Dissertations / Theses on the topic 'Generalised smoothing'
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Li, Yuyi. "Empirical likelihood with applications in time series." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/empirical-likelihood-with-applications-in-time-series(29c74808-f784-4306-8df9-26f45b30b553).html.
Full textSheppard, Therese. "Extending covariance structure analysis for multivariate and functional data." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/extending-covariance-structure-analysis-for-multivariate-and-functional-data(e2ad7f12-3783-48cf-b83c-0ca26ef77633).html.
Full textBaker, Jannah F. "Bayesian spatiotemporal modelling of chronic disease outcomes." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104455/1/Jannah_Baker_Thesis.pdf.
Full textUtami, Zuliana Sri. "Penalized regression methods with application to generalized linear models, generalized additive models, and smoothing." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/20908/.
Full textWang, Xiaohui 1969. "Combining the generalized linear model and spline smoothing to analyze examination data." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=26176.
Full textThe statistical tools used in this thesis are the generalized linear models and spline smoothing. The method tries to combine the advantages of both parametric modeling and nonparametric regression to get a good estimate of the item characteristic curve. A special basis for spline smoothing is proposed which is motivated by the properties of the item characteristic curve. Based on the estimate of the item characteristic curve by this method, a more stable estimate of the item information function can be generated. Some illustrative analysis of simulated data are presented. The results seem to indicate that this method does have the advantages of both parametric modeling and nonparametric regression: it is faster to compute and more flexible than the methods using parametric models, for example, the three-parameter model in psychometrics, and on the other hand, it generates more stable estimate of derivatives than the purely nonparametric regression.
Cao, Jiguo. "Generalized profiling method and the applications to adaptive penalized smoothing, generalized semiparametric additive models and estimating differential equations." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102483.
Full textFirst, penalized smoothing is extended by allowing for a functional smoothing parameter, which is adaptive to the geometry of the underlying curve, which is called adaptive penalized smoothing. In the first level of optimization, the smooth ing coefficients are local parameters, estimated by minimizing sum of squared errors, conditional on the functional smoothing parameter. In the second level, the functional smoothing parameter is a complexity parameter, estimated by minimizing generalized cross-validation (GCV), treating the smoothing coefficients as explicit functions of the functional smoothing parameter. Adaptive penalized smoothing is shown to obtain better estimates for fitting functions and their derivatives.
Next, the generalized semiparametric additive models are estimated by three levels of optimization, allowing response variables in any kind of distribution. In the first level, the nonparametric functional parameters are nuisance parameters, estimated by maximizing the regularized likelihood function, conditional on the linear coefficients and the smoothing parameter. In the second level, the linear coefficients are structural parameters, estimated by maximizing the likelihood function with the nonparametric functional parameters treated as implicit functions of linear coefficients and the smoothing parameter. In the third level, the smoothing parameter is a complexity parameter, estimated by minimizing the approximated GCV with the linear coefficients treated as implicit functions of the smoothing parameter. This method is applied to estimate the generalized semiparametric additive model for the effect of air pollution on the public health.
Finally, parameters in differential equations (DE's) are estimated from noisy data with the generalized profiling method. In the first level of optimization, fitting functions are estimated to approximate DE solutions by penalized smoothing with the penalty term defined by DE's, fixing values of DE parameters. In the second level of optimization, DE parameters are estimated by weighted sum of squared errors, with the smoothing coefficients treated as an implicit function of DE parameters. The effects of the smoothing parameter on DE parameter estimates are explored and the optimization criteria for smoothing parameter selection are discussed. The method is applied to fit the predator-prey dynamic model to biological data, to estimate DE parameters in the HIV dynamic model from clinical trials, and to explore dynamic models for thermal decomposition of alpha-Pinene.
Kaivanipour, Kivan. "Non-Life Insurance Pricing Using the Generalized Additive Model, Smoothing Splines and L-Curves." Thesis, KTH, Matematik (Avd.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168389.
Full textNästan alla tariffanalyser inom sakförsäkring inkluderar kontinuerliga premieargument, såsom försäkringstagarens ålder eller vikten på det försäkrade fordonet. I den generaliserade linjära modellen så grupperas kontinuerliga premiearguments möjliga värden i intervaller och alla värden inom ett intervall behandlas som identiska. Genom att använda den generaliserade additativa modellen så slipper man arbetet med att dela in kontinuerliga premiearguments möjliga värden i intervaller. Detta examensarbete kommer att behandla olika metoder för att uppskatta den optimala smoothing-parametern inom den generaliserade additativa modellen. Metoden för korsvalidering används vanligen för detta ändamål. L-kurve-metoden, som är en mer ovanlig metod, undersöks för dess prestanda i jämförelse med metoden för korsvalidering. Numeriska beräkningar på testdata visar att L-kurve-metoden är betydligt snabbare än metoden för korsvalidering, men att den underutjämnar och därför inte är en lämplig metod för att uppskatta den optimala smoothing-parametern.
Pya, Natalya. "Additive models with shape constraints." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527433.
Full textBjörkwall, Susanna. "Stochastic claims reserving in non-life insurance : Bootstrap and smoothing models." Doctoral thesis, Stockholms universitet, Matematiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-55347.
Full textHanh, Nguyen T. "Lasso for Autoregressive and Moving Average Coeffients via Residuals of Unobservable Time Series." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo154471227291601.
Full textWang, Xiaohui. "Bayesian classification and survival analysis with curve predictors." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1205.
Full textMetwalli, Nader. "High angular resolution diffusion-weighted magnetic resonance imaging: adaptive smoothing and applications." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34854.
Full textRyu, Duchwan. "Regression analysis with longitudinal measurements." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2398.
Full textSong, Song. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16341.
Full textIn many applications it is necessary to know the stochastic fluctuation of the maximal deviations of the nonparametric quantile estimates, e.g. for various parametric models check. Uniform confidence bands are therefore constructed for nonparametric quantile estimates of regression functions. The first method is based on the strong approximations of the empirical process and extreme value theory. The strong uniform consistency rate is also established under general conditions. The second method is based on the bootstrap resampling method. It is proved that the bootstrap approximation provides a substantial improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. A labor market analysis is provided to illustrate the method. High dimensional time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science, e.g. macroeconomics, meteorology, medicine and financial engineering. One of the common approach is to separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via dynamic factor analysis. We propose a two-step estimation procedure. At the first step, we detrend the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under the dependent scenario. At the second step, we obtain the detrended low dimensional stochastic process (stationary).
Létourneau, Étienne. "Impact of algorithm, iterations, post-smoothing, count level and tracer distribution on single-frame positrom emission tomography quantification using a generalized image space reconstruction algorithm." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110750.
Full textLa tomographie par Émission de Positons est une technique d'imagerie médicale traçant les procédures fonctionnelles qui se déroulent dans le patient. L'une des applications courantes de cet appareil consiste à performer un diagnostique subjectif à partir des images obtenues. Cependant, l'imagerie quantitative (IQ) permet de performer une analyse objective en plus de nous procurer de l'information additionnelle telle que la courbe temps-activité (CTA) ainsi que des détails visuels qui échappent à l'œil. Le but de ce travail était, en comparant plusieurs algorithmes de reconstruction tels que le ML-EM PSF, le ISRA PSF et les algorithmes qui en découlent ainsi que la rétroprojection filtrée pour une image bidimensionnelle fixe, de développer une analyse robuste sur les performances quantitatives dépendamment de la localisation des régions d'intérêt (RdI), de leur taille, du niveau de bruit dans l'image, de la distribution de l'activité et des paramètres post-lissage. En simulant des acquisitions à partir d'une coupe axiale d'un cerveau digitale sur Matlab, une comparaison quantitative appuyée de figures qualitative en guise d'outils explicatifs a été effectuée pour toutes les techniques de reconstruction à l'aide de l'Erreur Absolue Moyenne (EAM) et de la relation Biais-Variance. Les résultats obtenus démontrent que la performance de chaque algorithme dépend principalement du nombre d'événements enregistré provenant de la RdI ainsi que de la combinaison itération/post-lissage utilisée qui, lorsque choisie adéquatement, permet à la majorité des algorithmes étudiés de donner des quantités similaires dans la majorité des cas. Parmi les 10 techniques analysées, 3 se sont démarquées : ML-EM PSF, ISRA PSF en utilisant les valeurs prévues avec lissage comme facteur de pondération et RPF avec un post-lissage adéquat les principaux prétendants pour atteindre l'EMA minimale. Mots-clés: Tomographie par émission de positons, Maximum-Likelihood Expectation-Maximization, Image Space Reconstruction Algorithm, Rétroprojection Filtrée, Erreur Absolue Moyenne, Imagerie quantitative.
Thomas, Nicole. "Validation of Criteria Used to Predict Warfarin Dosing Decisions." BYU ScholarsArchive, 2004. https://scholarsarchive.byu.edu/etd/40.
Full textBetnér, Staffan. "Trends in Forest Soil Acidity : A GAM Based Approach with Application on Swedish Forest Soil Inventory Data." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352392.
Full textHolanda, Amanda Amorim. "Modelos lineares parciais aditivos generalizados com suavização por meio de P-splines." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-31052018-113859/.
Full textIn this work we present the generalized partial linear models with one continuous explanatory variable treated nonparametrically and the generalized additive partial linear models with at least two continuous explanatory variables treated in such a way. The P-splines are used to describe the relationship among the response and the continuous explanatory variables. Then, the penalized likelihood functions, penalized score functions and penalized Fisher information matrices are derived to obtain the penalized maximum likelihood estimators by the combination of the backfitting (Gauss-Seidel) algorithm and the Fisher escoring iterative method for the two types of model. In addition, we present ways to estimate the smoothing parameter as well as the effective degrees of freedom. Finally, for the purpose of illustration, the proposed models are fitted to real data sets.
Sanja, Rapajić. "Iterativni postupci sa regularizacijom za rešavanje nelinearnih komplementarnih problema." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2005. https://www.cris.uns.ac.rs/record.jsf?recordId=6022&source=NDLTD&language=en.
Full textIterative methods for nonlinear complementarity problems (NCP) are con-sidered in this doctoral dissertation. NCP problems appear in many math-ematical models from economy, engineering and optimization theory. Solv-ing NCP is very atractive in recent years. Among many numerical methods for NCP, we are interested in generalized Newton-type methods and Jaco-bian smoothing methođs. Several new methods for NCP are defined in this dissertation and their local or global convergence is proved. Theoretical results are tested on relevant numerical examples.
Hancock, Penelope. "Finite element approximation of minimum generalised cross validation bivariate thin plate smoothing splines." Phd thesis, 2002. http://hdl.handle.net/1885/148534.
Full text"Robust estimation for generalized additive models." 2010. http://library.cuhk.edu.hk/record=b5894486.
Full textThesis (M.Phil.)--Chinese University of Hong Kong, 2010.
Includes bibliographical references (leaves 46-49).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Background --- p.4
Chapter 2.1 --- Notation and Definitions --- p.4
Chapter 2.2 --- Influence Function of β --- p.5
Chapter 3 --- Methodology --- p.7
Chapter 3.1 --- Robust Estimating Equations --- p.7
Chapter 3.2 --- A General Algorithm for Robust GAM Estimation --- p.9
Chapter 4 --- Asymptotic Equivalence --- p.12
Chapter 5 --- Smoothing Parameter Selection --- p.16
Chapter 5.1 --- Robust Cross-Validation --- p.17
Chapter 5.2 --- Robust Information Criteria --- p.17
Chapter 6 --- Multiple Covariates --- p.19
Chapter 7 --- Simulation Study --- p.21
Chapter 8 --- Real Data Examples --- p.26
Chapter 8.1 --- Air Pollution Data --- p.26
Chapter 8.2 --- Bronchitis Data --- p.28
Chapter 9 --- Concluding Remarks --- p.31
Chapter A --- Auxiliary Lemmas and Proofs --- p.32
Chapter B --- Fisher Consistency Correction --- p.42
Chapter B.1 --- Poisson distribution --- p.42
Chapter B.2 --- Bernoulli distribution --- p.43
Chapter C --- Derivation of (5.2) --- p.44
Bibliography --- p.46
Lin, Tzu-Ching, and 林子靖. "A smoothing Newton method based on the generalized Fischer-Burmeister function for MCPs." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/hg76p7.
Full text國立臺灣師範大學
數學系
97
We present a smooth approximation for the generalized Fischer-Burmeister function where the 2-norm in the FB function is relaxed to a general p-norm (p > 1), and establish some favorable properties for it, for example, the Jacobian consistency. With the smoothing function, we transform the mixed complementarity problem (MCP) into solving a sequence of smooth system of equations.
Su, Shao-Zu, and 蘇少祖. "Using Kernel Smoothing Approaches Imporves the Parameter Estimation based on Generalized Partial Credit Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/75909558041762230547.
Full text國立臺中教育大學
教育測驗統計研究所
96
In this paper, a modified version of MMLE/EM is proposed. There are two modifications in the proposed algorithm. First, kernel density estimation technique is applied to estimate the distribution of ability parameter in E-step. Second, kernel density estimation technique is applied to estimate the item parameters and ability parameters with EAP in M-step. Finally, we use this methodology to estimate the ability and item parameters iteratively. This algorithm is named kernel smoothing - generalized partial credit model , KS-GPCM for short. In this paper, a simulation experiment based on the generalized partial credit model is conducted to compare the performances of PARSCALE and KS-GPCM. In the experiment, three types of distributions of ability parameters (normal, bi-mode and skewed distributions) are considered. Experimental results show as follow: (i) When distribution of ability parameter is normally distributed, RMSE of ability parameter of PARSCALE is less than KS-GPCM. (ii) When distributions of ability parameters are bimodal and skewness, RMSE of ability parameter of KS-GPCM is less than PARSCALE. (iii) When distribution of ability parameter is normally distributed, RMSE of slope and item step parameters of PARSCALE is less than KS-GPCM. (iv) When distributions of ability parameters are bimodal and skewness, RMSE of slope and item step parameters of KS-GPCM is less than PARSCALE.
Shun-Te, O., and 歐順德. "The Monte Carlo Simulation Study of The Hybrid Model of Generalized Hidden Markov Model and Kernel smoothing based Item Response Theory." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/02075678413122792655.
Full text亞洲大學
資訊工程學系碩士班
94
Item characteristic curve is the central concept of the item response theory, the accuracy of ICC estimation of IRT model that is unable to prove better or not with mathematical or statistical method or the logic rule. The main purpose of this study rely on simulation to compare the accuracy of ICC estimation of four IRT Models, i.e. three-parameter logistic IRT model, the hybrid model of GHMM and 2PL-IRT, kernel smoothing based IRT, the hybrid model of GHMM and kernel smoothing based IRT. Simulation utilized MATLAB software to develop programs and simulate the data needed. Supposing discrimination parameter, difficulty parameter and ability parameter are normal distribution, guessing parameter is uniform distribution, item numbers are 25, there are six different numbers of examinees : 100,200,500,1000,1500 and 2000. According to this study, several findings have been concluded as follows: 1. The hybrid model of GHMM and kernel smoothing based IRT is better than the other’s IRT models for the accuracy of ICC estimation. 2. No matter parameter or nonparameter IRT model with GHMM is more accurate for the accuracy of ICC estimation. 3. The size of examinees will influence the accuracy of ICC estimation, more examinees are more accurate.
Tilahun, Gelila. "Statistical Methods for Dating Collections of Historical Documents." Thesis, 2011. http://hdl.handle.net/1807/29890.
Full textXu, Ganggang. "Variable Selection and Function Estimation Using Penalized Methods." Thesis, 2011. http://hdl.handle.net/1969.1/ETD-TAMU-2011-12-10451.
Full text