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Dissertations / Theses on the topic 'Influential observation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Stål, Karin. "Identifying Influential Observations in Nonlinear Regression : a focus on parameter estimates and the score test." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-114324.

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This thesis contributes to influence analysis in nonlinear regression and in particular the detection of influential observations. The focus is on a regression model with a known mean function, which is nonlinear in its parameters and where the function is chosen according to the knowledge about the process generating the data. The error term in the regression model is assumed to be additive. The main goal of this thesis is to work out diagnostic measures for assessing the influence of observations on various results from a nonlinear regression analysis. The obtained results comprise diagnosti

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2

Ware, Lezlee J. "The highs and lows of visual salience and status influential factors in source monitoring decisions /." Ohio : Ohio University, 2009. http://www.ohiolink.edu/etd/view.cgi?ohiou1242239270.

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3

Hao, Chengcheng. "Explicit Influence Analysis in Crossover Models." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-107703.

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This dissertation develops influence diagnostics for crossover models. Mixed linear models and generalised mixed linear models are utilised to investigate continuous and count data from crossover studies, respectively. For both types of models, changes in the maximum likelihood estimates of parameters, particularly in the estimated treatment effect, due to minor perturbations of the observed data, are assessed. The novelty of this dissertation lies in the analytical derivation of influence diagnostics using decompositions of the perturbed mixed models. Consequently, the suggested influence dia

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4

關志威 and Chi-wai Kwan. "Influential observations in factor analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B29803895.

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5

Kwan, Chi-wai. "Influential observations in factor analysis /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19003110.

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6

Lefrancois, Bernard Carleton University Dissertation Mathematics. "Detecting overly influential observations in time series." Ottawa, 1989.

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7

Danilevicz, Ian Meneghel. "Detecting Influential observations in spatial models using Bregman divergence." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/104/104131/tde-13112018-160231/.

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How to evaluate if a spatial model is well ajusted to a problem? How to know if it is the best model between the class of conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models, including homoscedasticity and heteroscedasticity cases? To answer these questions inside Bayesian framework, we propose new ways to apply Bregman divergence, as well as recent information criteria as widely applicable information criterion (WAIC) and leave-one-out cross-validation (LOO). The functional Bregman divergence is a generalized form of the well known Kullback-Leiber (KL) divergence. Th

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8

Katshunga, Dominique. "Identifying outliers and influential observations in general linear regression models." Thesis, University of Cape Town, 2004. http://hdl.handle.net/11427/6772.

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Includes bibliographical references (leaves 140-149).<br>Identifying outliers and/or influential observations is a fundamental step in any statistical analysis, since their presence is likely to lead to erroneous results. Numerous measures have been proposed for detecting outliers and assessing the influence of observations on least squares regression results. Since outliers can arise in different ways, the above mentioned measures are based on motivational arguments and they are designed to measure the influence of observations on different aspects of various regression results. In what follo

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9

Li, Jianzhu. "Regression diagnostics for complex survey data identification of influential observations /." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7598.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Joint Program in Survey Methodology. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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10

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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11

Jones, Tamekia L. "A statistical approach identifying and limiting the effect of influential observations in linear regression." Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2009r/jones.pdf.

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12

Ramaboa, Kutlwano. "Contributions to Linear Regression diagnostics using the singular value decompostion: Measures to Indentify Outlying Observations, Influential Observations and Collinearity in Multivariate Data." Doctoral thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/4391.

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13

Martim, Simoni Fernanda. "Diagnostico de influencia em modelos de volatilidade estocastica." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306152.

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Orientadores: Mauricio Enrique Zevallos Herencia, Luiz Koodi Hotta<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica<br>Made available in DSpace on 2018-08-14T12:07:35Z (GMT). No. of bitstreams: 1 Martim_SimoniFernanda_M.pdf: 2441806 bytes, checksum: 4d34450ac590270c90e7eb66a293b51b (MD5) Previous issue date: 2009<br>Resumo: O diagnóstico de modelos é uma etapa fundamental para avaliar a qualidade do ajuste dos modelos. Nesse sentido, uma das ferramentas de diagnóstico mais importantes é a análise de influência. Peña (

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14

Reis, Sandra Santos dos 1983. "Diagnóstico em modelos de regressão linear e não-linear com erros simétricos." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306148.

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Orientador: Mauricio Enrique Zevallos Herencia<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica<br>Made available in DSpace on 2018-08-24T02:03:22Z (GMT). No. of bitstreams: 1 Reis_SandraSantosdos_M.pdf: 1897835 bytes, checksum: 24e50267c694dbcb380ddcfc9d7bdace (MD5) Previous issue date: 2013<br>Resumo: Neste trabalho discutimos a detecção de observações influentes em modelos simétricos lineares e não lineares. Em primeiro lugar é realizado um estudo de simulação para avaliar o desempenho de três métodos de estimação

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15

Lin, Yu-Chih, and 林禹致. "Our Lady of Guadalupe and Her Influential Symbol: A Semiological Observation." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/34470776596895857614.

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碩士<br>淡江大學<br>拉丁美洲研究所碩士班<br>98<br>Our Lady of Guadalupe is one of the most prestigious figures and most venerated religious icon in Mexico. Mexican people regard Her as the representative of the country; She is thus named the “Symbol of Mexico.” Guadalupe miraculously appears herself on a piece of cloth believed to contain numerous symbols. By functioning of the symbols, the Virgin of Guadalupe has gradually become an influential symbol through the process of the time. She has caused enormous influences in wide spheres such as politics, society, religion, culture…etc. Our Lady performs influen

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16

Ming-I, Chen, and 陳明義. "Diagnosing collinearity-influential observations." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/61470954446522990050.

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17

Wang, Lih-Min, and 王立民. "Detecting Influential Observations by Bootstrap Method." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/74114793168513195824.

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18

Lin, Yi-Fang, and 林意芳. "Using Clustering Methods to Detect Influential Observations." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/67560777891744585291.

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19

Silva, Tânia Isabel Oliveira da. "Dealing with influential observations in accounting empirical research." Master's thesis, 2009. http://hdl.handle.net/10071/1845.

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JEL Classification System: C51 – Econometric Modelling: Model Construction and Estimation; M41 – Accounting<br>The main objective of this dissertation is the study of influential observations and their treatment in the linear regression model. When the linear regression model is applied, the observations have different influence in the estimation results and their importance and influence will induce to wrong results if the empirical studies are not correctly treated. To detect these observations (influential observations) is indispensable to apply diagnostic measures and then proceed to t

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20

Ing, Shieh Jih, and 謝季英. "Some diagnostics for outliers and influential observations from." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/69253882513514172132.

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碩士<br>國立政治大學<br>應用數學研究所<br>82<br>In this thesis, some different diagnostic methodologies for outliers and influential observations from Bayesian point of view are proposed. We firstly derive the marginal posterior distribution of the mean-shift parameter $a=(a_1, \cdots , a_k)'$, then use the posterior mean of $a'a/k$ to detect the spurious data items. Secondly, we use the posterior total variance and generalized variance of $\beta$ as diagnostic criterions for outliers and influential

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21

Liu, Mei-Tsu, and 劉美足. "Iterative Approach in Diagnosing Influential Observations for Longitudinal Regression." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/39584200651198036967.

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22

"Identify influential observations in the estimation of covariance matrix." 2000. http://library.cuhk.edu.hk/record=b5895818.

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Wong Yuen Kwan Virginia.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.<br>Includes bibliographical references (leaves 85-86).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Deletion and Distance Measure --- p.6<br>Chapter 2.1 --- Mahalanobis and Cook's Distances --- p.6<br>Chapter 2.2 --- Defining New Measure Di --- p.8<br>Chapter 2.3 --- Derivation of cov(s(i) ´ؤ s) --- p.10<br>Chapter 3 --- Procedures for Detecting Influential Observations --- p.18<br>Chapter 3.1 --- The One-Step Method --- p.18<br>Chapter 3.1.1 --- The Method

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23

"Identify influential observations in the estimation of polyserial correlation." 2002. http://library.cuhk.edu.hk/record=b5891055.

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by Mannon Wong.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.<br>Includes bibliographical references (leaves 42-47).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Maximum Likelihood Estimations of Polyserial Correlations --- p.7<br>Chapter 3 --- Normal Curvature and the Conformal Normal Curvature of Lo- cal Influence --- p.12<br>Chapter 3.1 --- Normal Curvature --- p.14<br>Chapter 3.2 --- Conformal Normal Curvature as an Influential Measure --- p.16<br>Chapter 4 --- Influential Observations in the Estimations of Polyserial Corre-

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24

"Influential observations in the analysis of additive ipsative data." 2003. http://library.cuhk.edu.hk/record=b5891608.

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Wong Wai-Wan.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.<br>Includes bibliographical references (leaves 45-46).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgement --- p.iii<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Ipsative Data --- p.1<br>Chapter 1.2 --- Transformation --- p.2<br>Chapter 1.3 --- Influence Analysis --- p.3<br>Chapter 2 --- Ipsative Data --- p.5<br>Chapter 2.1 --- Additive Ipsative Data (AID) and Multiplicative Ipsative Data (MID) --- p.6<br>Chapter 2.1.1 --- Additive Ipsative Data (AID) --- p.6<br>Chapter 2.1.2

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25

Li, Yu Fen, and 李郁芬. "The detection of multiple influential observations in regression models." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/13877824299235754411.

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26

Liu, Aki, and 劉怡華. "Using the bootstrap to detect influential observations in logistic regression." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/26149345679870717586.

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27

Wen, Chih-Ping, and 溫志平. "The Performance of Cook''s Distance in Diagonalizing Influential Observations." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/45701278440463570979.

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28

Ya-Ping, Wang, and 王雅萍. "Detection of Influential Observations in Quantitative Trait Analysis Using Triad Families." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/46287063977804481677.

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碩士<br>輔仁大學<br>應用統計學研究所<br>92<br>Quantitative trait is aggressively discussed in the area of genetic analysis. The external phenotype of quantitative trait is influenced by genetic and environmental factors. The data may contain particular observations, termed outliers, that are not well fitted by the model, or observations, termed influential observations, that have an undue impact on the conclusions to be drawn from the analysis. In fact, an influential observation contribute more than other general one. Thus, it is necessary to find out influential observations as many as possible to obt

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29

Lee, Jae June. "A study on influential observations in linear regression and time series." 1990. http://catalog.hathitrust.org/api/volumes/oclc/22268233.html.

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Thesis (Ph. D.)--University of Wisconsin--Madison, 1990.<br>Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 122-128).

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30

"A forward search approach to identify influential observations in structural equation model." 2002. http://library.cuhk.edu.hk/record=b5891175.

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Lam Yuk Hing.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.<br>Includes bibliographical references (leaves 67-70).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Diagnostic Measure --- p.7<br>Chapter 2.1 --- A diagnostic measure di based on Cook's likelihood distance --- p.7<br>Chapter 2.2 --- The estimates 6 and θ (i) --- p.8<br>Chapter 2.3 --- "The one-step estimator,θ1 (i)" --- p.9<br>Chapter 3 --- Methods For Identifying Influential Observations --- p.11<br>Chapter 3.1 --- One-step method --- p.11<br>Chapter 3.2 --- Forward

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31

Nolet-Pigeon, Isabelle. "Finite population inference for population with a large number of zero-valued observations." Thesis, 2020. http://hdl.handle.net/1866/25110.

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Dans certaines enquêtes auprès des entreprises, il n'est pas rare de s'intéresser à estimer le total ou la moyenne d'une variable qui, par sa nature, prend souvent une valeur nulle. En présence d'une grande proportion de valeurs nulles, les estimateurs usuels peuvent s'avérer inefficaces. Dans ce mémoire, nous étudions les propriétés des estimateurs habituels pour des populations exhibant une grande proportion de zéros. Dans un contexte d'une approche fondée sur le modèle, nous présentons des prédicteurs robustes à la présence de valeurs influentes pour ce type de populations. Finalement, nous

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