Relevant bibliographies by topics / Prediction models

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Academic literature on the topic 'Prediction models'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Prediction models"

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Geweke, John, and Gianni Amisano. "Prediction with Misspecified Models." American Economic Review 102, no. 3 (May 1, 2012): 482–86. http://dx.doi.org/10.1257/aer.102.3.482.

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The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard US postwar quarterly macroeconomic time series.
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Archer, Graeme, Michael Balls, Leon H. Bruner, Rodger D. Curren, Julia H. Fentem, Hermann-Georg Holzhütter, Manfred Liebsch, David P. Lovell, and Jacqueline A. Southee. "The Validation of Toxicological Prediction Models." Alternatives to Laboratory Animals 25, no. 5 (September 1997): 505–16. http://dx.doi.org/10.1177/026119299702500507.

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An alternative method is shown to consist of two parts: the test system itself; and a prediction model for converting in vitro endpoints into predictions of in vivo toxicity. For the alternative method to be relevant and reliable, it is important that its prediction model component is of high predictive power and is sufficiently robust against sources of data variability. In other words, the prediction model must be subjected to criticism, leading successful models to the state of confirmation. It is shown that there are certain circumstances in which a new prediction model may be introduced without the necessity to generate new test system data.
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Stenhaug, Benjamin A., and Benjamin W. Domingue. "Predictive Fit Metrics for Item Response Models." Applied Psychological Measurement 46, no. 2 (February 13, 2022): 136–55. http://dx.doi.org/10.1177/01466216211066603.

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The fit of an item response model is typically conceptualized as whether a given model could have generated the data. In this study, for an alternative view of fit, “predictive fit,” based on the model’s ability to predict new data is advocated. The authors define two prediction tasks: “missing responses prediction”—where the goal is to predict an in-sample person’s response to an in-sample item—and “missing persons prediction”—where the goal is to predict an out-of-sample person’s string of responses. Based on these prediction tasks, two predictive fit metrics are derived for item response models that assess how well an estimated item response model fits the data-generating model. These metrics are based on long-run out-of-sample predictive performance (i.e., if the data-generating model produced infinite amounts of data, what is the quality of a “model’s predictions on average?”). Simulation studies are conducted to identify the prediction-maximizing model across a variety of conditions. For example, defining prediction in terms of missing responses, greater average person ability, and greater item discrimination are all associated with the 3PL model producing relatively worse predictions, and thus lead to greater minimum sample sizes for the 3PL model. In each simulation, the prediction-maximizing model to the model selected by Akaike’s information criterion, Bayesian information criterion (BIC), and likelihood ratio tests are compared. It is found that performance of these methods depends on the prediction task of interest. In general, likelihood ratio tests often select overly flexible models, while BIC selects overly parsimonious models. The authors use Programme for International Student Assessment data to demonstrate how to use cross-validation to directly estimate the predictive fit metrics in practice. The implications for item response model selection in operational settings are discussed.
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Ansah, Kwabena, Ismail Wafaa Denwar, and Justice Kwame Appati. "Intelligent Models for Stock Price Prediction." Journal of Information Technology Research 15, no. 1 (January 2022): 1–17. http://dx.doi.org/10.4018/jitr.298616.

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Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.
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Karpac, Dusan, and Viera Bartosova. "The verification of prediction and classification ability of selected Slovak prediction models and their emplacement in forecasts of financial health of a company in aspect of globalization." SHS Web of Conferences 74 (2020): 06010. http://dx.doi.org/10.1051/shsconf/20207406010.

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Predicting financial health of a company is in this global world necessary for each business entity, especially for the international ones, as it´s very important to know financial stability. Forecasting business failure is a worldwide known term, in a global notion, and there is a lot of prediction models constructed to compute financial health of a company and, by that, state whether a company inclines to financial boom or bankruptcy. Globalized prediction models compute financial health of companies, but the vast majority of models predicting business failure are constructed solely for the conditions of a particular country or even just for a specific sector of a national economy. Field of financial predictions regarding to international view consists of elementary used models, for example, such as Altman´s Z-score or Beerman´s index, which are globally know and used as basic of many other modificated models. Following article deals with selected Slovak prediction models designed to Slovak conditions, states how these models stand in this global world, what is their international connection to the worldwide economies, and also states verification of their prediction ability in a specific sector. The verification of predictive ability of the models is defined by ROC analysis and through results the paper demonstrates the most suitable prediction models to use in the selected sector.
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Martínez-Fernández, Pelayo, Zulima Fernández-Muñiz, Ana Cernea, Juan Luis Fernández-Martínez, and Andrzej Kloczkowski. "Three Mathematical Models for COVID-19 Prediction." Mathematics 11, no. 3 (January 17, 2023): 506. http://dx.doi.org/10.3390/math11030506.

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The COVID-19 outbreak was a major event that greatly impacted the economy and the health systems around the world. Understanding the behavior of the virus and being able to perform long-term and short-term future predictions of the daily new cases is a working field for machine learning methods and mathematical models. This paper compares Verhulst’s, Gompertz´s, and SIR models from the point of view of their efficiency to describe the behavior of COVID-19 in Spain. These mathematical models are used to predict the future of the pandemic by first solving the corresponding inverse problems to identify the model parameters in each wave separately, using as observed data the daily cases in the past. The posterior distributions of the model parameters are then inferred via the Metropolis–Hastings algorithm, comparing the robustness of each prediction model and making different representations to visualize the results obtained concerning the posterior distribution of the model parameters and their predictions. The knowledge acquired is used to perform predictions about the evolution of both the daily number of infected cases and the total number of cases during each wave. As a main conclusion, predictive models are incomplete without a corresponding uncertainty analysis of the corresponding inverse problem. The invariance of the output (posterior prediction) with respect to the forward predictive model that is used shows that the methodology shown in this paper can be used to adopt decisions in real practice (public health).
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Pace, Michael L. "Prediction and the aquatic sciences." Canadian Journal of Fisheries and Aquatic Sciences 58, no. 1 (January 1, 2001): 63–72. http://dx.doi.org/10.1139/f00-151.

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The need for prediction is now widely recognized and frequently articulated as an objective of research programs in aquatic science. This recognition is partly the legacy of earlier advocacy by the school of empirical limnologists. This school, however, presented prediction narrowly and failed to account for the diversity of predictive approaches as well to set prediction within the proper scientific context. Examples from time series analysis and probabilistic models oriented toward management provide an expanded view of approaches and prospects for prediction. The context and rationale for prediction is enhanced understanding. Thus, prediction is correctly viewed as an aid to building scientific knowledge with better understanding leading to improved predictions. Experience, however, suggests that the most effective predictive models represent condensed models of key features in aquatic systems. Prediction remains important for the future of aquatic sciences. Predictions are required in the assessment of environmental concerns and for testing scientific fundamentals. Technology is driving enormous advances in the ability to study aquatic systems. If these advances are not accompanied by improvements in predictive capability, aquatic research will have failed in delivering on promised objectives. This situation should spark discomfort in aquatic scientists and foster creative approaches toward prediction.
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Ben-Haim, Yakov, and François M. Hemez. "Robustness, fidelity and prediction-looseness of models." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2137 (September 14, 2011): 227–44. http://dx.doi.org/10.1098/rspa.2011.0050.

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Assessment of the credibility of a mathematical or numerical model of a complex system must combine three components: (i) the fidelity of the model to test data, e.g. as quantified by a mean-squared error; (ii) the robustness, of model fidelity, to lack of understanding of the underlying processes; and (iii) the prediction-looseness of the model. ‘Prediction-looseness’ is the range of predictions of models that are equivalent in terms of fidelity. The main result of this paper asserts that fidelity, robustness and prediction-looseness are mutually antagonistic. A change in the model that enhances one of these attributes will cause deterioration of another. In particular, increasing the fidelity to test data will decrease the robustness to imperfect understanding of the process. Likewise, increasing the robustness will increase the predictive looseness . The conclusion is that focusing only on fidelity-to-data is not a sound decision-making strategy for model building and validation. A better strategy is to explore the trade-offs between robustness-to-uncertainty, fidelity to data and tightness of predictions. Our analysis is based on info-gap models of uncertainty, which can be applied to cases of severe uncertainty and lack of knowledge.
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Genç, Onur, Bilal Gonen, and Mehmet Ardıçlıoğlu. "A comparative evaluation of shear stress modeling based on machine learning methods in small streams." Journal of Hydroinformatics 17, no. 5 (April 28, 2015): 805–16. http://dx.doi.org/10.2166/hydro.2015.142.

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Predicting shear stress distribution has proved to be a critical problem to solve. Hence, the basic objective of this paper is to develop a prediction of shear stress distribution by machine learning algorithms including artificial neural networks, classification and regression tree, generalized linear models. The data set, which is large and feature-rich, is utilized to improve machine learning-based predictive models and extract the most important predictive factors. The 10-fold cross-validation approach was used to determine the performances of prediction methods. The predictive performances of the proposed models were found to be very close to each other. However, the results indicated that the artificial neural network, which has the R value of 0.92 ± 0.03, achieved the best classification performance overall accuracy on the 10-fold holdout sample. The predictions of all machine learning models were well correlated with measurement data.
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Kappen, Teus H., and Linda M. Peelen. "Prediction models." Current Opinion in Anaesthesiology 29, no. 6 (December 2016): 717–26. http://dx.doi.org/10.1097/aco.0000000000000386.

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Dissertations / Theses on the topic "Prediction models"

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Haider, Peter. "Prediction with Mixture Models." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2014/6961/.

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Learning a model for the relationship between the attributes and the annotated labels of data examples serves two purposes. Firstly, it enables the prediction of the label for examples without annotation. Secondly, the parameters of the model can provide useful insights into the structure of the data. If the data has an inherent partitioned structure, it is natural to mirror this structure in the model. Such mixture models predict by combining the individual predictions generated by the mixture components which correspond to the partitions in the data. Often the partitioned structure is latent, and has to be inferred when learning the mixture model. Directly evaluating the accuracy of the inferred partition structure is, in many cases, impossible because the ground truth cannot be obtained for comparison. However it can be assessed indirectly by measuring the prediction accuracy of the mixture model that arises from it. This thesis addresses the interplay between the improvement of predictive accuracy by uncovering latent cluster structure in data, and further addresses the validation of the estimated structure by measuring the accuracy of the resulting predictive model. In the application of filtering unsolicited emails, the emails in the training set are latently clustered into advertisement campaigns. Uncovering this latent structure allows filtering of future emails with very low false positive rates. In order to model the cluster structure, a Bayesian clustering model for dependent binary features is developed in this thesis. Knowing the clustering of emails into campaigns can also aid in uncovering which emails have been sent on behalf of the same network of captured hosts, so-called botnets. This association of emails to networks is another layer of latent clustering. Uncovering this latent structure allows service providers to further increase the accuracy of email filtering and to effectively defend against distributed denial-of-service attacks. To this end, a discriminative clustering model is derived in this thesis that is based on the graph of observed emails. The partitionings inferred using this model are evaluated through their capacity to predict the campaigns of new emails. Furthermore, when classifying the content of emails, statistical information about the sending server can be valuable. Learning a model that is able to make use of it requires training data that includes server statistics. In order to also use training data where the server statistics are missing, a model that is a mixture over potentially all substitutions thereof is developed. Another application is to predict the navigation behavior of the users of a website. Here, there is no a priori partitioning of the users into clusters, but to understand different usage scenarios and design different layouts for them, imposing a partitioning is necessary. The presented approach simultaneously optimizes the discriminative as well as the predictive power of the clusters. Each model is evaluated on real-world data and compared to baseline methods. The results show that explicitly modeling the assumptions about the latent cluster structure leads to improved predictions compared to the baselines. It is beneficial to incorporate a small number of hyperparameters that can be tuned to yield the best predictions in cases where the prediction accuracy can not be optimized directly.
Das Lernen eines Modells für den Zusammenhang zwischen den Eingabeattributen und annotierten Zielattributen von Dateninstanzen dient zwei Zwecken. Einerseits ermöglicht es die Vorhersage des Zielattributs für Instanzen ohne Annotation. Andererseits können die Parameter des Modells nützliche Einsichten in die Struktur der Daten liefern. Wenn die Daten eine inhärente Partitionsstruktur besitzen, ist es natürlich, diese Struktur im Modell widerzuspiegeln. Solche Mischmodelle generieren Vorhersagen, indem sie die individuellen Vorhersagen der Mischkomponenten, welche mit den Partitionen der Daten korrespondieren, kombinieren. Oft ist die Partitionsstruktur latent und muss beim Lernen des Mischmodells mitinferiert werden. Eine direkte Evaluierung der Genauigkeit der inferierten Partitionsstruktur ist in vielen Fällen unmöglich, weil keine wahren Referenzdaten zum Vergleich herangezogen werden können. Jedoch kann man sie indirekt einschätzen, indem man die Vorhersagegenauigkeit des darauf basierenden Mischmodells misst. Diese Arbeit beschäftigt sich mit dem Zusammenspiel zwischen der Verbesserung der Vorhersagegenauigkeit durch das Aufdecken latenter Partitionierungen in Daten, und der Bewertung der geschätzen Struktur durch das Messen der Genauigkeit des resultierenden Vorhersagemodells. Bei der Anwendung des Filterns unerwünschter E-Mails sind die E-Mails in der Trainingsmende latent in Werbekampagnen partitioniert. Das Aufdecken dieser latenten Struktur erlaubt das Filtern zukünftiger E-Mails mit sehr niedrigen Falsch-Positiv-Raten. In dieser Arbeit wird ein Bayes'sches Partitionierunsmodell entwickelt, um diese Partitionierungsstruktur zu modellieren. Das Wissen über die Partitionierung von E-Mails in Kampagnen hilft auch dabei herauszufinden, welche E-Mails auf Veranlassen des selben Netzes von infiltrierten Rechnern, sogenannten Botnetzen, verschickt wurden. Dies ist eine weitere Schicht latenter Partitionierung. Diese latente Struktur aufzudecken erlaubt es, die Genauigkeit von E-Mail-Filtern zu erhöhen und sich effektiv gegen verteilte Denial-of-Service-Angriffe zu verteidigen. Zu diesem Zweck wird in dieser Arbeit ein diskriminatives Partitionierungsmodell hergeleitet, welches auf dem Graphen der beobachteten E-Mails basiert. Die mit diesem Modell inferierten Partitionierungen werden via ihrer Leistungsfähigkeit bei der Vorhersage der Kampagnen neuer E-Mails evaluiert. Weiterhin kann bei der Klassifikation des Inhalts einer E-Mail statistische Information über den sendenden Server wertvoll sein. Ein Modell zu lernen das diese Informationen nutzen kann erfordert Trainingsdaten, die Serverstatistiken enthalten. Um zusätzlich Trainingsdaten benutzen zu können, bei denen die Serverstatistiken fehlen, wird ein Modell entwickelt, das eine Mischung über potentiell alle Einsetzungen davon ist. Eine weitere Anwendung ist die Vorhersage des Navigationsverhaltens von Benutzern einer Webseite. Hier gibt es nicht a priori eine Partitionierung der Benutzer. Jedoch ist es notwendig, eine Partitionierung zu erzeugen, um verschiedene Nutzungsszenarien zu verstehen und verschiedene Layouts dafür zu entwerfen. Der vorgestellte Ansatz optimiert gleichzeitig die Fähigkeiten des Modells, sowohl die beste Partition zu bestimmen als auch mittels dieser Partition Vorhersagen über das Verhalten zu generieren. Jedes Modell wird auf realen Daten evaluiert und mit Referenzmethoden verglichen. Die Ergebnisse zeigen, dass das explizite Modellieren der Annahmen über die latente Partitionierungsstruktur zu verbesserten Vorhersagen führt. In den Fällen bei denen die Vorhersagegenauigkeit nicht direkt optimiert werden kann, erweist sich die Hinzunahme einer kleinen Anzahl von übergeordneten, direkt einstellbaren Parametern als nützlich.
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Vaidyanathan, Sivaranjani. "Bayesian Models for Computer Model Calibration and Prediction." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435527468.

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Charraud, Jocelyn, and Saez Adrian Garcia. "Bankruptcy prediction models on Swedish companies." Thesis, Umeå universitet, Företagsekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185143.

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Bankruptcies have been a sensitive topic all around the world for over 50 years. From their research, the authors have found that only a few bankruptcy studies have been conducted in Sweden and even less on the topic of bankruptcy prediction models. This thesis investigates the performance of the Altman, Ohlson and Zmijewski bankruptcy prediction models. This research investigates all Swedish companies during the years 2017 and 2018.  This study has the intention to shed light on some of the most famous bankruptcy prediction models. It is interesting to explore the predictive abilities and usability of those three models in Sweden. The second purpose of this study is to create two models from the most significant variable out of the three models studied and to test its prediction power with the aim to create two models designed for Swedish companies.  We identified a research gap in terms of Sweden, where bankruptcy prediction models have been rather unexplored and especially with those three models. Furthermore, we have identified a second research gap regarding the time period of the research. Only a few studies have been conducted on the topic of bankruptcy prediction models post the financial crisis of 2007/08.  We have conducted a quantitative study in order to achieve the purpose of the study. The data used was secondary data gathered from the Serrano database. This research followed an abductive approach with a positive paradigm. This research has studied all active Swedish companies between the years 2017 and 2018. Finally, this contributed to the current field of knowledge on the topic through the analysis of the results of the models on Swedish companies, using the liquidity theory, solvency and insolvency theory, the pecking order theory, the profitability theory, the cash flow theory, and the contagion effect. The results aligned with the liquidity theory, the solvency and insolvency theory and the profitability theory. Moreover, from this research we have found that the Altman model has the lowest performance out of the three models, followed by the Ohlson model that shows some mixed results depending on the statistical analysis. Lastly, the Zmijewski model has the best performance out of the three models. Regarding the performance and the prediction power of the two new models were significantly higher than the three models studied.
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Rice, Nigel. "Multivariate prediction models in medicine." Thesis, Keele University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314647.

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Brefeld, Ulf. "Semi-supervised structured prediction models." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15748.

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Das Lernen aus strukturierten Eingabe- und Ausgabebeispielen ist die Grundlage für die automatisierte Verarbeitung natürlich auftretender Problemstellungen und eine Herausforderung für das Maschinelle Lernen. Die Einordnung von Objekten in eine Klassentaxonomie, die Eigennamenerkennung und das Parsen natürlicher Sprache sind mögliche Anwendungen. Klassische Verfahren scheitern an der komplexen Natur der Daten, da sie die multiplen Abhängigkeiten und Strukturen nicht erfassen können. Zudem ist die Erhebung von klassifizierten Beispielen in strukturierten Anwendungsgebieten aufwändig und ressourcenintensiv, während unklassifizierte Beispiele günstig und frei verfügbar sind. Diese Arbeit thematisiert halbüberwachte, diskriminative Vorhersagemodelle für strukturierte Daten. Ausgehend von klassischen halbüberwachten Verfahren werden die zugrundeliegenden analytischen Techniken und Algorithmen auf das Lernen mit strukturierten Variablen übertragen. Die untersuchten Verfahren basieren auf unterschiedlichen Prinzipien und Annahmen, wie zum Beispiel der Konsensmaximierung mehrerer Hypothesen im Lernen aus mehreren Sichten, oder der räumlichen Struktur der Daten im transduktiven Lernen. Desweiteren wird in einer Fallstudie zur Email-Batcherkennung die räumliche Struktur der Daten ausgenutzt und eine Lösung präsentiert, die der sequenziellen Natur der Daten gerecht wird. Aus den theoretischen Überlegungen werden halbüberwachte, strukturierte Vorhersagemodelle und effiziente Optmierungsstrategien abgeleitet. Die empirische Evaluierung umfasst Klassifikationsprobleme, Eigennamenerkennung und das Parsen natürlicher Sprache. Es zeigt sich, dass die halbüberwachten Methoden in vielen Anwendungen zu signifikant kleineren Fehlerraten führen als vollständig überwachte Baselineverfahren.
Learning mappings between arbitrary structured input and output variables is a fundamental problem in machine learning. It covers many natural learning tasks and challenges the standard model of learning a mapping from independently drawn instances to a small set of labels. Potential applications include classification with a class taxonomy, named entity recognition, and natural language parsing. In these structured domains, labeled training instances are generally expensive to obtain while unlabeled inputs are readily available and inexpensive. This thesis deals with semi-supervised learning of discriminative models for structured output variables. The analytical techniques and algorithms of classical semi-supervised learning are lifted to the structured setting. Several approaches based on different assumptions of the data are presented. Co-learning, for instance, maximizes the agreement among multiple hypotheses while transductive approaches rely on an implicit cluster assumption. Furthermore, in the framework of this dissertation, a case study on email batch detection in message streams is presented. The involved tasks exhibit an inherent cluster structure and the presented solution exploits the streaming nature of the data. The different approaches are developed into semi-supervised structured prediction models and efficient optimization strategies thereof are presented. The novel algorithms generalize state-of-the-art approaches in structural learning such as structural support vector machines. Empirical results show that the semi-supervised algorithms lead to significantly lower error rates than their fully supervised counterparts in many application areas, including multi-class classification, named entity recognition, and natural language parsing.
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Asterios, Geroukis. "Prediction of Linear Models: Application of Jackknife Model Averaging." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297671.

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When using linear models, a common practice is to find the single best model fit used in predictions. This on the other hand can cause potential problems such as misspecification and sometimes even wrong models due to spurious regression. Another method of predicting models introduced in this study as Jackknife Model Averaging developed by Hansen & Racine (2012). This assigns weights to all possible models one could use and allows the data to have heteroscedastic errors. This model averaging estimator is compared to the Mallows’s Model Averaging (Hansen, 2007) and model selection by Bayesian Information Criterion and Mallows’s Cp. The results show that the Jackknife Model Averaging technique gives less prediction errors compared to the other methods of model prediction. This study concludes that the Jackknife Model Averaging technique might be a useful choice when predicting data.
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Shrestha, Rakshya. "Deep soil mixing and predictive neural network models for strength prediction." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607735.

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Grant, Stuart William. "Risk prediction models in cardiovascular surgery." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/risk-prediction-models-in-cardiovascular-surgery(1befbc5d-2aa6-4d24-8c32-e635cf55e339).html.

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Objectives: Cardiovascular disease is the leading cause of mortality and morbidity in the developed world. Surgery can improve prognosis and relieve symptoms. Risk prediction models are increasingly being used to inform clinicians and patients about the risks of surgery, to facilitate clinical decision making and for the risk-adjustment of surgical outcome data. The importance of risk prediction models in cardiovascular surgery has been highlighted by the publication of cardiovascular surgery outcome data and the need for risk-adjustment. The overall objective of this thesis is to advance risk prediction modelling in cardiovascular surgery with a focus on the development of models for elective AAA repair and assessment of models for cardiac surgery. Methods: Three large clinical databases (two elective AAA repair and one cardiac surgery) were utilised. Each database was cleaned prior to analysis. Logistic regression was used to develop both regional and national risk prediction models for mortality following elective AAA repair. A regional model to identify the risk of developing renal failure following elective AAA repair was also developed. The performance of a widely used cardiac surgery risk prediction model (the logistic EuroSCORE) over time was evaluated using a national cardiac database. In addition an updated model version (EuroSCORE II) was validated and both models’ performance in emergency cardiac surgery was evaluated. Results: Regional risk models for mortality following elective AAA repair (VGNW model) and a model to predict post-operative renal failure were developed. Validation of the model for mortality using a national dataset demonstrated good performance compared to other available risk models. To improve generalisability a national model (the BAR score) with better discriminatory ability was developed. In a prospective validation of both models using regional data, the BAR score demonstrated excellent discrimination overall and good discrimination in procedural sub-groups. The EuroSCORE was found to have lost calibration over time due to a fall in observed mortality despite an increase in the predicted mortality of patients undergoing cardiac surgery. The EuroSCORE II demonstrated good performance for contemporary cardiac surgery. Both EuroSCORE models demonstrated inadequate performance for emergency cardiac surgery. Conclusions: Risk prediction models play an important role in cardiovascular surgery. Two accurate risk prediction models for mortality following elective AAA repair have been developed and can be used to risk-adjust surgical outcomes and facilitate clinical decision making. As surgical practice changes over time risk prediction models may lose accuracy which has implications for their application. Cardiac risk models may not be sufficiently accurate for high-risk patient groups such as those undergoing emergency surgery and specific emergency models may be required. Continuing research into new risk factors and model outcomes is needed and risk prediction models may play an increasing role in clinical decision making in the future.
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Jones, Margaret. "Point prediction in survival time models." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340616.

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Monsch, Matthieu (Matthieu Frederic). "Large scale prediction models and algorithms." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84398.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Operations Research Center, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 129-132).
Over 90% of the data available across the world has been produced over the last two years, and the trend is increasing. It has therefore become paramount to develop algorithms which are able to scale to very high dimensions. In this thesis we are interested in showing how we can use structural properties of a given problem to come up with models applicable in practice, while keeping most of the value of a large data set. Our first application provides a provably near-optimal pricing strategy under large-scale competition, and our second focuses on capturing the interactions between extreme weather and damage to the power grid from large historical logs. The first part of this thesis is focused on modeling competition in Revenue Management (RM) problems. RM is used extensively across a swathe of industries, ranging from airlines to the hospitality industry to retail, and the internet has, by reducing search costs for customers, potentially added a new challenge to the design and practice of RM strategies: accounting for competition. This work considers a novel approach to dynamic pricing in the face of competition that is intuitive, tractable and leads to asymptotically optimal equilibria. We also provide empirical support for the notion of equilibrium we posit. The second part of this thesis was done in collaboration with a utility company in the North East of the United States. In recent years, there has been a number of powerful storms that led to extensive power outages. We provide a unified framework to help power companies reduce the duration of such outages. We first train a data driven model to predict the extent and location of damage from weather forecasts. This information is then used in a robust optimization model to optimally dispatch repair crews ahead of time. Finally, we build an algorithm that uses incoming customer calls to compute the likelihood of damage at any point in the electrical network.
by Matthieu Monsch.
Ph.D.
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Books on the topic "Prediction models"

1

Steyerberg, E. W. Clinical Prediction Models. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-77244-8.

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Steyerberg, Ewout W. Clinical Prediction Models. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16399-0.

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Spectral numerical weather prediction models. Philadelphia: Society for Industrial and Applied Mathematics, 2012.

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Auerbach, Jonathan Lyle. Some Statistical Models for Prediction. [New York, N.Y.?]: [publisher not identified], 2020.

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R, Wilcock Peter, Iverson Richard Matthew, AGU Fall Meeting, and American Geophysical Union, eds. Prediction in geomorphology. Washington, D.C: American Geophysical Union, 2003.

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R, Wilcock Peter, and Iverson Richard Matthew, eds. Prediction in geomorphology. Washington, D.C: American Geophysical Union, 2002.

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Rick, Archer, and U.S. Army Research Institute for the Behavioral and Social Sciences., eds. Improving soldier factors in prediction models. Alexandria, Va: U.S. Army Research Institute for the Behavioral and Social Sciences, 2002.

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Building and Fire Research Laboratory (U.S.) and Factory Mutual Research Corporation, eds. Prediction of fire dynamics. Gaithersburg, MD: The Institute, 1997.

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R, Robinson Allan, and Lee Ding 1925-, eds. Oceanography and acoustics: Prediction and propagation models. New York: American Institute of Physics, 1994.

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1952-, Hadorn David C., United States. Health Care Financing Administration., and Rand/UCLA/Harvard Center for Health Care Financing Policy Research., eds. Assessing the performance of mortality prediction models. Santa Monica, CA: RAND, 1993.

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Book chapters on the topic "Prediction models"

1

Deistler, Manfred, and Wolfgang Scherrer. "Prediction." In Time Series Models, 29–42. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13213-1_2.

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Bacmeister, Julio T. "Weather Prediction Models weather prediction model." In Encyclopedia of Sustainability Science and Technology, 12062–79. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-0851-3_362.

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Lefebvre, Cedric W., Jay P. Babich, James H. Grendell, James H. Grendell, John E. Heffner, Ronan Thibault, Claude Pichard, et al. "Prediction Models." In Encyclopedia of Intensive Care Medicine, 1803. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-00418-6_2077.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Linear Mixed Models." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 141–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_5.

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AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.
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Faraway, Julian J. "Prediction." In Linear Models with Python, 53–60. 10th ed. First edition. | Boca Raton : CRC Press, 2021. | Series: Chapman & Hall/CRC texts in statistical science: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781351053419-4.

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den Brinker, Albertus C., and Harm J. W. Belt. "Using Kautz Models in Model Reduction." In Signal Analysis and Prediction, 185–96. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1768-8_13.

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Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In Lecture Notes in Business Information Processing, 140–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.

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AbstractProcess mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. We propose an approach that decreases the previous approaches’ reliance on user knowledge for discovering fine-grained process behavior. Furthermore, we enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. We assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.
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Dodla, Venkata Bhaskar Rao. "Hierarchy of Atmospheric Models." In Numerical Weather Prediction, 29–66. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003354017-2.

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Vaseghi, Saeed V. "Linear Prediction Models." In Advanced Signal Processing and Digital Noise Reduction, 185–213. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-92773-6_7.

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Bacmeister, Julio T. "Weather Prediction Models." In Climate Change Modeling Methodology, 89–114. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5767-1_5.

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Conference papers on the topic "Prediction models"

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Zou, Qiaosha, and Yuan Xie. "Compact Models and Model Standard for 2.5D and 3D Integration." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633955.

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Ben-Haim, Yakov, and Franc¸ois M. Hemez. "Robustness, Fidelity and Prediction-Looseness of Models." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58008.

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Assessment of the credibility of a mathematical or numerical model of an engineering system must combine three components: (1) The fidelity of the model to test data. (2) The robustness, of model fidelity, to lack of understanding of the underlying processes. (3) The prediction looseness of the model. ‘Prediction looseness’ is the range of predictions of models which are equivalent in terms of fidelity. The main result of this paper is that high fidelity, high robustness, and small prediction looseness are mutually incompatible. A model with high fidelity to data and high robustness to imperfect understanding of the process, will have low predictive focus. Our analysis is based on info-gap models of uncertainty.
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Brockhoff, Tobias, Malte Heithoff, Istvan Koren, Judith Michael, Jerome Pfeiffer, Bernhard Rumpe, Merih Seran Uysal, Wil M. P. Van Der Aalst, and Andreas Wortmann. "Process Prediction with Digital Twins." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00032.

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Ketata, Aymen, Carlos Moreno, Sebastian Fischmeister, Jia Liang, and Krzysztof Czarnecki. "Performance prediction upon toolchain migration in model-based software." In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2015. http://dx.doi.org/10.1109/models.2015.7338261.

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Tran, Ke M., Yonatan Bisk, Ashish Vaswani, Daniel Marcu, and Kevin Knight. "Unsupervised Neural Hidden Markov Models." In Proceedings of the Workshop on Structured Prediction for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/w16-5907.

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Bjegovic, D. "Models for service life prediction." In 2nd International RILEM Workshop on Life Prediction and Aging Management of Concrete Structures. RILEM Publications SARL, 2003. http://dx.doi.org/10.1617/2912143780.002.

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Scheffer, L. "Session details: Models and metrics of interconnect performance." In SLIP04: International Workshop on System Level Interconnect Prediction. New York, NY, USA: ACM, 2004. http://dx.doi.org/10.1145/3248398.

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Yu, Shipeng, Alexander van Esbroeck, Faisal Farooq, Glenn Fung, Vikram Anand, and Balaji Krishnapuram. "Predicting Readmission Risk with Institution Specific Prediction Models." In 2013 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2013. http://dx.doi.org/10.1109/ichi.2013.57.

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Xie, Yanwen, Dan Feng, Fang Wang, Xuehai Tang, Jizhong Han, and Xinyan Zhang. "DFPE: Explaining Predictive Models for Disk Failure Prediction." In 2019 35th Symposium on Mass Storage Systems and Technologies (MSST). IEEE, 2019. http://dx.doi.org/10.1109/msst.2019.000-3.

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Nezhad Karim Nobakht, B., and M. Christie. "Model Prediction under Uncertainty Using Hierarchical Models." In 79th EAGE Conference and Exhibition 2017. Netherlands: EAGE Publications BV, 2017. http://dx.doi.org/10.3997/2214-4609.201701024.

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Reports on the topic "Prediction models"

1

Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, September 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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Srikant, Rayadurgam, and Bruce Hajek. Reduced-Complexity Models for Network Performance Prediction. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada435841.

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Chung, C. F., and J. M. Shaw. Quantitative prediction models for landslide hazard assessment. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/210202.

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Murphy, D. D., W. M. Thomas, W. M. Evanco, and W. W. Agresti. Procedures for Applying Ada Quality Prediction Models. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada264730.

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Iskandarani, Mohamed, Omar Knio, Ashwanth Srinivasan, and William C. Thacker. Quantifying Prediction Fidelity in Ocean Circulation Models. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada590693.

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Iskandarani, Mohamed, Omar Knio, Ashwanth Srinivasan, and William C. Thacker. Quantifying Prediction Fidelity in Ocean Circulation Models. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada601423.

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Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani, and Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, March 2019. http://dx.doi.org/10.34074/ocds.12019.

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Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performance of stock market prices. The purpose of this review is to investigate different techniques applied in stock market price prediction with special emphasis on hybrid models.
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Przystupa, Marek A., Jimin Zhang, and Annetta J. Luevano. Development of the Microstructure Based Stochastic Life Prediction Models. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada269880.

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Przystupa, M. A., and A. K. Vasudevan. Development of the Microstructure Based Stochastic Life Prediction Models. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada270453.

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Dinda, Peter A., and David R. O'Hallaron. An Evaluation of Linear Models for Host Load Prediction. Fort Belvoir, VA: Defense Technical Information Center, November 1998. http://dx.doi.org/10.21236/ada358577.

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