Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 54 for “"parametric methods"”.
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Essays on semi-/non-parametric methods in econometrics
… contains three chapters focusing on semi-/non-parametric models in econometrics. The first chapter, which is a joint work with Sukjin Han, considers parametric/semiparametric estimation and inference in a class of bivariate threshold crossing models with dummy endogenous variables. We …
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Historically implied swaption skews using non-parametric methods
… this the dissertation adopts and constructs non-parametric methods which only make use of historical realised data of the underlying variable rather than any implied pricing history of the derivative itself. Stutzer's method of canonical valuation (1996) is adapted for use with interest rate …
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The analysis of directional data: Performance comparisons of randomization and parametric methods
… data in recent years, the collection of methods available is still extremely limited. In the present study, several Monte Carlo simulations are carried out to assess the performance of several parametric and nonparametric randomization methods for the analysis of directional data, with …
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Modelling multiple disease risk factors for microsimulation studies
… factors for a synthetic population. A variety of methods have been used to model the progression through time of multiple disease risk factors, such as blood pressure and smoking status, in microsimulations, based on a variety of kinds of data. However, the relative merits of different methods …
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Assessing Robustness of the Rasch Mixture Model to Detect Differential Item Functioning - A Monte Carlo Simulation Study
… of traditional tools for DIF detection: non-parametric methods and parametric methods. Mantel Haenszel (MH), SIBTEST, and standardization are examples of non-parametric DIF detection methods. The majority of parametric DIF detection methods are item response theory (IRT) based. Both …
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Operations and maintenance cost for new major U.S. Coast Guard platforms: projected versus actual costs.
… the budget models are poor predictors and that parametric methods may provide more accuracy. Additional research in developing cost estimating relationships for this purpose is needed.
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The effect of neonicotinoids on Apis mellifera: A revisit
… and Wild Bees", in light of unsound statistical methods used in the original study. In the first part of this paper, I attempted to replicate the analysis underlying key findings in the original paper. In the second part, I reanalyzed the data using mostly non-parametric methods in order to …
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Survival analysis for lung cancer patients
… representative survival analysis model from semi-parametric methods (Cox proportional hazards model), one from non-parametric methods (Kaplan-Meier estimator), and one from machine learning approaches (random survival forests). Using the results obtained from these different methods, we identified …
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ENHANCING pm2.5 AIR POLLUTION ANALYSIS IN BEIJING: A TRANSITION FROM NON-PARAMETRIC REGRESSION TO ADVANCED MACHINE LEARNING METHODOLOGY
… in Beijing, transitioning from traditional non-parametric methods to the advanced Random Forest Plus (RF+) methodology. The original research, which used non-parametric regression and bandwidth selection frequency, to assess PM2.5 levels as well as feature importance, may result in overfitting, …
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A radial basis function approach to pricing and hedging options incorporating transaction costs
Nonparametric methods of pricing options have been available for some time, providing a viable alternative to traditional parametric methods. A general class of methods known as learning networks has been making significant inroads in option pricing literature. This thesis will adopt McLoone's …
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A Bayesian classification framework with label corrections
… it’s also universally applicable on top of any parametric or non-parametric method, such as the kernel method and Dirichlet Process (DP) priors. With a thorough study of the kernel and Dirichlet Process method, we successfully applied our framework onto these non-parametric methods and achieved …
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Study of Condition Monitoring of Bridges Using Genetic Algorithms
… model of the real structural system through parametric methods. The problem of structural condition monitoring is formulated as an optimization problem. The remotely sensed static measurements, such as displacements and strains, under normal service loads are used to estimate the state of the …
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Reaching the Chronic Poor and Food Insecure after a Disaster: The Case of Niger
… data collected in 2005, this study uses both parametric and semi-parametric methods to investigate key factors associated with household vulnerability to drought and economic downturns from exposure to shocks in Niger, conditioned on household and community assets. The findings provides …
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Self-exciting point processes and their applications to crime data
… where future crime may occur.;We introduce a new parametric form for the triggering function, which can be utilised in situations where an initial event does not immediately lead to an increased risk of further events, but rather the risk increases over a period of time after the event. We also …
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Automatic Summarization of Financial Reports
… model. This model is combined with various non-parametric methods to generate sentence vectors which can facilitate the efficient selection of important sentences in the document as part of the summarization algorithm. We illustrate the superiority of a financially tuned language model. Further, …
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Statistical methods for gene set based significance analysis.
… existing approaches have largely relied on nonparametric methods and require repeated computation of permutation and resampling data to assess the significance of a gene set. In this dissertation, we study parametric approaches for GSEA by formulating the enrichment analysis into a simple model …
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Analysis of SELDI mass spectra for biomarker discovery and cancer classification
… best performance when compared to the existing methods. The assumption of normality of SELDI protein profiles, on which the standard statistical methods are based, was examined. Normality tests and the multiple testing procedures revealed that SELDI protein profiles do not follow normal …
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Fault detection in multivariate processes : handling autocorrelation, contamination, and small sample sizes in engineered systems.
… in-control process behavior. Many existing methods perform well when monitoring multivariate processes, but they typically require that certain assumptions are met, including multivariate normality and independence. Furthermore, the availability of a large set of in-control historical data …
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A Parametric Method of Perspective Alignment and Color Correction for Skin Lesion Imaging
<p>ABSTRACT</p> <p>A Parametric Method of Perspective Alignment and Color Correction on Skin Lesion Imaging</p> <p>Jian Xu</p> <p>This thesis investigates the employment of a parametric and fiducial-based perspective alignment method, and a color correction method in the context of digital skin …
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Essays in Hierarchical Time Series Forecasting and Forecast Combination
… range of different popular forecast combination methods. We implement techniques from 3 broad categories: (a) simple non-parametric methods, (b) regression-based methods, and (c) geometric (eigenvector) methods, allowing for static or dynamic estimation of each approach. Using S3 classes/methods …
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