Relevant bibliographies by topics / Models evaluation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Models evaluation'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Models evaluation"

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Stufflebeam, Daniel. "Evaluation Models." New Directions for Evaluation 2001, no. 89 (2001): 7. http://dx.doi.org/10.1002/ev.3.

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Contandriopoulos, Damien, and Astrid Brousselle. "Evaluation models and evaluation use." Evaluation 18, no. 1 (January 2012): 61–77. http://dx.doi.org/10.1177/1356389011430371.

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Albers, J., S. Hassfeld, and C. F. Vahl. "EVALUATION OF MODELS." Biomedizinische Technik/Biomedical Engineering 47, s1b (2002): 919–22. http://dx.doi.org/10.1515/bmte.2002.47.s1b.919.

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Hansen, Hanne Foss. "Choosing Evaluation Models." Evaluation 11, no. 4 (October 2005): 447–62. http://dx.doi.org/10.1177/1356389005060265.

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Gandomkar, Roghayeh, Mohammad Jalili, and Azim Mirzazadeh. "Evaluating assessment programmes using programme evaluation models." Medical Teacher 37, no. 8 (May 29, 2015): 792–93. http://dx.doi.org/10.3109/0142159x.2015.1042436.

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Gallegos, Arnold. "Meta-evaluation of school evaluation models." Studies in Educational Evaluation 20, no. 1 (January 1994): 41–54. http://dx.doi.org/10.1016/s0191-491x(00)80004-8.

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Ricolfe-Viala, Carlos, and Antonio-Jose Sanchez-Salmeron. "Lens distortion models evaluation." Applied Optics 49, no. 30 (October 19, 2010): 5914. http://dx.doi.org/10.1364/ao.49.005914.

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Wagener, Thorsten. "Evaluation of catchment models." Hydrological Processes 17, no. 16 (2003): 3375–78. http://dx.doi.org/10.1002/hyp.5158.

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House, E. R. "Economic models of evaluation." Scandinavian Journal of Social Welfare 7, no. 2 (April 1998): 110–13. http://dx.doi.org/10.1111/j.1468-2397.1998.tb00210.x.

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Granger, Clive W. J., and Yongil Jeon. "Evaluation of global models." Economic Modelling 24, no. 6 (November 2007): 980–89. http://dx.doi.org/10.1016/j.econmod.2007.03.008.

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Dissertations / Theses on the topic "Models evaluation"

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Sawade, Christoph. "Active evaluation of predictive models." Phd thesis, Universität Potsdam, 2012. http://opus.kobv.de/ubp/volltexte/2013/6558/.

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The field of machine learning studies algorithms that infer predictive models from data. Predictive models are applicable for many practical tasks such as spam filtering, face and handwritten digit recognition, and personalized product recommendation. In general, they are used to predict a target label for a given data instance. In order to make an informed decision about the deployment of a predictive model, it is crucial to know the model’s approximate performance. To evaluate performance, a set of labeled test instances is required that is drawn from the distribution the model will be exposed to at application time. In many practical scenarios, unlabeled test instances are readily available, but the process of labeling them can be a time- and cost-intensive task and may involve a human expert. This thesis addresses the problem of evaluating a given predictive model accurately with minimal labeling effort. We study an active model evaluation process that selects certain instances of the data according to an instrumental sampling distribution and queries their labels. We derive sampling distributions that minimize estimation error with respect to different performance measures such as error rate, mean squared error, and F-measures. An analysis of the distribution that governs the estimator leads to confidence intervals, which indicate how precise the error estimation is. Labeling costs may vary across different instances depending on certain characteristics of the data. For instance, documents differ in their length, comprehensibility, and technical requirements; these attributes affect the time a human labeler needs to judge relevance or to assign topics. To address this, the sampling distribution is extended to incorporate instance-specific costs. We empirically study conditions under which the active evaluation processes are more accurate than a standard estimate that draws equally many instances from the test distribution. We also address the problem of comparing the risks of two predictive models. The standard approach would be to draw instances according to the test distribution, label the selected instances, and apply statistical tests to identify significant differences. Drawing instances according to an instrumental distribution affects the power of a statistical test. We derive a sampling procedure that maximizes test power when used to select instances, and thereby minimizes the likelihood of choosing the inferior model. Furthermore, we investigate the task of comparing several alternative models; the objective of an evaluation could be to rank the models according to the risk that they incur or to identify the model with lowest risk. An experimental study shows that the active procedure leads to higher test power than the standard test in many application domains. Finally, we study the problem of evaluating the performance of ranking functions, which are used for example for web search. In practice, ranking performance is estimated by applying a given ranking model to a representative set of test queries and manually assessing the relevance of all retrieved items for each query. We apply the concepts of active evaluation and active comparison to ranking functions and derive optimal sampling distributions for the commonly used performance measures Discounted Cumulative Gain and Expected Reciprocal Rank. Experiments on web search engine data illustrate significant reductions in labeling costs.
Maschinelles Lernen befasst sich mit Algorithmen zur Inferenz von Vorhersagemodelle aus komplexen Daten. Vorhersagemodelle sind Funktionen, die einer Eingabe – wie zum Beispiel dem Text einer E-Mail – ein anwendungsspezifisches Zielattribut – wie „Spam“ oder „Nicht-Spam“ – zuweisen. Sie finden Anwendung beim Filtern von Spam-Nachrichten, bei der Text- und Gesichtserkennung oder auch bei der personalisierten Empfehlung von Produkten. Um ein Modell in der Praxis einzusetzen, ist es notwendig, die Vorhersagequalität bezüglich der zukünftigen Anwendung zu schätzen. Für diese Evaluierung werden Instanzen des Eingaberaums benötigt, für die das zugehörige Zielattribut bekannt ist. Instanzen, wie E-Mails, Bilder oder das protokollierte Nutzerverhalten von Kunden, stehen häufig in großem Umfang zur Verfügung. Die Bestimmung der zugehörigen Zielattribute ist jedoch ein manueller Prozess, der kosten- und zeitaufwendig sein kann und mitunter spezielles Fachwissen erfordert. Ziel dieser Arbeit ist die genaue Schätzung der Vorhersagequalität eines gegebenen Modells mit einer minimalen Anzahl von Testinstanzen. Wir untersuchen aktive Evaluierungsprozesse, die mit Hilfe einer Wahrscheinlichkeitsverteilung Instanzen auswählen, für die das Zielattribut bestimmt wird. Die Vorhersagequalität kann anhand verschiedener Kriterien, wie der Fehlerrate, des mittleren quadratischen Verlusts oder des F-measures, bemessen werden. Wir leiten die Wahrscheinlichkeitsverteilungen her, die den Schätzfehler bezüglich eines gegebenen Maßes minimieren. Der verbleibende Schätzfehler lässt sich anhand von Konfidenzintervallen quantifizieren, die sich aus der Verteilung des Schätzers ergeben. In vielen Anwendungen bestimmen individuelle Eigenschaften der Instanzen die Kosten, die für die Bestimmung des Zielattributs anfallen. So unterscheiden sich Dokumente beispielsweise in der Textlänge und dem technischen Anspruch. Diese Eigenschaften beeinflussen die Zeit, die benötigt wird, mögliche Zielattribute wie das Thema oder die Relevanz zuzuweisen. Wir leiten unter Beachtung dieser instanzspezifischen Unterschiede die optimale Verteilung her. Die entwickelten Evaluierungsmethoden werden auf verschiedenen Datensätzen untersucht. Wir analysieren in diesem Zusammenhang Bedingungen, unter denen die aktive Evaluierung genauere Schätzungen liefert als der Standardansatz, bei dem Instanzen zufällig aus der Testverteilung gezogen werden. Eine verwandte Problemstellung ist der Vergleich von zwei Modellen. Um festzustellen, welches Modell in der Praxis eine höhere Vorhersagequalität aufweist, wird eine Menge von Testinstanzen ausgewählt und das zugehörige Zielattribut bestimmt. Ein anschließender statistischer Test erlaubt Aussagen über die Signifikanz der beobachteten Unterschiede. Die Teststärke hängt von der Verteilung ab, nach der die Instanzen ausgewählt wurden. Wir bestimmen die Verteilung, die die Teststärke maximiert und damit die Wahrscheinlichkeit minimiert, sich für das schlechtere Modell zu entscheiden. Des Weiteren geben wir eine Möglichkeit an, den entwickelten Ansatz für den Vergleich von mehreren Modellen zu verwenden. Wir zeigen empirisch, dass die aktive Evaluierungsmethode im Vergleich zur zufälligen Auswahl von Testinstanzen in vielen Anwendungen eine höhere Teststärke aufweist. Im letzten Teil der Arbeit werden das Konzept der aktiven Evaluierung und das des aktiven Modellvergleichs auf Rankingprobleme angewendet. Wir leiten die optimalen Verteilungen für das Schätzen der Qualitätsmaße Discounted Cumulative Gain und Expected Reciprocal Rank her. Eine empirische Studie zur Evaluierung von Suchmaschinen zeigt, dass die neu entwickelten Verfahren signifikant genauere Schätzungen der Rankingqualität liefern als die untersuchten Referenzverfahren.
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Egilsson, Guðlaugur Stefán. "Event Models : An Evaluation Framework." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-389.

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Event based programming is an important metaphor used in a variety of applications. Research and practice in this field comes primarily from two distinct sources, component software and databases. In both those fields, the need for asynchronous notifications has led to the specification and implementation of event models, but with somewhat different emphasis.

This dissertation defines an evaluation framework for evaluating event models. In doing so, it defines several factors that are important when reviewing different event models with respect to implementing applications or components that require event notification mechanisms.

It has been suggested that the event models defined for COM and CORBA can each be used as the basis for implementing advanced event services. The framework presented in this dissertation is used to evaluate these two event models with respect to their capability to support an advanced event service originating from active database research.

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Martinez, Baquero Guillermo Felipe. "Diagnostic Evaluation of Watershed Models." Thesis, The University of Arizona, 2007. http://hdl.handle.net/10150/193357.

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With increasing model complexity there is a pressing need for new methods that can be used to mine information from large volumes of model results and available data. This work explores strategies to identify and evaluate the causes of discrepancy between models and data related to hydrologic processes, and to increase our knowledge about watershed input-output relationships. In this context, we evaluate the performance of the abcd monthly water balance model for 764 watersheds in the conterminous United States. The work required integration of the Hydro-Climatic Data Network dataset with various kinds of spatial information, and a diagnostic approach to relating model performance with assumptions and characteristics of the basins. The diagnostic process was implemented via classification of watersheds, evaluation of hydrologic signatures and the identification of dominant processes. Knowledge acquired during this process was used to test modifications of the model for hydrologic regions where the performance was "poor".
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Sgherri, Silvia. "Policy evaluation with macroeconometric models." Thesis, University of Warwick, 2000. http://wrap.warwick.ac.uk/4154/.

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This thesis presents a number of examples where macroeconometric models are employed as useful tools for evaluation of contemporary policy problems. A range of approaches is proposed to shed light on how macromodels can actually contribute to the policy debate. In particular, the thesis emphasises how different models maybe augmented or modified and stresses the need for care in the experimental design of policy simulations. Small stylised models of the UK economy are estimated in the first part of this thesis. They are used to assess the performance of simple monetary policy rules under the current inflation targeting monetary regime. In a monetary policy regime of inflation targeting, the appropriate target band-width can be assessed by calculating the variance of inflation in a macroeconomic model under alternative policy rules. A recent Bank of England study concludes from stochastic simulation of a small semi-structural model that a 'fairly substantial lump of inflation uncertainty' exists in the United Kingdom. In chapter 2 an extended and improved version of that model is developed while their estimates of inflation variability are revised downwards by deploying analytic techniques. In chapter 3 a new small 'semi structural' dynamic model of the UK economy is estimated, with particular attention to the modelling of wages and prices. It is used to assess the performance of simple monetary policy rules, including 'inflation forecast targeting' and 'Taylor' rules, while taking into account different degrees of forward-lookingness in both inflation targeting horizon and wage bargaining. Computation of asymptotic inflation-output standard-error trade-offs is provided under various specifications and parametrisations of the model. Large-scale country models have the convenience to make explicit a complete range of relationships among macroeconomic variables most of which, for obvious reasons, are neglected in smaller dynamic models. As a consequence, such quantitative framework offers an unique opportunity to evaluate not only the aggregate impact of exogenous shocks on the variables of interest, but also to identify the underlying economic mechanisms enabling the transmission of such shocks. In the second part of the thesis, I undertake simulations of the National Institute's Domestic Econometric Model (NIDEM) to analyse the characteristics of the UK monetary transmission mechanism. Chapter 4 emphasises that the impact of interest rate movements on real variables is strictly determined by both the monetary regime at work and the underlying assumptions regarding consumption behaviour. Certainly, the steady integration of the members of the EMU and increasing awareness of the need for closer co-operation in monetary and fiscal policy have stimulated greater interest in modelling interdependencies between European countries and the impact and feedbacks from the rest of the world economy. Many of the key issues have now an international aspect, so it becomes more and more difficult to rely on single-country models to provide necessary analysis. International transmission mechanisms can therefore be better tackled with a multi-country model. The third and last part of this thesis focuses on cross-country asymmetric transmissions in response to a common monetary shock within EMU. In particular, in chapter 5 an empirical analysis of the links between monetary and fiscal policy within EMU is presented. This is done through simulation of a neo-classical highly non-Ricardian multi-country model: the IMF's MULTIMOD Mark III (MM3). Chapter 6 provides further evidence about the effects of embracing a Monetary Union when underlying macroeconomist structures still differ across countries. By use of the same model-based quantitative framework, this chapter examines the role of nominal and real rigidities in European labour markets for the assessment of asymmetries in monetary transmission under various monetary regimes.
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Jessop, Alan Thomas. "Multiattribute models for engineering evaluation." Thesis, Heriot-Watt University, 1999. http://hdl.handle.net/10399/1226.

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Lystig, Theodore C. "Evaluation of hidden Markov models /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/9597.

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Malmsten, Hans. "Properties and evaluation of volatility models." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (Ekonomiska forskningsinstitutet vid Handelshögsk.) (EFI), 2004. http://www.hhs.se/efi/summary/641.htm.

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Venter, Daniel Jacobus Lodewyk. "An evaluation of paired comparison models." Thesis, University of Port Elizabeth, 2004. http://hdl.handle.net/10948/364.

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Introduction: A typical task in quantitative data analysis is to derive estimates of population parameters based on sample statistics. For manifest variables this is usually a straightforward process utilising suitable measurement instruments and standard statistics such the mean, median and standard deviation. Latent variables on the other hand are typically more elusive, making it difficult to obtain valid and reliable measurements. One of the most widely used methods of estimating the parameter value of a latent variable is to use a summated score derived from a set of individual scores for each of the various attributes of the latent variable. A serious limitation of this method and other similar methods is that the validity and reliability of measurements depend on whether the statements included in the questionnaire cover all characteristics of the variable being measured and also on respondents’ ability to correctly indicate their perceived assessment of the characteristics on the scale provided. Methods without this limitation and that are especially useful where a set of objects/entities must be ranked based on the parameter values of one or more latent variables, are methods of paired comparisons. Although the underlying assumptions and algorithms of these methods often differ dramatically, they all rely on data derived from a series of comparisons, each consisting of a pair of specimens selected from the set of objects/entities being investigated. Typical examples of the comparison process are: subjects (judges) who have to indicate for each pair of objects which of the two they prefer; sport teams that compete against each other in matches that involve two teams at a time. The resultant data of each comparison range from a simple dichotomy to indicate which of the two objects are preferred/better, to an interval or ratio scale score for e d Bradley-Terry models, and were based on statistical theory assuming that the variable(s) being measured is either normally (Thurstone-Mosteller) or exponentially (Bradley-Terry) distributed. For many years researchers had to rely on these PCM’s when analysing paired comparison data without any idea about the implications if the distribution of the data from which their sample were obtained differed from the assumed distribution for the applicable PCM being utilised. To address this problem, PCM’s were subsequently developed to cater for discrete variables and variables with distributions that are neither normal or exponential. A question that remained unanswered is how the performance, as measured by the accuracy of parameter estimates, of PCM's are affected if they are applied to data from a range of discrete and continuous distribution that violates the assumptions on which the applicable paired comparison algorithm is based. This study is an attempt to answer this question by applying the most popular PCM's to a range of randomly derived data sets that spans typical continuous and discrete data distributions. It is hoped that the results of this study will assist researchers when selecting the most appropriate PCM to obtain accurate estimates of the parameters of the variables in their data sets.
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Derradji-Aouat, Ahmed. "Evaluation of Prevost's elasto-plastic models." Thesis, University of Ottawa (Canada), 1988. http://hdl.handle.net/10393/5545.

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Sathi, Veer Reddy, and Jai Simha Ramanujapura. "A Quality Criteria Based Evaluation of Topic Models." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13274.

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Context. Software testing is the process, where a particular software product, or a system is executed, in order to find out the bugs, or issues which may otherwise degrade its performance. Software testing is usually done based on pre-defined test cases. A test case can be defined as a set of terms, or conditions that are used by the software testers to determine, if a particular system that is under test operates as it is supposed to or not. However, in numerous situations, test cases can be so many that executing each and every test case is practically impossible, as there may be many constraints. This causes the testers to prioritize the functions that are to be tested. This is where the ability of topic models can be exploited. Topic models are unsupervised machine learning algorithms that can explore large corpora of data, and classify them by identifying the hidden thematic structure in those corpora. Using topic models for test case prioritization can save a lot of time and resources. Objectives. In our study, we provide an overview of the amount of research that has been done in relation to topic models. We want to uncover various quality criteria, evaluation methods, and metrics that can be used to evaluate the topic models. Furthermore, we would also like to compare the performance of two topic models that are optimized for different quality criteria, on a particular interpretability task, and thereby determine the topic model that produces the best results for that task. Methods. A systematic mapping study was performed to gain an overview of the previous research that has been done on the evaluation of topic models. The mapping study focused on identifying quality criteria, evaluation methods, and metrics that have been used to evaluate topic models. The results of mapping study were then used to identify the most used quality criteria. The evaluation methods related to those criteria were then used to generate two optimized topic models. An experiment was conducted, where the topics generated from those two topic models were provided to a group of 20 subjects. The task was designed, so as to evaluate the interpretability of the generated topics. The performance of the two topic models was then compared by using the Precision, Recall, and F-measure. Results. Based on the results obtained from the mapping study, Latent Dirichlet Allocation (LDA) was found to be the most widely used topic model. Two LDA topic models were created, optimizing one for the quality criterion Generalizability (TG), and one for Interpretability (TI); using the Perplexity, and Point-wise Mutual Information (PMI) measures respectively. For the selected metrics, TI showed better performance, in Precision and F-measure, than TG. However, the performance of both TI and TG was comparable in case of Recall. The total run time of TI was also found to be significantly high than TG. The run time of TI was 46 hours, and 35 minutes, whereas for TG it was 3 hours, and 30 minutes.Conclusions. Looking at the F-measure, it can be concluded that the interpretability topic model (TI) performs better than the generalizability topic model (TG). However, while TI performed better in precision, Conclusions. Looking at the F-measure, it can be concluded that the interpretability topic model (TI) performs better than the generalizability topic model (TG). However, while TI performed better in precision, recall was comparable. Furthermore, the computational cost to create TI is significantly higher than for TG. Hence, we conclude that, the selection of the topic model optimization should be based on the aim of the task the model is used for. If the task requires high interpretability of the model, and precision is important, such as for the prioritization of test cases based on content, then TI would be the right choice, provided time is not a limiting factor. However, if the task aims at generating topics that provide a basic understanding of the concepts (i.e., interpretability is not a high priority), then TG is the most suitable choice; thus making it more suitable for time critical tasks.
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Books on the topic "Models evaluation"

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McMasters, Alan W. Wholesale provisioning models: Model evaluation. Monterey, California: Naval Postgraduate School, 1986.

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Stufflebeam, Daniel L., George F. Madaus, and Thomas Kellaghan, eds. Evaluation Models. Dordrecht: Kluwer Academic Publishers, 2002. http://dx.doi.org/10.1007/0-306-47559-6.

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Bouyssou, Denis, Thierry Marchant, Marc Pirlot, Patrice Perny, Alexis Tsoukiàs, and Philippe Vincke. Evaluation and Decision Models. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-1593-7.

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Gokgoz. Evaluation of activated sludge models. Manchester: UMIST, 1998.

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Branch, Alberta Special Education. Promising assessment models and practices. Edmonton, AB: Alberta education., 1994.

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Pecchenino, Rowena Ann. P* type models: Evaluation and forecasts. Cambridge, MA: National Bureau of Economic Research, 1990.

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Poswal, Muhammad S. Evaluation of supply chain improvement models. Manchester: UMIST, 1997.

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Systems evaluation: Methods, models, and applications. Boca Raton, FL: CRC Press, 2012.

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Hansen, Lars Peter. Econometric evaluation of asset pricing models. [Cambridge, Mass: Sloan School of Management, Massachusetts Institute of Technology, 1994.

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Powlson, David S., Pete Smith, and Jo U. Smith, eds. Evaluation of Soil Organic Matter Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61094-3.

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Book chapters on the topic "Models evaluation"

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Shinkfield, Anthony J., and Daniel L. Stufflebeam. "Models for Teacher Evaluation." In Teacher Evaluation, 173–376. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-009-1796-5_4.

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Pintilie, Melania. "Competing Risk Models." In Health Services Evaluation, 433–46. New York, NY: Springer US, 2019. http://dx.doi.org/10.1007/978-1-4939-8715-3_30.

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Bowler, Nicola. "Flaw Models." In Eddy-Current Nondestructive Evaluation, 167–98. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9629-2_9.

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Shinkfield, Anthony J., and Daniel L. Stufflebeam. "An Analysis of Alternate Models." In Teacher Evaluation, 377–95. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-009-1796-5_5.

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Adelman, Leonard, and Sharon L. Riedel. "Overview: Development Models, Evaluation Dimensions, and Evaluation Models." In Handbook for Evaluating Knowledge-Based Systems, 17–62. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6171-2_2.

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Isaias, Pedro, and Tomayess Issa. "Usability Evaluation Models." In High Level Models and Methodologies for Information Systems, 83–89. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9254-2_5.

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Isaias, Pedro, and Tomayess Issa. "Quality Evaluation Models." In High Level Models and Methodologies for Information Systems, 91–120. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9254-2_6.

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Labys, Walter C. "Evaluation of Models." In Modeling Mineral and Energy Markets, 107–12. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5101-0_11.

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Mousa, Shaker A. "Hemostasis Models: Bleeding Models." In Drug Discovery and Evaluation: Pharmacological Assays, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27728-3_14-1.

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Mousa, Shaker A. "Hemostasis Models: Bleeding Models." In Drug Discovery and Evaluation: Pharmacological Assays, 783–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-05392-9_14.

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Conference papers on the topic "Models evaluation"

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Ulke, Bastian, Friedrich Steimann, and Ralf Lammel. "Partial Evaluation of OCL Expressions." In 2017 ACM/IEEE 20th International Conference on Model-Driven Engineering Languages and Systems (MODELS). IEEE, 2017. http://dx.doi.org/10.1109/models.2017.31.

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"PROCESS MODEL VALIDATION - Transforming Process Models to Extended Checking Models." In International Conference on Evaluation of Novel Approaches to Software Engineering. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003016602140220.

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Brand, Thomas, and Holger Giese. "Modeling Approach and Evaluation Criteria for Adaptable Architectural Runtime Model Instances." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2019. http://dx.doi.org/10.1109/models.2019.00006.

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Hall, Russell K. "Evaluating Resource Plays with Statistical Models." In Hydrocarbon Economics and Evaluation Symposium. Society of Petroleum Engineers, 2007. http://dx.doi.org/10.2118/107435-ms.

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Scholtz, Jean, Oriana Love, Mark Whiting, Duncan Hodges, Lia Emanuel, and Danaë Stanton Fraser. "Utility evaluation of models." In the Fifth Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2669557.2669562.

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Gralha, Catarina. "Evaluation of Requirements Models." In 2016 IEEE 24th International Requirements Engineering Conference (RE). IEEE, 2016. http://dx.doi.org/10.1109/re.2016.28.

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Cevallos-Macas, Fanny, Samanta Cueva-Carrion, and Santos Urbina-Ramirez. "MOOC platforms evaluation models." In 2018 13th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2018. http://dx.doi.org/10.23919/cisti.2018.8399149.

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Lopez-Sanchez, Ana. "Measurement Models for Predicting the Ultrasonic Response from Side-Drilled Holes." In QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2004. http://dx.doi.org/10.1063/1.1711611.

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Starke, E., M. Krause, G. Pfeifer, and W. J. Fischer. "Applying network models to improve FE-models." In SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, edited by Mehrdad N. Ghasemi-Nejhad. SPIE, 2011. http://dx.doi.org/10.1117/12.885635.

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Ruiz, Jenny, and Monique Snoeck. "Adapting Kirkpatrick's evaluation model to technology enhanced learning." In MODELS '18: ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3270112.3270114.

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Reports on the topic "Models evaluation"

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Preller, Ruth H., and Dong S. Ko. Relocatable Models Development and Evaluation. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada628345.

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Preller, Ruth H., and Dong S. Ko. Relocatable Models Development and Evaluation. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada625117.

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Hansen, Lars Peter, John Heaton, and Erzo G. J. Luttmer. Econometric Evaluation of Asset Pricing Models. Cambridge, MA: National Bureau of Economic Research, October 1993. http://dx.doi.org/10.3386/t0145.

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Pecchenino, R. A., and Robert Rasche. P* Type Models: Evaluation and Forecasts. Cambridge, MA: National Bureau of Economic Research, August 1990. http://dx.doi.org/10.3386/w3406.

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Fu, Gongkang. Evaluation of Illinois Bridge Deterioration Models. Illinois Center for Transportation, September 2021. http://dx.doi.org/10.36501/0197-9191/21-029.

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The National Bridge Inventory bridge inspection system ranks the condition of bridge components on a scale of zero to nine. The resulting condition ratings represent an important element considered in deciding measures for bridge maintenance, repair, and rehabilitation. Thus, forecasting future condition ratings well is critical to reliable planning for these activities and estimating the costs. The Illinois Department of Transportation currently has deterministic models for this purpose. This study’s objective is to review the current models using condition rating histories gathered from 1980 to 2020 in Illinois for the following bridge components: deck, superstructure, substructure, culvert, and deck beam. The results show the current Illinois Department of Transportation models are inadequate in forecasting condition ratings, producing overestimates of the transition times between two condition rating levels for these components / systems, except for the deck beam, which is underestimated. It is recommended that the mean transition times found in this study from condition rating histories are used to replace the current models as a short-term solution. Further research is recommended to develop probabilistic models as a long-term solution to address observed significant variation or uncertainty in condition rating and transition times between condition rating levels.
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Castañeda-Fuentes, Juan Carlos, Héctor Augusto Valle-Samayoa, Juan Carlos Catalán-Herrera, Juan Carlos Arriaza-Herrera, Mariano José Gutiérrez-Morales, Carlos Eduardo Castillo-Maldonado, Douglas Napoleón Galindo-Gonzáles, Guisela Hurtarte-Aguilar, and Edson Roger Ortiz-Cardona. Evaluation of Inflation Forecasting Models in Guatemala. Inter-American Development Bank, July 2018. http://dx.doi.org/10.18235/0001266.

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Hanson, William L. Evaluation of Antileishmanial Drugs in Animal Models. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada382788.

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Angrist, Joshua, and Guido Imbens. Sources of Identifying Information in Evaluation Models. Cambridge, MA: National Bureau of Economic Research, December 1991. http://dx.doi.org/10.3386/t0117.

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Callahan, G. D., M. C. Loken, L. L. Van Sambeek, R. Chen, T. W. Pfeifle, J. D. Nieland, and F. D. Hansen. Evaluation of potential crushed-salt constitutive models. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/188535.

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Fast, J. D. Evaluation protocol for the WIND system atmospheric models. Office of Scientific and Technical Information (OSTI), December 1991. http://dx.doi.org/10.2172/10156991.

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