Dissertations / Theses on the topic 'Model selection curves'
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MONTEIRO, ANDRE MONTEIRO DALMEIDA. "NON-PARAMETRIC ESTIMATIONS OF INTEREST RATE CURVES : MODEL SELECTION CRITERION: MODEL SELECTION CRITERIONPERFORMANCE DETERMINANT FACTORS AND BID-ASK S." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2684@1.
Full textEsta tese investiga a estimação de curvas de juros sob o ponto de vista de métodos não-paramétricos. O texto está dividido em dois blocos. O primeiro investiga a questão do critério utilizado para selecionar o método de melhor desempenho na tarefa de interpolar a curva de juros brasileira em uma dada amostra. Foi proposto um critério de seleção de método baseado em estratégias de re-amostragem do tipo leave-k-out cross validation, onde K k £ £ 1 e K é função do número de contratos observados a cada curva da amostra. Especificidades do problema reduzem o esforço computacional requerido, tornando o critério factível. A amostra tem freqüência diária: janeiro de 1997 a fevereiro de 2001. O critério proposto apontou o spline cúbico natural -utilizado com método de ajuste perfeito aos dados - como o método de melhor desempenho. Considerando a precisão de negociação, este spline mostrou-se não viesado. A análise quantitativa de seu desempenho identificou, contudo, heterocedasticidades nos erros simulados. A partir da especificação da variância condicional destes erros e de algumas hipóteses, foi proposto um esquema de intervalo de segurança para a estimação de taxas de juros pelo spline cúbico natural, empregado como método de ajuste perfeito aos dados. O backtest sugere que o esquema proposto é consistente, acomodando bem as hipóteses e aproximações envolvidas. O segundo bloco investiga a estimação da curva de juros norte-americana construída a partir dos contratos de swaps de taxas de juros dólar-Libor pela Máquina de Vetores Suporte (MVS), parte do corpo da Teoria do Aprendizado Estatístico. A pesquisa em MVS tem obtido importantes avanços teóricos, embora ainda sejam escassas as implementações em problemas reais de regressão. A MVS possui características atrativas para a modelagem de curva de juros: é capaz de introduzir já na estimação informações a priori sobre o formato da curva e sobre aspectos da formação das taxas e liquidez de cada um dos contratos a partir dos quais ela é construída. Estas últimas são quantificadas pelo bid-ask spread (BAS) de cada contrato. A formulação básica da MVS é alterada para assimilar diferentes valores do BAS sem que as propriedades dela sejam perdidas. É dada especial atenção ao levantamento de informação a priori para seleção dos parâmetros da MVS a partir do formato típico da curva. A amostra tem freqüência diária: março de 1997 a abril de 2001. Os desempenhos fora da amostra de diversas especificações da MVS foram confrontados com aqueles de outros métodos de estimação. A MVS foi o método que melhor controlou o trade- off entre viés e variância dos erros.
This thesis investigates interest rates curve estimation under non-parametric approach. The text is divided into two parts. The first one focus on which criterion to use to select the best performance method in the task of interpolating Brazilian interest rate curve. A selection criterion is proposed to measure out-of-sample performance by combining resample strategies leave-k-out cross validation applied upon the whole sample curves, where K k £ £ 1 and K is function of observed contract number in each curve. Some particularities reduce substantially the required computational effort, making the proposed criterion feasible. The data sample range is daily, from January 1997 to February 2001. The proposed criterion selected natural cubic spline, used as data perfect-fitting estimation method. Considering the trade rate precision, the spline is non-biased. However, quantitative analysis of performance determinant factors showed the existence of out-of-sample error heteroskedasticities. From a conditional variance specification of these errors, a security interval scheme is proposed for interest rate generated by perfect-fitting natural cubic spline. A backtest showed that the proposed security interval is consistent, accommodating the evolved assumptions and approximations. The second part estimate US free-for-floating interest rate swap contract curve by using Support Vector Machine (SVM), a method derived from Statistical Learning Theory. The SVM research has got important theoretical results, however the number of implementation on real regression problems is low. SVM has some attractive characteristics for interest rates curves modeling: it has the ability to introduce already in its estimation process a priori information about curve shape and about liquidity and price formation aspects of the contracts that generate the curve. The last information set is quantified by the bid-ask spread. The basic SVM formulation is changed in order to be able to incorporate the different values for bid-ask spreads, without losing its properties. Great attention is given to the question of how to extract a priori information from swap curve typical shape to be used in MVS parameter selection. The data sample range is daily, from March 1997 to April 2001. The out-of-sample performances of different SVM specifications are faced with others method performances. SVM got the better control of trade- off between bias and variance of out-of-sample errors.
Chia, Yan Wah. "Radiation from curved (conical) frequency selective surfaces." Thesis, Loughborough University, 1993. https://dspace.lboro.ac.uk/2134/7200.
Full textPaterson, Chay Giles Blair. "Minimal models of invasion and clonal selection in cancer." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28986.
Full textWang, Wen-Chyi. "Regularized variable selection in proportional hazards model using area under receiver operating characteristic curve criterion." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9972.
Full textThesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Flake, Darl D. II. "Separation of Points and Interval Estimation in Mixed Dose-Response Curves with Selective Component Labeling." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/4697.
Full textBoruvka, Audrey. "Data-driven estimation for Aalen's additive risk model." Thesis, Kingston, Ont. : [s.n.], 2007. http://hdl.handle.net/1974/489.
Full textLee, Kyeong Eun. "Bayesian models for DNA microarray data analysis." Diss., Texas A&M University, 2005. http://hdl.handle.net/1969.1/2465.
Full textPlašil, Miroslav. "Empirické ověření nové Keynesiánské Philipsovy křivky v ČR." Doctoral thesis, Vysoká škola ekonomická v Praze, 2003. http://www.nusl.cz/ntk/nusl-77088.
Full textRückert, Nadja. "Studies on two specific inverse problems from imaging and finance." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-91587.
Full textSun, Limei. "Probabilistic model designs and selection curves of trawl gears /." 2001.
Find full textWENG, SHIH-CHIEH, and 翁士傑. "Exploring Influential Factors for Model Selection in Latent Growth Curve Models." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/50528846949302923171.
Full text國立臺北大學
統計學系
95
The use of Latent Growth Curve Modeling(LGCM), in longitudinal study data analysis has been widely used in the field of Psychology, Education and Medical research. We are interested in observing the effect of time on the action or attitude of the subject in a general longitudinal study, whether these behavior vary as time progress. Hence, LGCM is a technique to analyze the repeat measurements of a variable at different time frame. This essay will discuss the use of LGCM with small sample size, to identify the accuracy of model selection or power using test statistics. In the past, researchers rarely investigate the use of LGCM with small sample size, and the model selection is mainly continues variable type. This research will follow the selection of model and use continues variable type. In the simulation study, the following six factors are used: number of sample sizes, variance of intercept, variance of slope, means of slope, covariance of intercept and slope, number of observation variables. This research uses the test statistics to identify the power performance of LGCM in small sample size. The research uses Monte Carlo simulation, first simulating the data need for analysis, then feedback the data in to the five nested model for simulation research to estimate the power performance in different affecting factor variation. The results of simulation indicate that the main factors are number of sample sizes, covariance of intercept and slope and the number of observation variables. In terms of the power of the sample size and model selection for this research, BIC model selection indicator has the best performance, followed by , Adjusted-BIC has the worst performance. This research has resulted in producing a power table for the use of experimental researchers.
Gadoury, David. "Distributions d'auto-amorçage exactes ponctuelles des courbes ROC et des courbes de coûts." Thèse, 2009. http://hdl.handle.net/1866/7896.
Full textChen, Chun-Shu, and 陳春樹. "Model Selection for Curve and Surface Fitting Using Generalized Degrees of Freedom." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/49906881842793562861.
Full text國立中央大學
統計研究所
95
In the process of data analysis, there are usually a number of candidate statistical methods (models) that can be used, and different methods (models) generally have different performances under different situations. In this thesis, we focus on model selection in curve and surface fitting. We develop a general rule to fairly assess among candidate curve or surface fitting methods regardless of whether the fitting procedures are complex and whether the corresponding estimates are linear, nonlinear, or even discontinuous. Based on the concept of generalized degrees of freedom (GDF) (Ye 1998), we propose an improved Cp method to select among a class of selection criteria in spline smoothing. In addition, a general methodology for geostatistical model selection is proposed by further generalizing GDF to spatial prediction. The proposed method not only can be used to select among various spatial prediction methods, but also can be applied to the variable selection problem in spatial regression. The validities of the proposed model selection methods for curve and surface fitting are justified both numerically and theoretically.
"Addressing the Variable Selection Bias and Local Optimum Limitations of Longitudinal Recursive Partitioning with Time-Efficient Approximations." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.54792.
Full textDissertation/Thesis
Doctoral Dissertation Psychology 2019
Ma, Liangzhuang. "Optimization of trawlnet codend mesh size to allow for maximal undersized fish release and a model consideration of towing time to the effects of the selection curve /." 2005.
Find full textKruger, Ester. "Assessing the accuracy of the growth in theoretical capability as predicted by the career path appreciation (CPA) 1 VS CPA 2." Diss., 2013. http://hdl.handle.net/10500/11875.
Full textIndustrial & Organisational Psychology
M. Admin. (Industrial and Organisational Psychology)