Selection Guidelines (TMSG).TMSG utvärderades genom intervjuer med två personer som jobbar inom mjukvaru-branschen. Kriterierna som användes vid utvärderingen av TMSG var följande: Trovärdighet hos den intervjuade personen, Syntaktisk korrekthet, Semantisk korrekthet, Användbarhet och Modellens flexibilitet. I båda intervjuerna ansåg de medverkande att TMSG behöver ytterligare finslipning, och de var båda positiva till det uppnådda resultatet.
Belitz, Christiane. "Model Selection in Generalised Structured Additive Regression Models". Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-78896.
Texto completoSommer, Julia. "Regularized estimation and model selection in compartment models". Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-157673.
Texto completoSmith, Peter William Frederick. "Edge exclusion and model selection in graphical models". Thesis, Lancaster University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315138.
Texto completoGuo, Yixuan. "Bayesian Model Selection for Poisson and Related Models". University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310177.
Texto completoDey, Tanujit. "Prediction and Variable Selection". Cleveland, Ohio : Case Western Reserve University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1212581055.
Texto completoSelén, Yngve. "Model selection and sparse modeling /". Uppsala : Department of Information Technology, Uppsala University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8202.
Texto completoVinciotti, Veronica. "Model selection in supervised classification". Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.397929.
Texto completoSassoon, 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.
Texto completoGrosse, Roger Baker. "Model selection in compositional spaces". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87789.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 172-181).
We often build complex probabilistic models by composing simpler models-using one model to generate parameters or latent variables for another model. This allows us to express complex distributions over the observed data and to share statistical structure between dierent parts of a model. In this thesis, we present a space of matrix decomposition models defined by the composition of a small number of motifs of probabilistic modeling, including clustering, low rank factorizations, and binary latent factor models. This compositional structure can be represented by a context-free grammar whose production rules correspond to these motifs. By exploiting the structure of this grammar, we can generically and eciently infer latent components and estimate predictive likelihood for nearly 2500 model structures using a small toolbox of reusable algorithms. Using a greedy search over this grammar, we automatically choose the decomposition structure from raw data by evaluating only a small fraction of all models. The proposed method typically finds the correct structure for synthetic data and backs o gracefully to simpler models under heavy noise. It learns sensible structures for datasets as diverse as image patches, motion capture, 20 Questions, and U.S. Senate votes, all using exactly the same code. We then consider several improvements to compositional structure search. We present compositional importance sampling (CIS), a novel procedure for marginal likelihood estimation which requires only posterior inference and marginal likelihood estimation algorithms corresponding to the production rules of the grammar. We analyze the performance of CIS in the case of identifying additional structure within a low-rank decomposition. This analysis yields insights into how one should design a space of models to be recursively searchable. We next consider the problem of marginal likelihood estimation for the production rules. We present a novel method for obtaining ground truth marginal likelihood values on synthetic data, which enables the rigorous quantitative comparison of marginal likelihood estimators. Using this method, we compare a wide variety of marginal likelihood estimators for the production rules of our grammar. Finally, we present a framework for analyzing the sequences of distributions used in annealed importance sampling, a state-of-the-art marginal likelihood estimator, and present a novel sequence of intermediate distributions based on averaging moments of the initial and target distributions.
by Roger Baker Grosse.
Ph. D.
Velasco-Cruz, Ciro. "Spatially Correlated Model Selection (SCOMS)". Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/27791.
Texto completoPh. D.
You, Di. "Model Selection in Kernel Methods". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322581224.
Texto completoOsaka, Haruki. "Asymptotics of Mixture Model Selection". Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27230.
Texto completoJIANG, DONGMING. "OBJECTIVE BAYESIAN TESTING AND MODEL SELECTION FOR POISSON MODELS". University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1185821399.
Texto completoLiu, Tuo. "Model Selection and Adaptive Lasso Estimation of Spatial Models". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500379101560737.
Texto completoWu, Jingwen. "Model-based clustering and model selection for binned data". Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0005/document.
Texto completoThis thesis studies the Gaussian mixture model-based clustering approaches and the criteria of model selection for binned data clustering. Fourteen binned-EM algorithms and fourteen bin-EM-CEM algorithms are developed for fourteen parsimonious Gaussian mixture models. These new algorithms combine the advantages in computation time reduction of binning data and the advantages in parameters estimation simplification of parsimonious Gaussian mixture models. The complexities of the binned-EM and the bin-EM-CEM algorithms are calculated and compared to the complexities of the EM and the CEM algorithms respectively. In order to select the right model which fits well the data and satisfies the clustering precision requirements with a reasonable computation time, AIC, BIC, ICL, NEC, and AWE criteria, are extended to binned data clustering when the proposed binned-EM and bin-EM-CEM algorithms are used. The advantages of the different proposed methods are illustrated through experimental studies
Lu, Pingbo. "Calibrated Bayes factors for model selection and model averaging". The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343396705.
Texto completoSchnücker, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker". Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.
Texto completoLipkovich, Ilya A. "Bayesian Model Averaging and Variable Selection in Multivariate Ecological Models". Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/11045.
Texto completoPh. D.
Camehl, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker". Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.
Texto completoDhurandhar, Amit. "Semi-analytical method for analyzing models and model selection measures". [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024733.
Texto completoGoşoniu, Nicoleta Francisca. "On model selection in additive regression /". Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17637.
Texto completoLui, Hon-kwong y 呂漢光. "An econometric model of spouse selection". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B30110750.
Texto completoLuo, Ye Ph D. Massachusetts Institute of Technology. "High-dimensional econometrics and model selection". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98686.
Texto completoTitle as it appears in MIT Commencement Exercises program, June 5, 2015: Essays in high-dimensional econometrics and model selection. Cataloged from PDF version of thesis.
Includes bibliographical references.
This dissertation consists of three chapters. Chapter 1 proposes a new method to solve the many moment problem: in Generalized Method of Moments (GMM), when the number of moment conditions is comparable to or larger than the sample size, the traditional methods lead to biased estimators. We propose a LASSO based selection procedure in order to choose the informative moments and then, using the selected moments, conduct optimal GMM. My method can significantly reduce the bias of the optimal GMM estimator while retaining most of the information in the full set of moments. We establish theoretical asymptotics of the LASSO and post-LASSO estimators. The formulation of LASSO is a convex optimization problem and thus the computational cost is low compared to all existing alternative moment selection procedures. We propose penalty terms using data-driven methods, of which the calculation is carried out by a non-trivial adaptive algorithm. In Chapter 2, we consider partially identified models with many inequalities. Under such circumstances, existing inference procedures may break down asymptotically and are computationally difficult to conduct. We first propose a combinatorial method to select the informative inequalities in the Core Determining Class problem, in which a large set of linear inequalities are generated from a bipartite graph. Our method selects the set of irredudant inequalities and outperforms all existing methods in shrinking the number of inequalities and computational speed. We further consider a more general problem with many linear inequalities. We propose an inequality selection method similar to the Dantzig selector. We establish theoretical results of such a selection method under our sparsity assumptions. Chapter 3 proposes an innovative way of reporting results in empirical analysis of economic data. Instead of reporting the Average Partial Effect, we propose to report multiple effects sorted in increasing order, as an alternative and more complete summary measure of the heterogeneity in the model. We established asymptotics and inference for such a procedure via functional delta method. Numerical examples and an empirical application to female labor supply using data from the 1980 U.S. Census illustrate the performance of our methods in finite samples.
by Ye Luo.
Chapter 1. Chapter 2. Chapter 3. Selecting informative moments via LASSO -- Core determining class : construction, approximation, and inference -- Summarizing partial effects beyond averages.
Ph. D.
McGrory, Clare Anne. "Variational approximations in Bayesian model selection". Thesis, University of Glasgow, 2005. http://theses.gla.ac.uk/6941/.
Texto completoArledge, Christopher S. "Cosmological Model Selection and Akaike’s Criterion". Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1430478203.
Texto completoLui, Hon-kwong. "An econometric model of spouse selection /". Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B16027450.
Texto completoZhang, Tao. "Discrepancy-based algorithms for best-subset model selection". Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/4800.
Texto completoMaiti, Dipayan. "Multiset Model Selection and Averaging, and Interactive Storytelling". Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28563.
Texto completoPh. D.
Wenren, Cheng. "Mixed Model Selection Based on the Conceptual Predictive Statistic". Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1403735738.
Texto completoPan, Juming. "Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood". Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921.
Texto completoSmith, Andrew Korb. "New results in dimension reduction and model selection". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22586.
Texto completoCommittee Chair: Huo, Xiaoming; Committee Member: Serban, Nicoleta; Committee Member: Shapiro, Alexander; Committee Member: Yuan, Ming; Committee Member: Zha, Hongyuan.
Sommer, Julia C. [Verfasser]. "Regularized estimation and model selection in compartment models / Julia C. Sommer". München : Verlag Dr. Hut, 2013. http://d-nb.info/1037286790/34.
Texto completoBakir, Mehmet Emin. "Automatic selection of statistical model checkers for analysis of biological models". Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/20216/.
Texto completoSmith, Connor James. "Resampling Based Model Selection for Correlated and Complex Data". Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/27428.
Texto completoMu, He Qing. "Bayesian model class selection on regression problems". Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2492988.
Texto completoColeman, Kimberley. "A new capture-recapture model selection criterion /". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101841.
Texto completoNing, Hoi-Kwan Flora. "Model-based regression clustering with variable selection". Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497059.
Texto completoHildebrand, Annelize. "Model selection". Diss., 1995. http://hdl.handle.net/10500/16951.
Texto completoMathematical Sciences
M. Sc. (Statistics)
Hu, Chin-Yen y 胡智彥. "Model selection for two part models". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/14583065088945422872.
Texto completo國立雲林科技大學
財務金融系
90
In this study, we want to identify the robust model in different distribution data, especially in facing censored variables or Tobit —like variables. In order to verify this thought, we choose two competitive models: lognormal model and Cragg’s model for examination. With two different kinds distribution simulated data, we use Voung’s model selection tests for two competitive hurdle, or two-tier models. In these simulated data, we find out that Cragg’s model will be more robust than lognormal model. So, we take it to compare with the traditional Tobit model, for another suggestion when researching the R&D expenditure. After testing through KLIC rule with real data, we can find that the Cragg’s model is more suitable than Tobit’s model in this real data set.
江支耀. "Model selection in regression models with heteroscedasticity". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/27534234750910615312.
Texto completoChang, Le. "Essays on Robust Model Selection and Model Averaging for Linear Models". Phd thesis, 2017. http://hdl.handle.net/1885/139176.
Texto completoWu, Ming-chuan y 吳旻娟. "Developing a selection model for logistics strategy selection". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/29460189068441130214.
Texto completo國立高雄第一科技大學
運輸倉儲營運所
93
In recent years, as rapid change for industrial structure and higher consuming ability, the needs for logistics become more important than before. For all of the enterprise, logistics operations’ ability of a company becomes an important factor for the success of the company. My research defines that the logistics strategy is the development way for the enterprise executes the logistics activity chooses. My research use Fuzzy AHP approach to develop a strategic model for logistics strategy selection to obtains each logistics strategy consideration factor aspects and the indicators weight value, and combine Fuzzy Synthetic Evaluation Model to calculate overall evaluation of this alternative way for the logistics strategy, which help decision makers to have the results of each way by use of these systematic indicators. Not only can we obtain the evaluation of each strategy, but also the result of each indicator in every aspect. In the last, adjuvant with statistic tests to further get the idea of variables of the enterprise’s character, consideration factors of the logistics strategy, and the relationship between logistics strategy execution factors. The main contributions of this research are to classify and reorganize the related literature of logistics strategy and purpose the view of logistics strategy classification. My research hope to assist enterprises to make future considerations and references in logistics strategy related decisions.