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Dissertations / Theses on the topic 'Model selection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Selén, Yngve. "Model selection /." Uppsala : Univ. : Dept. of Information Technology, Univ, 2004. http://www.it.uu.se/research/reports/lic/2004-003/.

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2

Selén, Yngve. "Model Selection." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-86308.

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Before using a parametric model one has to be sure that it offers a reasonable description of the system to be modeled. If a bad model structure is employed, the obtained model will also be bad, no matter how good is the parameter estimation method. There exist many possible ways of validating candidate models. This thesis focuses on one of the most common ways, i.e., the use of information criteria. First, some common information criteria are presented, and in the later chapters, various extentions and implementations are shown. An important extention, which is advocated in the thesis, is the

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3

Evers, 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.

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4

Billah, 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.

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5

Yoshimura, Arihiro. "Essays on Semiparametric Model Selection and Model Averaging." Kyoto University, 2015. http://hdl.handle.net/2433/199059.

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6

PENG, SISI. "Evaluating Automatic Model Selection." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154449.

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In this paper, we briefly describe the automatic model selection which is provided by Autometrics in the PcGive program. The modeler only needs to specify the initial model and the significance level at which to reduce the model. Then, the algorithm does the rest. The properties of Autometrics are discussed. We also explain its background concepts and try to see whether the model selected by the Autometrics can perform well. For a given data set, we use Autometrics to find a “new” model, and then compare the “new” model with a previously selected one by another modeler. It is an interesting is

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7

Bello, Bernardo. "PROCESS MANUFACTURING SELECTION MODEL." Thesis, KTH, Industriell produktion, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218031.

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8

Aasberg, Pipirs Freddy, and 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.

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Software as a Service (SaaS) is a subset of cloud services where a vendor provides software as a service to customers. The SaaS application is installed on the SaaS provider’s servers, and is often accessed via the web browser. In the context of SaaS, a customer is called tenant, which often is an organization that is accessing the SaaS application, but it could also be a single individual. A SaaS application can be classified into tenancy models. A tenancy model describes how a tenant’s data is mapped to the storage on the server-side of the SaaS application.By doing a research, the authors h

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9

Belitz, Christiane. "Model Selection in Generalised Structured Additive Regression Models." Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-78896.

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10

Sommer, 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.

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Dynamic imaging series acquired in medical and biological research are often analyzed with the help of compartment models. Compartment models provide a parametric, nonlinear function of interpretable, kinetic parameters describing how some concentration of interest evolves over time. Aiming to estimate the kinetic parameters, this leads to a nonlinear regression problem. In many applications, the number of compartments needed in the model is not known from biological considerations but should be inferred from the data along with the kinetic parameters. As data from medical and biological exper

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11

Smith, 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.

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12

Guo, Yixuan. "Bayesian Model Selection for Poisson and Related Models." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310177.

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13

Dey, Tanujit. "Prediction and Variable Selection." Cleveland, Ohio : Case Western Reserve University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1212581055.

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14

Selén, 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.

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15

Vinciotti, Veronica. "Model selection in supervised classification." Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.397929.

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16

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

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17

Grosse, Roger Baker. "Model selection in compositional spaces." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87789.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 172-181).<br>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 obser

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18

Velasco-Cruz, Ciro. "Spatially Correlated Model Selection (SCOMS)." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/27791.

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In this dissertation, a variable selection method for spatial data is developed. It is assumed that the spatial process is non-stationary as a whole but is piece-wise stationary. The pieces where the spatial process is stationary are called regions. The variable selection approach accounts for two sources of correlation: (1) the spatial correlation of the data within the regions, and (2) the correlation of adjacent regions. The variable selection is carried out by including indicator variables that characterize the significance of the regression coefficients. The Ising distribution as prior f

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19

You, Di. "Model Selection in Kernel Methods." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322581224.

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20

Osaka, Haruki. "Asymptotics of Mixture Model Selection." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27230.

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In this thesis, we consider the likelihood ratio test (LRT) when testing for homogeneity in a three component normal mixture model. It is well-known that the LRT in this setting exhibits non-standard asymptotic behaviour, due to non-identifiability of the model parameters and possible degeneracy of Fisher Information matrix. In fact, Liu and Shao (2004) showed that for the test of homogeneity in a two component normal mixture model with a single fixed component, the limiting distribution is an extreme value Gumbel distribution under the null hypothesis, rather than the usual chi-squared distri

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21

JIANG, DONGMING. "OBJECTIVE BAYESIAN TESTING AND MODEL SELECTION FOR POISSON MODELS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1185821399.

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22

Liu, Tuo. "Model Selection and Adaptive Lasso Estimation of Spatial Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1500379101560737.

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23

Wu, Jingwen. "Model-based clustering and model selection for binned data." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0005/document.

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Cette thèse étudie les approches de classification automatique basées sur les modèles de mélange gaussiens et les critères de choix de modèles pour la classification automatique de données discrétisées. Quatorze algorithmes binned-EM et quatorze algorithmes bin-EM-CEM sont développés pour quatorze modèles de mélange gaussiens parcimonieux. Ces nouveaux algorithmes combinent les avantages des données discrétisées en termes de réduction du temps d’exécution et les avantages des modèles de mélange gaussiens parcimonieux en termes de simplification de l'estimation des paramètres. Les complexités d

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24

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.

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25

Schnücker, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.

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26

Lipkovich, Ilya A. "Bayesian Model Averaging and Variable Selection in Multivariate Ecological Models." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/11045.

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Bayesian Model Averaging (BMA) is a new area in modern applied statistics that provides data analysts with an efficient tool for discovering promising models and obtaining esti-mates of their posterior probabilities via Markov chain Monte Carlo (MCMC). These probabilities can be further used as weights for model averaged predictions and estimates of the parameters of interest. As a result, variance components due to model selection are estimated and accounted for, contrary to the practice of conventional data analysis (such as, for example, stepwise model selection). In addition, variable acti

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27

Camehl, Annika [Verfasser]. "Model Selection Methods for Panel Vector Autoregressive Models / Annika Schnücker." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176708147/34.

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28

Dhurandhar, Amit. "Semi-analytical method for analyzing models and model selection measures." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024733.

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29

Goşoniu, Nicoleta Francisca. "On model selection in additive regression /." Zürich : ETH, 2008. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17637.

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30

Lui, Hon-kwong, and 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.

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31

Luo, 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.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Economics, 2015.<br>Title 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.<br>Includes bibliographical references.<br>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 prop

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32

McGrory, Clare Anne. "Variational approximations in Bayesian model selection." Thesis, University of Glasgow, 2005. http://theses.gla.ac.uk/6941/.

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The research presented in this thesis is on the topic of the Bayesian approach to statistical inference. In particular it focuses on the analysis of mixture models. Mixture models are a useful tool for representing complex data and are widely applied in many areas of statistics (see, for example, Titterington et al. (1985)). The representation of mixture models as missing data models is often useful as it makes more techniques of inference available to us. In addition, it allows us to introduce further dependencies within the mixture model hierarchy leading to the definition of the hidden Mark

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33

Arledge, Christopher S. "Cosmological Model Selection and Akaike’s Criterion." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1430478203.

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34

Lui, Hon-kwong. "An econometric model of spouse selection /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B16027450.

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35

Zhang, Tao. "Discrepancy-based algorithms for best-subset model selection." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/4800.

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The selection of a best-subset regression model from a candidate family is a common problem that arises in many analyses. In best-subset model selection, we consider all possible subsets of regressor variables; thus, numerous candidate models may need to be fit and compared. One of the main challenges of best-subset selection arises from the size of the candidate model family: specifically, the probability of selecting an inappropriate model generally increases as the size of the family increases. For this reason, it is usually difficult to select an optimal model when best-subset selection is

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36

Maiti, Dipayan. "Multiset Model Selection and Averaging, and Interactive Storytelling." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28563.

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The Multiset Sampler [Leman et al., 2009] has previously been deployed and developed for efficient sampling from complex stochastic processes. We extend the sampler and the surrounding theory to model selection problems. In such problems efficient exploration of the model space becomes a challenge since independent and ad-hoc proposals might not be able to jointly propose multiple parameter sets which correctly explain a new pro- posed model. In order to overcome this we propose a multiset on the model space to en- able efficient exploration of multiple model modes with almost no tuning. The M

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37

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.

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38

Pan, 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.

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39

Smith, Andrew Korb. "New results in dimension reduction and model selection." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22586.

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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2008.<br>Committee Chair: Huo, Xiaoming; Committee Member: Serban, Nicoleta; Committee Member: Shapiro, Alexander; Committee Member: Yuan, Ming; Committee Member: Zha, Hongyuan.

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40

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.

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41

Bakir, Mehmet Emin. "Automatic selection of statistical model checkers for analysis of biological models." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/20216/.

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Statistical Model Checking (SMC) blends the speed of simulation with the rigorous analytical capabilities of model checking, and its success has prompted researchers to implement a number of SMC tools whose availability provides flexibility and fine-tuned control over model analysis. However, each tool has its own practical limitations, and different tools have different requirements and performance characteristics. The performance of different tools may also depend on the specific features of the input model or the type of query to be verified. Consequently, choosing the most suitable tool fo

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42

Smith, Connor James. "Resampling Based Model Selection for Correlated and Complex Data." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/27428.

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Variable selection is a key component of regression modelling but slight changes to the initial data can result in changes to the models identified. In this thesis, we identify and examine multiple problems within the variable selection space and how through the use of stability based approaches we can construct solutions, where there is a current lack of statistical frameworks. At its core, this thesis tackles complex data in a generalized linear model (GLM) framework; both in robust and higher dimensional settings. We target three main aspects: - The inability to use exhaustive variable

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43

Mu, He Qing. "Bayesian model class selection on regression problems." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2492988.

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44

Coleman, 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.

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Capture-recapture methods are used to estimate population size from overlapping, incomplete sources of information. With three or more sources, dependence between sources may be modelled using log-linear models. We propose a Coefficient of Incremental Dependence Criterion (CIDC) for selecting an estimate of population size among all possible estimates that result from hierarchical log-linear models. A penalty for the number of parameters in the model was selected via simulation for the three-source and four-source settings. The performance of the proposed criterion was compared to the Akaike I

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45

Ning, 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.

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46

Hildebrand, Annelize. "Model selection." Diss., 1995. http://hdl.handle.net/10500/16951.

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In developing an understanding of real-world problems, researchers develop mathematical and statistical models. Various model selection methods exist which can be used to obtain a mathematical model that best describes the real-world situation in some or other sense. These methods aim to assess the merits of competing models by concentrating on a particular criterion. Each selection method is associated with its own criterion and is named accordingly. The better known ones include Akaike's Information Criterion, Mallows' Cp and cross-validation, to name a few. The value of the criteri

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47

Hu, Chin-Yen, and 胡智彥. "Model selection for two part models." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/14583065088945422872.

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碩士<br>國立雲林科技大學<br>財務金融系<br>90<br>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, fo

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48

江支耀. "Model selection in regression models with heteroscedasticity." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/27534234750910615312.

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49

Chang, Le. "Essays on Robust Model Selection and Model Averaging for Linear Models." Phd thesis, 2017. http://hdl.handle.net/1885/139176.

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Model selection is central to all applied statistical work. Selecting the variables for use in a regression model is one important example of model selection. This thesis is a collection of essays on robust model selection procedures and model averaging for linear regression models. In the first essay, we propose robust Akaike information criteria (AIC) for MM-estimation and an adjusted robust scale based AIC for M and MM-estimation. Our proposed model selection criteria can maintain their robust properties in the presence of a high proportion

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50

Wu, Ming-chuan, and 吳旻娟. "Developing a selection model for logistics strategy selection." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/29460189068441130214.

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碩士<br>國立高雄第一科技大學<br>運輸倉儲營運所<br>93<br>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 as

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