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Dissertations / Theses on the topic 'Robust regression'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

Bai, Xue. "Robust linear regression." Kansas State University, 2012. http://hdl.handle.net/2097/14977.

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Master of Science<br>Department of Statistics<br>Weixin Yao<br>In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value may have a large effect on the regression parameter estimates. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we review various robust regression methods including: M-estimate, LMS estimat

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2

Nottingham, Quinton J. "Model-robust quantal regression." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/40225.

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3

Robinson, Timothy J. "Dual Model Robust Regression." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/11244.

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In typical normal theory regression, the assumption of homogeneity of variances is often not appropriate. Instead of treating the variances as a nuisance and transforming away the heterogeneity, the structure of the variances may be of interest and it is desirable to model the variances. Aitkin (1987) proposes a parametric dual model in which a log linear dependence of the variances on a set of explanatory variables is assumed. Aitkin's parametric approach is an iterative one providing estimates for the parameters in the mean and variance models through joint maximum likelihood. Est

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4

Nargis, Suraiya, and n/a. "Robust methods in logistic regression." University of Canberra. Information Sciences & Engineering, 2005. http://erl.canberra.edu.au./public/adt-AUC20051111.141200.

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My Masters research aims to deepen our understanding of the behaviour of robust methods in logistic regression. Logistic regression is a special case of Generalized Linear Modelling (GLM), which is a powerful and popular technique for modelling a large variety of data. Robust methods are useful in reducing the effect of outlying values in the response variable on parameter estimates. A literature survey shows that we are still at the beginning of being able to detect extreme observations in logistic regression analyses, to apply robust methods in logistic regression and to present informativel

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5

Bai, Xiuqin. "Robust mixtures of regression models." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18683.

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Doctor of Philosophy<br>Department of Statistics<br>Kun Chen and Weixin Yao<br>This proposal contains two projects that are related to robust mixture models. In the robust project, we propose a new robust mixture of regression models (Bai et al., 2012). The existing methods for tting mixture regression models assume a normal distribution for error and then estimate the regression param- eters by the maximum likelihood estimate (MLE). In this project, we demonstrate that the MLE, like the least squares estimate, is sensitive to outliers and heavy-tailed error distributions. We propose a r

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6

Li, Xiongya. "Robust multivariate mixture regression models." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38427.

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Doctor of Philosophy<br>Department of Statistics<br>Weixing Song<br>In this dissertation, we proposed a new robust estimation procedure for two multivariate mixture regression models and applied this novel method to functional mapping of dynamic traits. In the first part, a robust estimation procedure for the mixture of classical multivariate linear regression models is discussed by assuming that the error terms follow a multivariate Laplace distribution. An EM algorithm is developed based on the fact that the multivariate Laplace distribution is a scale mixture of the multivariate standard no

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7

Waterman, Megan Janet Tuttle. "Linear Mixed Model Robust Regression." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27708.

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Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a nonpara

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8

Assaid, Christopher Ashley. "Outlier Resistant Model Robust Regression." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30493.

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Parametric regression fitting (such as OLS) to a data set requires specification of an underlying model. If the specified model is different from the true model, then the parametric fit suffers to a degree that varies with the extent of model misspecification. Mays and Birch (1996) addressed this problem in the one regressor variable case with a method known as Model Robust Regression (MRR), which is a weighted average of independent parametric and nonparametric fits to the data. This paper was based on the underlying assumption of "well-behaved" (Normal) data. The method seeks to ta

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9

Agard, David B. "Robust inferential procedures applied to regression." Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-10132005-152518/.

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10

Starnes, Brett Alden. "Asymptotic Results for Model Robust Regression." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/30244.

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Since the mid 1980's many statisticians have studied methods for combining parametric and nonparametric esimates to improve the quality of fits in a regression problem. Notably in 1987, Einsporn and Birch proposed the Model Robust Regression estimate (MRR1) in which estimates of the parametric function, f, and the nonparametric function, g, were combined in a straightforward fashion via the use of a mixing parameter, l. This technique was studied extensively at small samples and was shown to be quite effective at modeling various unusual functions. In 1995, Mays and Birch develop

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11

Detwiler, Dana. "Microcomputer implementation of robust regression techniques." Master's thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-03302010-020305/.

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12

Avci, Ezgi. "A Comparison Of Some Robust Regression Techniques." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12611165/index.pdf.

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Robust regression is a commonly required approach in industrial studies like data mining, quality control and improvement, and finance areas. Among the robust regression methods<br>Least Median Squares, Least Trimmed Squares, Mregression, MM-method, Least Absolute Deviations, Locally Weighted Scatter Plot Smoothing and Multivariate Adaptive Regression Splines are compared under contaminated normal distributions with each other and Ordinary Least Squares with respect to the multiple outlier detection performance measures. In this comparison<br>a simulation study is performed by changing some of

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13

Hamzah, Nor Aishah. "Robust regression estimation in generalized linear models." Thesis, University of Bristol, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294372.

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14

Wei, Yan. "Robust mixture regression models using t-distribution." Kansas State University, 2012. http://hdl.handle.net/2097/14110.

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Master of Science<br>Department of Statistics<br>Weixin Yao<br>In this report, we propose a robust mixture of regression based on t-distribution by extending the mixture of t-distributions proposed by Peel and McLachlan (2000) to the regression setting. This new mixture of regression model is robust to outliers in y direction but not robust to the outliers with high leverage points. In order to combat this, we also propose a modified version of the proposed method, which fits the mixture of regression based on t-distribution to the data after adaptively trimming the high leverage points. We

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15

Anderson, Cynthia 1962. "A Comparison of Five Robust Regression Methods with Ordinary Least Squares: Relative Efficiency, Bias and Test of the Null Hypothesis." Thesis, University of North Texas, 2001. https://digital.library.unt.edu/ark:/67531/metadc5808/.

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A Monte Carlo simulation was used to generate data for a comparison of five robust regression estimation methods with ordinary least squares (OLS) under 36 different outlier data configurations. Two of the robust estimators, Least Absolute Value (LAV) estimation and MM estimation, are commercially available. Three authormodified variations on MM were also included (MM1, MM2, and MM3). Design parameters that were varied include sample size (n=60 and n=180), number of independent predictor variables (2, 3 and 6), outlier density (0%, 5% and 15%) and outlier location (2x,2y s, 8x8y s, 4x,8y s and

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16

Hashem, Hussein Abdulahman. "Regularized and robust regression methods for high dimensional data." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/9197.

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Recently, variable selection in high-dimensional data has attracted much research interest. Classical stepwise subset selection methods are widely used in practice, but when the number of predictors is large these methods are difficult to implement. In these cases, modern regularization methods have become a popular choice as they perform variable selection and parameter estimation simultaneously. However, the estimation procedure becomes more difficult and challenging when the data suffer from outliers or when the assumption of normality is violated such as in the case of heavy-tailed errors.

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17

Bai, Xiuqin. "Robust mixtures of regressions models." Kansas State University, 2010. http://hdl.handle.net/2097/4613.

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Master of Science<br>Department of Statistics<br>Weixin Yao<br>In the fitting of mixtures of linear regression models, the normal assumption has been traditionally used for the error term and then the regression parameters are estimated by the maximum likelihood estimate (MLE) using the EM algorithm. Under the normal assumption, the M step of the EM algorithm uses a weighted least squares estimate (LSE) for the regression parameters. It is well known that the LSE is sensitive to outliers or heavy tailed error distributions. In this report, we propose a robust mixture of linear regression

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18

Xing, Yanru. "Robust mixture regression model fitting by Laplace distribution." Kansas State University, 2013. http://hdl.handle.net/2097/16534.

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Master of Science<br>Department of Statistics<br>Weixing Song<br>A robust estimation procedure for mixture linear regression models is proposed in this report by assuming the error terms follow a Laplace distribution. EM algorithm is imple- mented to conduct the estimation procedure of missing information based on the fact that the Laplace distribution is a scale mixture of normal and a latent distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in this literat

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19

Zeileis, Achim, and Christian Kleiber. "Approximate replication of high-breakdown robust regression techniques." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/422/1/document.pdf.

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This paper demonstrates that even regression results obtained by techniques close to the standard ordinary least squares (OLS) method can be difficult to replicate if a stochastic model fitting algorithm is employed.<br>Series: Research Report Series / Department of Statistics and Mathematics

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20

Clark, Seth K. "Model Robust Regression Based on Generalized Estimating Equations." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/26588.

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One form of model robust regression (MRR) predicts mean response as a convex combination of a parametric and a nonparametric prediction. MRR is a semiparametric method by which an incompletely or an incorrectly specified parametric model can be improved through adding an appropriate amount of a nonparametric fit. The combined predictor can have less bias than the parametric model estimate alone and less variance than the nonparametric estimate alone. Additionally, as shown in previous work for uncorrelated data with linear mean function, MRR can converge faster than the nonparametric pr

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21

Lawrence, David E. "Cluster-Based Bounded Influence Regression." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/28455.

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In the field of linear regression analysis, a single outlier can dramatically influence ordinary least squares estimation while low-breakdown procedures such as M regression and bounded influence regression may be unable to combat a small percentage of outliers. A high-breakdown procedure such as least trimmed squares (LTS) regression can accommodate up to 50% of the data (in the limit) being outlying with respect to the general trend. Two available one-step improvement procedures based on LTS are Mallows 1-step (M1S) regression and Schweppe 1-step (S1S) regression (the current state-of-the-

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22

Neugebauer, Shawn Patrick. "Robust Analysis of M-Estimators of Nonlinear Models." Thesis, Virginia Tech, 1996. http://hdl.handle.net/10919/36557.

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Estimation of nonlinear models finds applications in every field of engineering and the sciences. Much work has been done to build solid statistical theories for its use and interpretation. However, there has been little analysis of the tolerance of nonlinear model estimators to deviations from assumptions and normality. We focus on analyzing the robustness properties of M-estimators of nonlinear models by studying the effects of deviations from assumptions and normality on these estimators. We discuss St. Laurent and Cook's Jacobian Leverage and identify the relationship of the technique t

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23

Pitselis, Georgios. "On robust credibility models for premiums, including weighted regression." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0023/NQ38826.pdf.

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24

Njenga, Edward Gachangi. "Robust estimation of the regression coefficients in complex surveys." Thesis, University of Southampton, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305575.

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25

Liu, Yantong. "Robust mixture linear EIV regression models by t-distribution." Kansas State University, 2012. http://hdl.handle.net/2097/15157.

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Master of Science<br>Department of Statistics<br>Weixing Song<br>A robust estimation procedure for mixture errors-in-variables linear regression models is proposed in the report by assuming the error terms follow a t-distribution. The estimation procedure is implemented by an EM algorithm based on the fact that the t-distribution is a scale mixture of normal distribution and a Gamma distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies. Comparison is also made with the MLE procedure under normality assumption.

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26

Zhang, Jingyi. "Robust mixture regression modeling with Pearson type VII distribution." Kansas State University, 2013. http://hdl.handle.net/2097/15648.

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Master of Science<br>Department of Statistics<br>Weixing Song<br>A robust estimation procedure for parametric regression models is proposed in the paper by assuming the error terms follow a Pearson type VII distribution. The estimation procedure is implemented by an EM algorithm based on the fact that the Pearson type VII distributions are a scale mixture of a normal distribution and a Gamma distribution. A trimmed version of proposed procedure is also discussed in this paper, which can successfully trim the high leverage points away from the data. Finite sample performance of the proposed alg

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27

Mays, James Edward. "Model robust regression: combining parametric, nonparametric, and semiparametric methods." Diss., Virginia Polytechnic Institute and State University, 1995. http://hdl.handle.net/10919/49937.

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In obtaining a regression fit to a set of data, ordinary least squares regression depends directly on the parametric model formulated by the researcher. If this model is incorrect, a least squares analysis may be misleading. Alternatively, nonparametric regression (kernel or local polynomial regression, for example) has no dependence on an underlying parametric model, but instead depends entirely on the distances between regressor coordinates and the prediction point of interest. This procedure avoids the necessity of a reliable model, but in using no information from the researcher, may fit t

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28

Shah, Siddharth S. "Robust Heart Rate Variability Analysis using Gaussian Process Regression." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1293737259.

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29

Rettes, Julio Alberto Sibaja. "Robust algorithms for linear regression and locally linear embedding." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/22445.

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RETTES, Julio Alberto Sibaja. Robust algorithms for linear regression and locally linear embedding. 2017. 105 f. Dissertação (Mestrado em Ciência da Computação)- Universidade Federal do Ceará, Fortaleza, 2017.<br>Submitted by Weslayne Nunes de Sales (weslaynesales@ufc.br) on 2017-03-30T13:15:27Z No. of bitstreams: 1 2017_dis_rettesjas.pdf: 3569500 bytes, checksum: 46cedc2d9f96d0f58bcdfe3e0d975d78 (MD5)<br>Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2017-04-04T11:10:44Z (GMT) No. of bitstreams: 1 2017_dis_rettesjas.pdf: 3569500 bytes, checksum: 46cedc2d9f96d0f58bcdfe3e0

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Corrente, Salvatore. "Hierarchy and interaction of criteria in robust ordinal regression." Doctoral thesis, Università di Catania, 2013. http://hdl.handle.net/10761/1312.

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All decision making problems we deal with along our lives, have a multiple criteria structure, that is several alternatives are evaluated with respect to some points of view, technically called evaluation criteria, and then compared in order to make the ``best'' decision. Multiple Criteria Decision Aiding, proposes methodologies useful to take decisions explicitly considering the preferences of the Decision Maker. In many real world problems, the criteria are not independent but interacting, being possible to observe a certain degree of synergy or redundancy between the evaluation criteria an

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Zhang, Hongyang. "Linear model selection based on extended robust least angle regression." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43060.

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In variable selection problems, when the number of candidate covariates is relatively large, the "two-step" model building strategy, which consists of two consecutive steps sequencing and segmentation, is often used. Sequencing aims to first sequence all the candidate covariates to form a list of candidate variables in which more "important" ones are likely to appear at the beginning. Then, in the segmentation step, the subsets of the first m (chosen by the user) candidate covariates which are ranked at the top of the sequenced list will be carefully examined in order to select the final predi

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Nguyen, Tri-Dung Ph D. Massachusetts Institute of Technology. "Robust estimation, regression and ranking with applications in portfolio optimization." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/52800.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 108-112).<br>Classical methods of maximum likelihood and least squares rely a great deal on the correctness of the model assumptions. Since these assumptions are only approximations of reality, many robust statistical methods have

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McCants, Michael. "Efficacy of robust regression applied to fractional factorial treatment structures." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/9260.

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Master of Science<br>Department of Statistics<br>James J. Higgins<br>Completely random and randomized block designs involving n factors at each of two levels are used to screen for the effects of a large number of factors. With such designs it may not be possible either because of costs or because of time to run each treatment combination more than once. In some cases, only a fraction of all the treatments may be run. With a large number of factors and limited observations, even one outlier can adversely affect the results. Robust regression methods are designed to down-weight the adverse affe

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34

Zhang, Zongjun. "Adaptive Robust Regression Approaches in data analysis and their Applications." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445343114.

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35

Mattsson, Johan. "Constructing Residential Price Property Indices Using Robust and Shrinkage Regression Modelling." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252555.

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This thesis intends to construct and compare multiple Residential Price Property Indices (RPPI) with the aim to express the price development of houses in Stockholm county from January 2013 to September 2018. The index method used is the hedonic time dummy variable method. Different methods of imputation of missing data will be applied and new variables will be derived from the available data in order to develop various regression models. Observations judged as not part of the index's target population will be excluded to improve the quality of the training data. The indices will be computed b

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You, Jiazhong 1968. "Robust estimation and testing : finite-sample properties and econometric applications." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=36739.

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High breakdown point, bounded influence and high efficiency at the Gaussian model are desired properties of robust regression estimators. Robustness of validity, robustness of efficiency and high breakdown point size and power are the fundamental goals in robust testing. The objective of this dissertation is to examine the finite-sample properties of robust estimators and tests, and to find some useful applications for them. This is accomplished by extensive Monte Carlo experiments and other inference techniques in various contamination situations. In the linear regression model with an outlyi

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Wang, Chenjie. "The design exploration method for adaptive design systems." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28084.

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Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Janet K. Allen; Committee Member: Benjamin Klein; Committee Member: Farrokh Mistree; Committee Member: Seung-Kyum Choi.

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Van, der Westhuizen Magdelena Marianna. "Robust techniques for regression models with minimal assumptions / M.M. van der Westhuizen." Thesis, North-West University, 2011. http://hdl.handle.net/10394/6689.

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Good quality management decisions often rely on the evaluation and interpretation of data. One of the most popular ways to investigate possible relationships in a given data set is to follow a process of fitting models to the data. Regression models are often employed to assist with decision making. In addition to decision making, regression models can also be used for the optimization and prediction of data. The success of a regression model, however, relies heavily on assumptions made by the model builder. In addition, the model may also be influenced by the presence of outliers; a more robu

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Garlipp, Tim. "On robust jump detection in regression surfaces with applications to image analysis." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973256516.

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Meng, Li. "Robust estimation of the number of components for mixtures of linear regression." Kansas State University, 2014. http://hdl.handle.net/2097/17856.

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Master of Science<br>Department of Statistics<br>Weixin Yao<br>In this report, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criterion. Compared to the traditional information criterion, the trimmed criterion is robust and not sensitive to outliers. The superiority of the trimmed methods in comparison with the traditional information criterion methods is illustrated through a simulation study. A real data application is also used to illustrate the effectiveness of the trimmed model selection methods.

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Kovac, Arne. "Wavelet thresholding for unequally time-spaced data." Thesis, University of Bristol, 1999. http://hdl.handle.net/1983/2088715a-7792-4032-bb76-83e3b0389b94.

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Rätsch, Gunnar. "Robust boosting via convex optimization." Phd thesis, Universität Potsdam, 2001. http://opus.kobv.de/ubp/volltexte/2005/39/.

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In dieser Arbeit werden statistische Lernprobleme betrachtet. Lernmaschinen extrahieren Informationen aus einer gegebenen Menge von Trainingsmustern, so daß sie in der Lage sind, Eigenschaften von bisher ungesehenen Mustern - z.B. eine Klassenzugehörigkeit - vorherzusagen. Wir betrachten den Fall, bei dem die resultierende Klassifikations- oder Regressionsregel aus einfachen Regeln - den Basishypothesen - zusammengesetzt ist. Die sogenannten Boosting Algorithmen erzeugen iterativ eine gewichtete Summe von Basishypothesen, die gut auf ungesehenen Mustern vorhersagen. <br /> Die Arbeit behandelt

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Meneghel, Danilevicz Ian. "Robust linear mixed models, alternative methods to quantile regression for panel data, and adaptive LASSO quantile regression with fixed effects." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST176.

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La thèse est constituée de trois chapitres. Le premier s'intéresse au lien entre l’exposition à la pollution de l’air et les affections respiratoires chez les enfants et les adolescents. La cohorte comprend 82 individus observés mensuellement pendant 6 mois. Nous proposons un modèle linéaire mixte robuste combiné à une analyse en composantes principales afin de gérer la multicolinéarité entre les covariables et l’impact des observations extrêmes sur les estimations. Le deuxième chapitre analyse des données de panel au moyen de modèles à effets fixes et utilisant différentes fonction de perte.

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PAULA, ERICK ROMARIO DE. "ELECTRICAL ENERGY CONDITIONAL DEMAND ANALYSIS USING ROBUST REGRESSION: APLICATION TO A REAL CASE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9133@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>Este trabalho tem como objetivo avaliar o uso da técnica Análise Condicionada da Demanda, que é uma metodologia que quebra o consumo de energia elétrica (neste trabalho do setor residencial) em suas partes por equipamento e por uso final, via Regressão Robusta em contrapartida à utilização da regressão clássica, na estimação do consumo de energia elétrica por uso final do setor residencial. Para isto foram realizadas análises via regressão linear múltipla e também análises via regressão robusta (estimadores robustos). Serão realizada

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Vest, Jeffrey D. "Robust, location-free scale estimators for the linear regression and k-sample models." Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-06062008-151058/.

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Bouche, Dimitri. "Function-valued regression with kernels : Improving speed, flexibility and robustness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT001.

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L'augmentation du nombre et de la sophistication des appareils collectant des données permet de suivre l'évolution d'une multitude de phénomènes à des résolutions très fines. Cela étend le champ des applications possibles de l'apprentissage statistique. Un tel volume peut néanmoins devenir difficile à exploiter. Cependant quand leur nombre augmente, les données peuvent devenir redondantes. On peut alors chercher une représentation exploitant des propriétés du processus génératif. Dans cette thèse, nous nous concentrons sur la représentation fonctionnelle. Bien sûr, les données sont toujours de

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Van, Deventer Petrus Jacobus Uys. "Outliers, influential observations and robust estimation in non-linear regression analysis and discriminant analysis." Doctoral thesis, University of Cape Town, 1993. http://hdl.handle.net/11427/4363.

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Dai, Wei. "Robust Approaches to Marker Identification and Evaluation for Risk Assessment." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11087.

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Assessment of risk has been a key element in efforts to identify factors associated with disease, to assess potential targets of therapy and enhance disease prevention and treatment. Considerable work has been done to develop methods to identify markers, construct risk prediction models and evaluate such models. This dissertation aims to develop robust approaches for these tasks. In Chapter 1, we present a robust, flexible yet powerful approach to identify genetic variants that are associated with disease risk in genome-wide association studies when some subjects are related. In Chapter 2, we

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Andersson, Niklas. "Regression-Based Monte Carlo For Pricing High-Dimensional American-Style Options." Thesis, Umeå universitet, Institutionen för fysik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-119013.

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Pricing different financial derivatives is an essential part of the financial industry. For some derivatives there exists a closed form solution, however the pricing of high-dimensional American-style derivatives is still today a challenging problem. This project focuses on the derivative called option and especially pricing of American-style basket options, i.e. options with both an early exercise feature and multiple underlying assets. In high-dimensional problems, which is definitely the case for American-style options, Monte Carlo methods is advantageous. Therefore, in this thesis, regress

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Ozmen, Ayse. "Robust Conic Quadratic Programming Applied To Quality Improvement -a Robustification Of Cmars." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612513/index.pdf.

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In this thesis, we study and use Conic Quadratic Programming (CQP) for purposes of operational research, especially, for quality improvement in manufacturing. In previous works, the importance and benefit of CQP in this area became already demonstrated. There, the complexity of the regression method Multivariate Adaptive Regression Spline (MARS), which especially means sensitivity with respect to noise in the data, became penalized in the form of so-called Tikhonov regularization, which became expressed and studied as a CQP problem. This was leading to the new method CMARS<br>it is more model-

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