Dissertations / Theses on the topic 'Robus fitting'
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Xing, Yanru. "Robust mixture regression model fitting by Laplace distribution." Kansas State University, 2013. http://hdl.handle.net/2097/16534.
Full textDepartment of Statistics
Weixing Song
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 literature. A sensitivity study is also conducted based on a real data example to illustrate the application of the proposed method.
Truong, Ha-Giang. "Robust fitting: Assisted by semantic analysis and reinforcement learning." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2567.
Full textWang, Hanzi. "Robust statistics for computer vision : model fitting, image segmentation and visual motion analysis." Monash University, Dept. of Electrical and Computer Systems Engineering, 2004. http://arrow.monash.edu.au/hdl/1959.1/5345.
Full textYang, Li. "Robust fitting of mixture of factor analyzers using the trimmed likelihood estimator." Kansas State University, 2014. http://hdl.handle.net/2097/18118.
Full textDepartment of Statistics
Weixin Yao
Mixtures of factor analyzers have been popularly used to cluster the high dimensional data. However, the traditional estimation method is based on the normality assumptions of random terms and thus is sensitive to outliers. In this article, we introduce a robust estimation procedure of mixtures of factor analyzers using the trimmed likelihood estimator (TLE). We use a simulation study and a real data application to demonstrate the robustness of the trimmed estimation procedure and compare it with the traditional normality based maximum likelihood estimate.
Mordini, Nicola. "Multicentre study for a robust protocol in single-voxel spectroscopy: quantification of MRS signals by time-domain fitting algorithms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7579/.
Full textWillersjö, Nyfelt Emil. "Comparison of the 1st and 2nd order Lee–Carter methods with the robust Hyndman–Ullah method for fitting and forecasting mortality rates." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48383.
Full textRelvas, Carlos Eduardo Martins. "Modelos parcialmente lineares com erros simétricos autoregressivos de primeira ordem." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-28052013-182956/.
Full textIn this master dissertation, we present the symmetric partially linear models with AR(1) errors that generalize the normal partially linear models to contain autocorrelated errors AR(1) following a symmetric distribution instead of the normal distribution. Among the symmetric distributions, we can consider heavier tails than the normal ones, controlling the kurtosis and down-weighting outlying observations in the estimation process. The parameter estimation is made through the penalized likelihood by using score functions and the expected Fisher information. We derive these functions in this work. The effective degrees of freedom and asymptotic results are also presented as well as the residual analysis, highlighting the normal curvature of local influence under different perturbation schemes. An application with real data is given for illustration.
Cai, Zhipeng. "Consensus Maximization: Theoretical Analysis and New Algorithms." Thesis, 2020. http://hdl.handle.net/2440/127452.
Full textThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2020
Yu, Xinming. "Robust estimation for range image segmentation and fitting." Thesis, 1993. http://spectrum.library.concordia.ca/4144/1/NN84686.pdf.
Full textLe, Huu Minh. "New algorithmic developments in maximum consensus robust fitting." Thesis, 2018. http://hdl.handle.net/2440/115183.
Full textThesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 2018
Wong, Hoi Sim. "A preference analysis approach to robust geometric model fitting in computer vision." Thesis, 2013. http://hdl.handle.net/2440/82075.
Full textThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2013
Tran, Quoc Huy. "Robust parameter estimation in computer vision: geometric fitting and deformable registration." Thesis, 2014. http://hdl.handle.net/2440/86270.
Full textThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2014
Lin, ShihHsiang, and 林士翔. "Exploring the Use of Data Fitting and Clustering Techniques for Robust Speech Recognition." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/62281383807216796339.
Full text國立臺灣師範大學
資訊教育學系
95
Speech is the primary and the most convenient means of communication between individuals. It is also expected that automatic speech recognition (ASR) will play a more active role and will serve as the major human-machine interface for the interaction between people and different kinds of intelligent electronic devices in the near future. Most of the current state-of-the-art ASR systems can achieve quite high recognition performance levels in controlled laboratory environments. However, as the systems are moved out of the laboratory environments and deployed into real-world applications, the performance of the systems often degrade dramatically due to the reason that varying environmental effects will lead to a mismatch between the acoustic conditions of the training and test speech data. Therefore, robustness techniques have received great importance and attention in recent years. Robustness techniques in general fall into two aspects according to whether the methods’ orientation is either from feature domain or from their corresponding probability distributions. Methods of each have their own superiority and limitations. In this thesis, several attempts were made to integrate these two distinguishing information to improve the current speech robustness methods by using a novel data-fitting scheme. Firstly, cluster-based polynomial-fit histogram equalization (CPHEQ), based on histogram equalization and polynomial regression, was proposed to directly characterize the relationship between the speech feature vectors and their corresponding probability distributions by utilizing stereo speech training data. Moreover, we extended the idea of CPHEQ with some elaborate assumptions, and two different methods were derived as well, namely, polynomial-fit histogram equalization (PHEQ) and selective cluster-based polynomial-fit histogram equalization (SCPHEQ). PHEQ uses polynomial regression to efficiently approximate the inverse of the cumulative density functions of speech feature vectors for HEQ. It can avoid the need of high computation cost and large disk storage consumption caused by traditional HEQ methods. SCPHEQ is based on the missing feature theory and use polynomial regression to reconstruct unreliable feature components. All experiments were carried out on the Aurora-2 database and task. Experimental results shown that for clean-condition training, our method achieved a considerable word error rate reduction over the baseline system and also significantly outperformed the other robustness methods.
Hsiao, Yi, and 蕭奕. "Robust Model Fitting - Selection of Tuning Parameters in the Aspect of Gamma Clustering." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9escv5.
Full text國立臺灣大學
應用數學科學研究所
107
In 1995, Windham came out with an idea of weighted distribution in his thesis, Robustifying Model Fitting, and he used the idea to find a mean estimator when there are outliers in the original data. There is a tuning parameter in this estimator, and selecting the parameter will affect the mean estimate in the same data. In the same thesis, he also suggested a criterion of selecting the tuning parameter, but we found out that this criterion wasn’t doing well in some simulations. Considering the problem, we propose another criterion which can derive a better mean estimator. Besides, we can also apply this method to clustering problem.
Yeh, Han-Chun, and 葉漢軍. "A Study of Fitting Local Geoid Model by Robust Weighted Total Least Squares Method -A Case Study of Taichung Area." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/48876762392689670067.
Full text國立中興大學
土木工程學系所
105
The objective of this study involved using global navigation satellite system (GNSS) data to achieve reasonable point height accuracy. In this study, the orthometric heights of benchmarks were obtained from first order leveling of Taichung city and GNSS measurements of ellipsoid heights underwent fitting. A traditional fitting method was adopted, in which geoid height was built using generalized least squares combined with a curved surface fitting method. However, because generalized least square calculations do not take into consideration random errors that exist in coefficient matrices and observation vectors, weighted total generalized least square-based calculations were performed to solve these problems. In this study, the combination of weighted total generalized least squares and the quadratic curved surface fitting method improved on the traditional method by considering the covariance matrices of coefficient vectors and observation vectors. The solutions of the new model were subsequently analyzed, elevating point height accuracy to ±1.401 cm. The new method satisfies height accuracy requirements demanded in engineering surveys and provides valuable information for regional geoid height research.