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 31 for “"non-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 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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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 …
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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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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. …
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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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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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Comparison of Segment and Pixel Based Non-Parametric Classification of Land Cover in the Amazon Region of Brazil Using Multitemporal Landsat TM/ETM+ Imagery
… of segment-based classification paired with non-parametric methods (CART and kNN) to classify a chronosequence of Landsat TM/ETM+ imagery spanning from 1992 to 2002 within the state of Rondônia, Brazil. Pixel-based classification was also implemented for comparison. Interannual multitemporal …
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Motivaatiotekijät ja matematiikan osaaminen : sukupuolierot ja koulujen väliset erot peruskoulun kolmannella luokalla
… their mathematical skills. In the analysis non-parametric methods where used: Mann-Whitney U test, Kruskal-Wallis H test, and Spearman's rank correlation coefficient. Based on the results of this study it can be concluded that there were some differences between schools: intrinsic value, …
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The Development of an Adaptive Electromagnetic Testing Method
… a look into the use of several kinds of parametric and non-parametric modeling techniques is presented, and the non-parametric methods found to be superior. Finally the use of a non-parametric K-nearest neighbor model to inform a design of experiments is presented and methods for adapting …
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School Quality Capitalization Into Housing Prices In Minnesota And Pennsylvania
… the conditional mean of school quality using non-parametric methods as proposed previously in the literature. So in order to instrument the school quality variables, a parametric model in which school quality is a function of average home prices within its attendance zone and average home …
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Assessing Robustness of the Rasch Mixture Model to Detect Differential Item Functioning - A Monte Carlo Simulation Study
… kinds 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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Balance Optimization Subset Selection: a framework for causal inference with observational data
… causal relationships. Additional assumptions and methods for post-processing the data are needed to construct unbiased estimators of causal effects because such data is non-random. This dissertation describes the Balance Optimization Subset Selection (BOSS) framework to apply causal inference to …
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Gaussian processes:iterative sparse approximations
… there has been an increased interest in applying non-parametric methods to real-world problems. Significant research has been devoted to Gaussian processes (GPs) due to their increased flexibility when compared with parametric models. These methods use Bayesian learning, which generally leads to …
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Nonparametric Statistical Approaches for Benchmark Dose Estimation in Quantitative Risk Assessment
… risk are conducted.First introduced are eight parametric models. The advantage of parametric models is they can produce consistent result when the selected model fits the dose-response curve very well. The simplicity of knowing the expression of these models allows for the construction of a …
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Neighborhood analysis methods in acoustic modeling for automatic speech recognition
… the problem of using nearest-neighbor based non-parametric methods for performing multi-class class-conditional probability estimation. The methods developed are applied to the problem of acoustic modeling for speech recognition. Neighborhood components analysis (NCA) (Goldberger et al. …
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Experimental evaluation of the efficiencies of certain non- parametric statistics
… be. The use of the 10% level of significance in non-parametric tests does seem unrealistic, because, in general, non-parametric statistics tend to be more conservative than parametric statistics. In case non-parametric methods are applied to samples from a population which is normally …
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