Thèses sur le sujet « Regression analysis »
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Sullwald, Wichard. « Grain regression analysis ». Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86526.
Texte intégralENGLISH ABSTRACT: Grain regression analysis forms an essential part of solid rocket motor simulation. In this thesis a numerical grain regression analysis module is developed as an alternative to cumbersome and time consuming analytical methods. The surface regression is performed by the level-set method, a numerical interface advancement scheme. A novel approach to the integration of the surface area and volume of a numerical interface, as defined implicitly in a level-set framework, by means of Monte-Carlo integration is proposed. The grain regression module is directly coupled to a quasi -1D internal ballistics solver in an on-line fashion, in order to take into account the effects of spatially varying burn rate distributions. A multi-timescale approach is proposed for the direct coupling of the two solvers.
AFRIKAANSE OPSOMMING: Gryn regressie analise vorm ’n integrale deel van soliede vuurpylmotor simulasie. In hierdie tesis word ’n numeriese gryn regressie analise model, as ’n alternatief tot dikwels omslagtige en tydrowende analitiese metodes, ontwikkel. Die oppervlak regressie word deur die vlak-set metode, ’n numeriese koppelvlak beweging skema uitgevoer. ’n Nuwe benadering tot die integrasie van die buite-oppervlakte en volume van ’n implisiete numeriese koppelvlak in ’n vlakset raamwerk, deur middel van Monte Carlo-integrasie word voorgestel. Die gryn regressie model word direk en aanlyn aan ’n kwasi-1D interne ballistiek model gekoppel, ten einde die uitwerking van ruimtelik-wisselende brand-koers in ag te neem. ’n Multi-tydskaal benadering word voorgestel vir die direkte koppeling van die twee modelle.
Dai, Elin, et Lara Güleryüz. « Factors that influence condominium pricing in Stockholm : A regression analysis : A regression analysis ». Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254235.
Texte intégralDenna studie ämnar till att undersöka vilka faktorer som är av betydelse när syftet är att förutsäga prissättningen på bostadsrätter i Stockholms innerstad. Genom att använda multipel linjär regression, transformation av responsvariabeln, samt en mängd olika metoder för att förfina modellen, togs en slutgiltig, out of sample-validerad modell med ett 95%-konfidensintervall fram. För att genomföra de statistiska metoderna användes programmet R. Denna studie är avgränsad till de distrikt i Stockholms innerstad vars postnummer varierar mellan 112-118, därav är det viktigt att modellen endast appliceras på dessa områden eftersom de är inkluderade i modellen som regressorer. Tidsperioden inom vilket slutpriserna analyserades var mellan januari 2014 och april 2019, i vilket valutans volatilitet inte har analyserats som en ekonomisk påverkande faktor. Den slutgiltiga modellen innefattar de följande variablerna: våning, boarea, månadsavgift, konstruktionsår, distrikt.
Zuo, Yanling. « Monotone regression functions ». Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29457.
Texte intégralScience, Faculty of
Statistics, Department of
Graduate
Ryu, Duchwan. « Regression analysis with longitudinal measurements ». Texas A&M University, 2005. http://hdl.handle.net/1969.1/2398.
Texte intégralCampbell, Ian. « The geometry of regression analysis ». Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5755.
Texte intégralWiencierz, Andrea. « Regression analysis with imprecise data ». Diss., Ludwig-Maximilians-Universität München, 2013. http://nbn-resolving.de/urn:nbn:de:bvb:19-166786.
Texte intégralMethoden der statistischen Datenanalyse setzen in der Regel voraus, dass die vorhandenen Daten präzise und korrekte Beobachtungen der untersuchten Größen sind. Häufig können aber bei praktischen Studien die interessierenden Werte nur unvollständig oder unscharf beobachtet werden. Die vorliegende Arbeit beschäftigt sich mit der Fragestellung, wie Regressionsanalysen bei unscharfen Daten sinnvoll durchgeführt werden können. Zunächst werden verschiedene Ansätze zum Umgang mit unscharf beobachteten Variablen diskutiert, bevor eine neue Likelihood-basierte Methodologie für Regression mit unscharfen Daten eingeführt wird. Als Ergebnis der Regressionsanalyse wird bei diesem Ansatz keine einzelne Regressionsfunktion angestrebt, sondern die gesamte Menge aller anhand der Daten plausiblen Regressionsfunktionen betrachtet, welche als Konfidenzbereich für den untersuchten Zusammenhang interpretiert werden kann. Im darauffolgenden Kapitel wird im Rahmen dieser Methodologie eine Regressionsmethode entwickelt, die sehr allgemein bezüglich der Form der unscharfen Beobachtungen, der möglichen Verteilungen der Zufallsgrößen sowie der Form des funktionalen Zusammenhangs zwischen den untersuchten Variablen ist. Zudem werden ein exakter Algorithmus für den Spezialfall der linearen Einfachregression mit Intervalldaten entwickelt und einige statistische Eigenschaften der Methode näher untersucht. Dabei stellt sich heraus, dass die entwickelte Regressionsmethode sowohl robust im Sinne eines hohen Bruchpunktes ist, als auch sehr verlässliche Erkenntnisse hervorbringt, was sich in einer hohen Überdeckungswahrscheinlichkeit der Ergebnismenge äußert. Darüber hinaus wird in einem weiteren Kapitel ein in der Literatur vorgeschlagener Alternativansatz ausführlich diskutiert, der auf Support Vector Regression aufbaut. Dieser wird durch Einbettung in den methodologischen Rahmen des vorher eingeführten Likelihood-basierten Ansatzes weiter verallgemeinert. Abschließend werden die behandelten Regressionsmethoden auf zwei praktische Probleme angewandt.
Jeffrey, Stephen Glenn. « Quantile regression and frontier analysis ». Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/47747/.
Texte intégralRanganai, Edmore. « Aspects of model development using regression quantiles and elemental regressions ». Thesis, Stellenbosch : Stellenbosch University, 2007. http://hdl.handle.net/10019.1/18668.
Texte intégralENGLISH ABSTRACT: It is well known that ordinary least squares (OLS) procedures are sensitive to deviations from the classical Gaussian assumptions (outliers) as well as data aberrations in the design space. The two major data aberrations in the design space are collinearity and high leverage. Leverage points can also induce or hide collinearity in the design space. Such leverage points are referred to as collinearity influential points. As a consequence, over the years, many diagnostic tools to detect these anomalies as well as alternative procedures to counter them were developed. To counter deviations from the classical Gaussian assumptions many robust procedures have been proposed. One such class of procedures is the Koenker and Bassett (1978) Regressions Quantiles (RQs), which are natural extensions of order statistics, to the linear model. RQs can be found as solutions to linear programming problems (LPs). The basic optimal solutions to these LPs (which are RQs) correspond to elemental subset (ES) regressions, which consist of subsets of minimum size to estimate the necessary parameters of the model. On the one hand, some ESs correspond to RQs. On the other hand, in the literature it is shown that many OLS statistics (estimators) are related to ES regression statistics (estimators). Therefore there is an inherent relationship amongst the three sets of procedures. The relationship between the ES procedure and the RQ one, has been noted almost “casually” in the literature while the latter has been fairly widely explored. Using these existing relationships between the ES procedure and the OLS one as well as new ones, collinearity, leverage and outlier problems in the RQ scenario were investigated. Also, a lasso procedure was proposed as variable selection technique in the RQ scenario and some tentative results were given for it. These results are promising. Single case diagnostics were considered as well as their relationships to multiple case ones. In particular, multiple cases of the minimum size to estimate the necessary parameters of the model, were considered, corresponding to a RQ (ES). In this way regression diagnostics were developed for both ESs and RQs. The main problems that affect RQs adversely are collinearity and leverage due to the nature of the computational procedures and the fact that RQs’ influence functions are unbounded in the design space but bounded in the response variable. As a consequence of this, RQs have a high affinity for leverage points and a high exclusion rate of outliers. The influential picture exhibited in the presence of both leverage points and outliers is the net result of these two antagonistic forces. Although RQs are bounded in the response variable (and therefore fairly robust to outliers), outlier diagnostics were also considered in order to have a more holistic picture. The investigations used comprised analytic means as well as simulation. Furthermore, applications were made to artificial computer generated data sets as well as standard data sets from the literature. These revealed that the ES based statistics can be used to address problems arising in the RQ scenario to some degree of success. However, due to the interdependence between the different aspects, viz. the one between leverage and collinearity and the one between leverage and outliers, “solutions” are often dependent on the particular situation. In spite of this complexity, the research did produce some fairly general guidelines that can be fruitfully used in practice.
AFRIKAANSE OPSOMMING: Dit is bekend dat die gewone kleinste kwadraat (KK) prosedures sensitief is vir afwykings vanaf die klassieke Gaussiese aannames (uitskieters) asook vir data afwykings in die ontwerpruimte. Twee tipes afwykings van belang in laasgenoemde geval, is kollinearitiet en punte met hoë hefboom waarde. Laasgenoemde punte kan ook kollineariteit induseer of versteek in die ontwerp. Na sodanige punte word verwys as kollinêre hefboom punte. Oor die jare is baie diagnostiese hulpmiddels ontwikkel om hierdie afwykings te identifiseer en om alternatiewe prosedures daarteen te ontwikkel. Om afwykings vanaf die Gaussiese aanname teen te werk, is heelwat robuuste prosedures ontwikkel. Een sodanige klas van prosedures is die Koenker en Bassett (1978) Regressie Kwantiele (RKe), wat natuurlike uitbreidings is van rangorde statistieke na die lineêre model. RKe kan bepaal word as oplossings van lineêre programmeringsprobleme (LPs). Die basiese optimale oplossings van hierdie LPs (wat RKe is) kom ooreen met die elementale deelversameling (ED) regressies, wat bestaan uit deelversamelings van minimum grootte waarmee die parameters van die model beraam kan word. Enersyds geld dat sekere EDs ooreenkom met RKe. Andersyds, uit die literatuur is dit bekend dat baie KK statistieke (beramers) verwant is aan ED regressie statistieke (beramers). Dit impliseer dat daar dus ‘n inherente verwantskap is tussen die drie klasse van prosedures. Die verwantskap tussen die ED en die ooreenkomstige RK prosedures is redelik “terloops” van melding gemaak in die literatuur, terwyl laasgenoemde prosedures redelik breedvoerig ondersoek is. Deur gebruik te maak van bestaande verwantskappe tussen ED en KK prosedures, sowel as nuwes wat ontwikkel is, is kollineariteit, punte met hoë hefboom waardes en uitskieter probleme in die RK omgewing ondersoek. Voorts is ‘n lasso prosedure as veranderlike seleksie tegniek voorgestel in die RK situasie en is enkele tentatiewe resultate daarvoor gegee. Hierdie resultate blyk belowend te wees, veral ook vir verdere navorsing. Enkel geval diagnostiese tegnieke is beskou sowel as hul verwantskap met meervoudige geval tegnieke. In die besonder is veral meervoudige gevalle beskou wat van minimum grootte is om die parameters van die model te kan beraam, en wat ooreenkom met ‘n RK (ED). Met sodanige benadering is regressie diagnostiese tegnieke ontwikkel vir beide EDs en RKe. Die belangrikste probleme wat RKe negatief beinvloed, is kollineariteit en punte met hoë hefboom waardes agv die aard van die berekeningsprosedures en die feit dat RKe se invloedfunksies begrensd is in die ruimte van die afhanklike veranderlike, maar onbegrensd is in die ontwerpruimte. Gevolglik het RKe ‘n hoë affiniteit vir punte met hoë hefboom waardes en poog gewoonlik om uitskieters uit te sluit. Die finale uitset wat verkry word wanneer beide punte met hoë hefboom waardes en uitskieters voorkom, is dan die netto resultaat van hierdie twee teenstrydige pogings. Alhoewel RKe begrensd is in die onafhanklike veranderlike (en dus redelik robuust is tov uitskieters), is uitskieter diagnostiese tegnieke ook beskou om ‘n meer holistiese beeld te verkry. Die ondersoek het analitiese sowel as simulasie tegnieke gebruik. Voorts is ook gebruik gemaak van kunsmatige datastelle en standard datastelle uit die literatuur. Hierdie ondersoeke het getoon dat die ED gebaseerde statistieke met ‘n redelike mate van sukses gebruik kan word om probleme in die RK omgewing aan te spreek. Dit is egter belangrik om daarop te let dat as gevolg van die interafhanklikheid tussen kollineariteit en punte met hoë hefboom waardes asook dié tussen punte met hoë hefboom waardes en uitskieters, “oplossings” dikwels afhanklik is van die bepaalde situasie. Ten spyte van hierdie kompleksiteit, is op grond van die navorsing wat gedoen is, tog redelike algemene riglyne verkry wat nuttig in die praktyk gebruik kan word.
Lo, Sau Yee. « Measurement error in logistic regression model / ». View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?MATH%202004%20LO.
Texte intégralIncludes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.
Kulich, Michal. « Additive hazards regression with incomplete covariate data / ». Thesis, Connect to this title online ; UW restricted, 1997. http://hdl.handle.net/1773/9562.
Texte intégralMeless, Dejen. « Test Cycle Optimization using Regression Analysis ». Thesis, Linköping University, Automatic Control, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54809.
Texte intégralIndustrial robots make up an important part in today’s industry and are assigned to a range of different tasks. Needless to say, businesses need to rely on their machine park to function as planned, avoiding stops in production due to machine failures. This is where fault detection methods play a very important part. In this thesis a specific fault detection method based on signal analysis will be considered. When testing a robot for fault(s), a specific test cycle (trajectory) is executed in order to be able to compare test data from different test occasions. Furthermore, different test cycles yield different measurements to analyse, which may affect the performance of the analysis. The question posed is: Can we find an optimal test cycle so that the fault is best revealed in the test data? The goal of this thesis is to, using regression analysis, investigate how the presently executed test cycle in a specific diagnosis method relates to the faults that are monitored (in this case a so called friction fault) and decide if a different one should be recommended. The data also includes representations of two disturbances.
The results from the regression show that the variation in the test quantities utilised in the diagnosis method are not explained by neither the friction fault or the test cycle. It showed that the disturbances had too large effect on the test quantities. This made it impossible to recommend a different (optimal) test cycle based on the analysis.
Lee, Ho-Jin. « Functional data analysis : classification and regression ». Texas A&M University, 2004. http://hdl.handle.net/1969.1/2805.
Texte intégralLu, Xuewen. « Semiparametric regression models in survival analysis ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0030/NQ27458.pdf.
Texte intégralSulieman, Hana. « Parametric sensitivity analysis in nonlinear regression ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0004/NQ27858.pdf.
Texte intégralOlsén, Johan. « Logistic regression modelling for STHR analysis ». Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-148971.
Texte intégralJin, Yi. « Regression Analysis of University Giving Data ». Digital WPI, 2007. https://digitalcommons.wpi.edu/etd-theses/1.
Texte intégralLi, Yi-Hwei. « Regression analysis of failure time data / ». The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487694702784082.
Texte intégralAgard, David B. « Robust inferential procedures applied to regression ». Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-10132005-152518/.
Texte intégralBurnham, Alison J. « Multivariate latent variable regression : modelling and estimation / ». *McMaster only, 1997.
Trouver le texte intégral鄧明基 et Ming-kei Tang. « Assessment of influence in multivariate regression ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31219949.
Texte intégralTang, Ming-kei. « Assessment of influence in multivariate regression / ». Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19853658.
Texte intégralMitchell, Napoleon. « Outliers and Regression Models ». Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc279029/.
Texte intégralZhang, Zhigang. « Nonproportional hazards regression models for survival analysis / ». free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144473.
Texte intégralDetwiler, Dana. « Microcomputer implementation of robust regression techniques ». Master's thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-03302010-020305/.
Texte intégralMcGlothlin, Anna E. Stamey James D. Seaman John Weldon. « Logistic regression with misclassified response and covariate measurement error a Bayesian approach / ». Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5101.
Texte intégralLiu, Hai Chan Kung-sik. « Semiparametric regression analysis of zero-inflated data ». Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/308.
Texte intégralRatnasingam, Suthakaran. « Sequential Change-point Detection in Linear Regression and Linear Quantile Regression Models Under High Dimensionality ». Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu159050606401363.
Texte intégralLi, Lingzhu. « Model checking for general parametric regression models ». HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/654.
Texte intégralKriner, Monika. « Survival Analysis with Multivariate adaptive Regression Splines ». Diss., lmu, 2007. http://nbn-resolving.de/urn:nbn:de:bvb:19-73695.
Texte intégralCarvalho, Renato de Souza. « Nonlinear regression application to well test analysis / ». Access abstract and link to full text, 1993. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/9416602.
Texte intégralWiencierz, Andrea [Verfasser]. « Regression analysis with imprecise data / Andrea Wiencierz ». München : Verlag Dr. Hut, 2014. http://d-nb.info/1050331575/34.
Texte intégralFlogvall, Carl, et Stefan Nordenskjöld. « A regression analysis of NHL cap hits ». Thesis, KTH, Matematik (Inst.), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-155194.
Texte intégralBjartmar, Hylta Sanna, et Emma Lundquist. « Pricing Single Malt Whisky : A Regression Analysis ». Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189001.
Texte intégralDenna kandidatuppsats undersöker de faktorer som påverkar priset på whisky. Multipel regressionsanalys används för att modellera förhållandet mellan de identifierade variablerna som tros påverka priset på whisky. Vidare diskuteras den optimala marknadsföringsstrategi f ̈or whiskyproducenter i regionerna Islay och Campbeltown. Analysen baseras på en Marknadsmix-analys för whisky i Skottland. Detta följs av Porters femkraftsmodell med fokus på regionerna Islay och Campeltown. Slutligen sammanfattas resultaten i en rekommendation av marknadsföringsstrategi för producenter i regionerna Islay och Campbeltown. Resultatet från regressionsanalysen visar att kovariaterna alkoholhalt och regioner har störst påverkan på priset. De små regionerna Islay och Campbeltown, med få destillerier, har en stark positiv inverkan på priset. Whisky från ospecificerade regioner i Skottland har däremot en negativ inverkan. Alkoholhalten har en positiv, icke-linjär inverkan på priset. I kandidatuppsatsen dras slutsatsen att det positiva sambandet mellan alkohol och pris ej kan förklaras av Sveriges alkoholskatt, utan att kunder är redo att betala mer för en whisky med högre alkoholhalt. Vidare konstateras att små regioner med få destillerier resulterar i ett högre pris på whisky. Whiskyns ursprung och tradition har en stor inverkan på pris och bör därför betonas i marknadsföringen.
SILVA, Ana Hermínia Andrade e. « Essays on data transformation and regression analysis ». Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/24585.
Texte intégralMade available in DSpace on 2018-05-10T18:25:09Z (GMT). No. of bitstreams: 1 TESE Ana Hermínia Andrade e Silva.pdf: 1090771 bytes, checksum: 8e2a4ceb20b4376bc5081da0e2216081 (MD5) Previous issue date: 2017-02-14
CAPES
Na presente tese de doutorado, apresentamos estimadores dos parâmetros que indexam as transformações de Manly e Box-Cox, usadas para transformar a variável resposta do modelo de regressão linear, e também testes de hipóteses. A tese é composta por quatro capítulos. No Capítulo 2, desenvolvemos dois testes escore para a transformação de Box-Cox e dois testes escore para a transformação de Manly (Ts e Ts0), para estimar os parâmetros das transformações. A principal desvantagem da transformação de Box-Cox é que ela só pode ser aplicada a dados não negativos. Por outro lado, a transformação de Manly pode ser aplicada a qualquer dado real. Utilizamos simulações de Monte Carlo para avaliarmos os desempenhos dos estimadores e testes propostos. O principal resultado é que o teste Ts teve melhor desempenho que o teste Ts0, tanto em tamanho quanto em poder. No Capítulo 3 apresentamos refinamentos para os testes escore desenvolvidos no Capítulo 2 usando o fast double bootstrap. Seu desempenho foi avaliado via simulações de Monte Carlo. O resultado principal é que o teste fast double bootstrap é superior ao teste bootstrap clássico. No Capítulo 4 propusemos sete estimadores não-paramétricos para estimar os parâmetros que indexam as transformações de Box-Cox e Manly, com base em testes de normalidade. Realizamos simulações de Monte Carlo em três casos. Comparamos os desempenhos dos estimadores não-paramétricos com o do estimador de máxima verosimilhança (EMV). No terceiro caso, pelo menos um estimador não-paramétrico apresenta desempenho superior ao EMV.
In this PhD dissertation we develop estimators and tests on the parameters that index the Manly and Box-Cox transformations, which are used to transform the response variable of the linear regression model. It is composed of four chapters. In Chapter 2 we develop two score tests for the Box-Cox and Manly transformations (Ts and Ts0). The main disadvantage of the Box-Cox transformation is that it can only be applied to positive data. In contrast, Manly transformation can be applied to any real data. We performed Monte Carlo simulations to evaluate the finite sample performances of the pro-posed estimators and tests. The results show that the Ts test outperforms Ts0 test, both in size and in power. In Chapter 3, we present refinements for the score tests developed in Chapter 2 using the fast double bootstrap. We performed Monte Carlo simulations to evaluate the effectiveness of such a bootstrap scheme. The main result is that the fast double bootstrap is superior to the standard bootstrap. In Chapter 4, we propose seven nonparametric estimators for the parameters that index the Box-Cox and Manly transformations, based on normality tests. We performed Monte Carlo simulations in three cases. We compare performances of the nonparametric estimators with that of the maximum likelihood estimator (MLE).
Ah-Kine, Pascal Soon Shien. « Simultaneous confidence bands in linear regression analysis ». Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/167557/.
Texte intégralHu, ChungLynn. « Nonignorable nonresponse in the logistic regression analysis / ». The Ohio State University, 1998. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487950153601414.
Texte intégralWhite, Lisa A. « Predicting hospital admissions with Poisson regression analysis ». Thesis, Monterey, Calif. : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Jun/09Jun%5FWhite.pdf.
Texte intégralThesis Advisor(s): Whitaker, Lyn R. "June 2009." Description based on title screen as viewed on July 14, 2009. Author(s) subject terms: Poisson regression, MTF, military treatment facility, hospital admissions. Includes bibliographical references (p. 53-54). Also available in print.
Liu, Hai. « Semiparametric regression analysis of zero-inflated data ». Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/308.
Texte intégralOrmerod, John T. Mathematics & Statistics Faculty of Science UNSW. « On semiparametric regression and data mining ». Awarded by:University of New South Wales. Mathematics & ; Statistics, 2008. http://handle.unsw.edu.au/1959.4/40913.
Texte intégralLai, Pik-ying, et 黎碧瑩. « Lp regression under general error distributions ». Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30287844.
Texte intégralWang, Xue. « Empirical Bayes block shrinkage for wavelet regression ». Thesis, University of Nottingham, 2006. http://eprints.nottingham.ac.uk/13516/.
Texte intégralMcClelland, Robyn L. « Regression based variable clustering for data reduction / ». Thesis, Connect to this title online ; UW restricted, 2000. http://hdl.handle.net/1773/9611.
Texte intégralZhou, Qi Jessie. « Inferential methods for extreme value regression models / ». *McMaster only, 2002.
Trouver le texte intégralDaud, Isa Bin. « Influence diagnostics in regression with censored data ». Thesis, Loughborough University, 1987. https://dspace.lboro.ac.uk/2134/11728.
Texte intégralHuang, Jian. « Estimation in regression models with interval censoring / ». Thesis, Connect to this title online ; UW restricted, 1994. http://hdl.handle.net/1773/8950.
Texte intégralKao, Tzu-Yuan, et 高子瑗. « Effect Regression Analysis ». Thesis, 2015. http://ndltd.ncl.edu.tw/handle/05416517583972590707.
Texte intégral國立交通大學
統計學研究所
103
Specification of direct and total effects have been mistreated for years that will mislead to incorrect conclusion about statistical inferences about indirect effect. We prove that for detection of presence of mediation, it requires only to test the association between the predictor and mediator giving the reason that how Baron and Kenny (1986)’s three steps of tests has low power. We also provide theoretical proofs for the observation of Hayes (2009) for absence of total effect but there is indirect effect and the observation of Palmatier et al. (2009) for total effect containing no indirect effect. With regression function being formulated in terms of distributional parameters of variables of response, predictor and mediator, we allow to quantify the information of mediation existed in their joint distribution to be removed to specify unambiguous direct effect and total effects. One important discovery is that the mediation causes not only affect the regression slope parameters but also the regression intercept. So, instead of limited use of slope type effects, we introduce regression setup direct, indirect and total effects expanding to the scope of effect prediction. Statistical inferences for these effect regressions are introduced and evaluated.
Chang, Teng-Kai, et 張登凱. « Interaction Regression Analysis ». Thesis, 2017. http://ndltd.ncl.edu.tw/handle/t9ezhu.
Texte intégral國立交通大學
統計學研究所
105
Classically researchers verify for presence of interaction, the effect of interdependence, though detection for presence of product term in regression function on record product derivative of this function. We first verify the appropriateness of product term criterion by introducing a first order derivative criterion. We then investigate if the indirect effect in causal inference can interpret the interaction effect. We also study the presence of switch line that divides the regression function into two parts: one with synergistic effect and one with antagonistic effect. Finally we conduct a data analysis.
(8039492), Huyunting Huang Sr. « Regression Principal Analysis ». Thesis, 2019.
Trouver le texte intégral« Supervised ridge regression in high dimensional linear regression ». 2013. http://library.cuhk.edu.hk/record=b5549319.
Texte intégralIn the field of statistical learning, we usually have a lot of features to determine the behavior of some response. For example in gene testing problems we have lots of genes as features and their relations with certain disease need to be determined. Without specific knowledge available, the most simple and fundamental way to model this kind of problem would be a linear model. There are many existing method to solve linear regression, like conventional ordinary least squares, ridge regression and LASSO (least absolute shrinkage and selection operator). Let N denote the number of samples and p denote the number of predictors, in ordinary settings where we have enough samples (N > p), ordinary linear regression methods like ridge regression will usually give reasonable predictions for the future values of the response. In the development of modern statistical learning, it's quite often that we meet high dimensional problems (N << p), like documents classification problems and microarray data testing problems. In high-dimensional problems it is generally quite difficult to identify the relationship between the predictors and the response without any further assumptions. Despite the fact that there are many predictors for prediction, most of the predictors are actually spurious in a lot of real problems. A predictor being spurious means that it is not directly related to the response. For example in microarray data testing problems, millions of genes may be available for doing prediction, but only a few hundred genes are actually related to the target disease. Conventional techniques in linear regression like LASSO and ridge regression both have their limitations in high-dimensional problems. The LASSO is one of the "state of the art technique for sparsity recovery, but when applied to high-dimensional problems, LASSO's performance is degraded a lot due to the presence of the measurement noise, which will result in high variance prediction and large prediction error. Ridge regression on the other hand is more robust to the additive measurement noise, but has its obvious limitation of not being able to separate true predictors from spurious predictors. As mentioned previously in many high-dimensional problems a large number of the predictors could be spurious, then in these cases ridge's disability in separating spurious and true predictors will result in poor interpretability of the model as well as poor prediction performance. The new technique that I will propose in this thesis aims to accommodate for the limitations of these two methods thus resulting in more accurate and stable prediction performance in a high-dimensional linear regression problem with signicant measurement noise. The idea is simple, instead of the doing a single step regression, we divide the regression procedure into two steps. In the first step we try to identify the seemingly relevant predictors and those that are obviously spurious by calculating the uni-variant correlations between the predictors and the response. We then discard those predictors that have very small or zero correlation with the response. After the first step we should have obtained a reduced predictor set. In the second step we will perform a ridge regression between the reduced predictor set and the response, the result of this ridge regression will then be our desired output. The thesis will be organized as follows, first I will start with a literature review about the linear regression problem and introduce in details about the ridge and LASSO and explain more precisely about their limitations in high-dimensional problems. Then I will introduce my new method called supervised ridge regression and show the reasons why it should dominate the ridge and LASSO in high-dimensional problems, and some simulation results will be demonstrated to strengthen my argument. Finally I will conclude with the possible limitations of my method and point out possible directions for further investigations.
Detailed summary in vernacular field only.
Zhu, Xiangchen.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2013.
Includes bibliographical references (leaves 68-69).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstracts also in Chinese.
Chapter 1. --- BASICS ABOUT LINEAR REGRESSION --- p.2
Chapter 1.1 --- Introduction --- p.2
Chapter 1.2 --- Linear Regression and Least Squares --- p.2
Chapter 1.2.1 --- Standard Notations --- p.2
Chapter 1.2.2 --- Least Squares and Its Geometric Meaning --- p.4
Chapter 2. --- PENALIZED LINEAR REGRESSION --- p.9
Chapter 2.1 --- Introduction --- p.9
Chapter 2.2 --- Deficiency of the Ordinary Least Squares Estimate --- p.9
Chapter 2.3 --- Ridge Regression --- p.12
Chapter 2.3.1 --- Introduction to Ridge Regression --- p.12
Chapter 2.3.2 --- Expected Prediction Error And Noise Variance Decomposition of Ridge Regression --- p.13
Chapter 2.3.3 --- Shrinkage effects on different principal components by ridge regression --- p.18
Chapter 2.4 --- The LASSO --- p.22
Chapter 2.4.1 --- Introduction to the LASSO --- p.22
Chapter 2.4.2 --- The Variable Selection Ability and Geometry of LASSO --- p.25
Chapter 2.4.3 --- Coordinate Descent Algorithm to solve for the LASSO --- p.28
Chapter 3. --- LINEAR REGRESSION IN HIGH-DIMENSIONAL PROBLEMS --- p.31
Chapter 3.1 --- Introduction --- p.31
Chapter 3.2 --- Spurious Predictors and Model Notations for High-dimensional Linear Regression --- p.32
Chapter 3.3 --- Ridge and LASSO in High-dimensional Linear Regression --- p.34
Chapter 4. --- THE SUPERVISED RIDGE REGRESSION --- p.39
Chapter 4.1 --- Introduction --- p.39
Chapter 4.2 --- Definition of Supervised Ridge Regression --- p.39
Chapter 4.3 --- An Underlying Latent Model --- p.43
Chapter 4.4 --- Ridge LASSO and Supervised Ridge Regression --- p.45
Chapter 4.4.1 --- LASSO vs SRR --- p.45
Chapter 4.4.2 --- Ridge regression vs SRR --- p.46
Chapter 5. --- TESTING AND SIMULATION --- p.49
Chapter 5.1 --- A Simulation Example --- p.49
Chapter 5.2 --- More Experiments --- p.54
Chapter 5.2.1 --- Correlated Spurious and True Predictors --- p.55
Chapter 5.2.2 --- Insufficient Amount of Data Samples --- p.59
Chapter 5.2.3 --- Low Dimensional Problem --- p.62
Chapter 6. --- CONCLUSIONS AND DISCUSSIONS --- p.66
Chapter 6.1 --- Conclusions --- p.66
Chapter 6.2 --- References and Related Works --- p.68
Li, Chia-hua, et 李嘉華. « Influence Analysis for ROC Regression ». Thesis, 2008. http://ndltd.ncl.edu.tw/handle/34336694525320073168.
Texte intégral國立中正大學
統計科學所
96
Receiver operating characteristic (ROC) curve is a technique for evaluate screening or diagnostic tests with not binary test results. ROC regression analysis provides a method to evaluate covariate effects which may infuence the test accuracy. In ROC regression analysis, if we perturb one case in data, the ROC regression estimators estimated by using perturbed data may be more different than by complete data. The character of estimators may be determined by this case while most of the data is essentially ignored. Therefore, we have interests in studying the influence of unusual observations in ROC regression analysis. The perturbation theory provides a useful tool in sensitivity analysis. In this thesis, we develop single-perturbation influence functions to detect the influential points for ROC regression. A simulated data and a real data are provided to illustrate the applications of our approach.