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1
Lee, Stephen Man Sing. "Generalised bootstrap procedures". Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319569.
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Kan, Hasan E. "Bootstrap based signal denoising". Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://bosun.nps.edu/uhtbin/hyperion.exe/02Sep%5FKan.pdf.
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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2002.Thesis Advisor(s): Monique P. Fargues, Ralph D. Hippenstiel. "September 2002." Includes bibliographical references (p. 89-90). Also available in print.
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Ip, Wai Cheung. "Bootstrap methods in econometrics". Thesis, University of Leeds, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292325.
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NETO, ANSELMO CHAVES. "BOOTSTRAP IN TIME SERIES". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1991. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8324@1.
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O bootstrap de B. Efron, que não poderia ser imaginado sem
os computadores de hoje, pode resolver vários problemas
livre da suposição de Gaussianidade para os dados.
Este trabalho tem o objetivo de apresentar essa técnica
computacionalmente intensiva no contexto de Séries
temporais - Metodologia Box and Jenkins. Como se sabe essa
Metodologia possui alguns resultados assintóticos. Então,
na fase da identificação da estrutura do modelo, pode
apresentar problemas em regiões do espaço paramétrico aqui
determinadas,. O bootstrap é proposto como opção e um
estudo de simulação, comparativo, é apresentado. Constrói-
se a distribuição bootstrap da autocorrelação e
autocorrelação parcial, amostrais, e ainda a distribuição
bootstrap do estimador de MQNL dos coeficientes de modelos
ARMA (p, q). consequentemente, fica disponível medida não-
paramétrica da precisão da estimativa. O estudo de
simulação que aborda o estimador de MQNL dos coeficientes
enfoca, basicamente, a região de fronteira da
estacionariedade e inversibilidade.The bootstrap of B. Efron, what should not be imagined without fast andcheaper computation, can solve several problems free from assumption that the data conform to a bell-shaped curve. This work has the aim to present this computer-intensive technics in the context of Time Series - Box and Jenkins´s Methodology. As we know this methodology own some asymptotic results. Then in the identification stage of the structure of the model it may present some troubles on regions of the parametric space, as we show later on the bootstrap is proposed as an aption and a comparative simulation study is pointed out. We build up the bootstrap distribution of the sample autocorrelation and sample partial autocorrelation, and yet a bootstrap distribution to the non-linear LS estimator of the coefficients to the ARMA (p,q) model. As a consequence we get the non- parametric measure of the accuracy of the estimates. The study of simulation wich takes into account the non-linear LS estimato to the coefficients, actually focalize the borden of the stationarity and invertibility region.
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Ramli, Ahmad Lutfi Amri Bin. "Bootstrap based surface reconstruction". Thesis, Durham University, 2012. http://etheses.dur.ac.uk/4468/.
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Surface reconstruction is one of the main research areas in computer graphics. The goal is to find the best surface representation of the boundary of a real object. The typical input of a surface reconstruction algorithm is a point cloud, possibly obtained by a laser 3D scanner. The raw data from the scanner is usually noisy and contains outliers. Apart from creating models of high visual quality, assuring that a model is as faithful as possible to the original object is also one of the main aims of surface reconstruction. Most surface reconstruction algorithms proposed in the literature assess the reconstructed models either by visual inspection or, in cases where subjective manual input is not possible, by measuring the training error of the model. However, the training error underestimates systematically the test error and encourages overfitting. In this thesis, we provide a method for quantitative assessment in surface reconstruction. We integrate a model averaging method from statistics called bootstrap and define it into our context. Bootstrapping is a resampling procedure that provides statistical parameter. In surface fitting, we obtained error estimate which detect error caused by noise or bad fitting. We also define bootstrap method in context of normal estimation. We obtain variance and error estimates which we use as a quality measure of normal estimates. As application, we provide smoothing algorithm for point clouds and normal smoothing that can handle feature area. We also developed feature detection algorithm.
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Kan, Hasan Ertam. "Bootstrap based signal denoising". Thesis, Monterey, California. Naval Postgraduate School, 2002. http://hdl.handle.net/10945/2883.
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Approved for public release, distribution is unlimited"This work accomplishes signal denoising using the Bootstrap method when the additive noise is Gaussian. The noisy signal is separated into frequency bands using the Fourier or Wavelet transform. Each frequency band is tested for Gaussianity by evaluating the kurtosis. The Bootstrap method is used to increase the reliability of the kurtosis estimate. Noise effects are minimized using a hard or soft thresholding scheme on the frequency bands that were estimated to be Gaussian. The recovered signal is obtained by applying the appropriate inverse transform to the modified frequency bands. The denoising scheme is tested using three test signals. Results show that FFT-based denoising schemes perform better than WT-based denoising schemes on the stationary sinusoidal signals, whereas WT-based schemes outperform FFT-based schemes on chirp type signals. Results also show that hard thresholding never outperforms soft thresholding, at best its performance is similar to soft thresholding."--p.i.
First Lieutenant, Turkish Army
7
Rifai, Khaled. "Bootstrap-Instrumente für Unternehmensgründungen". Lohmar Eul, 2009. http://d-nb.info/995568170/04.
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Castedo, Echeverri Alejandro. "CFTs and the Bootstrap". Doctoral thesis, SISSA, 2016. http://hdl.handle.net/20.500.11767/3581.
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This thesis deals with investigations in the field of higher dimensional CFTs. The first
part is focused on the technology neccessary for the calculation of general conformal
blocks in 4D CFTs. These special functions are neccessary for general boostrap analysis
in 4D CFTs. We show how to reduce the calculation of arbitrary conformal blocks
to the calculation of a minimal set of "seed" conformal blocks through the use of
differential operators. We explicitly write the set of operators necessary and show a
general basis for the case of external traceless symmetric operators. We then compute
in closed analytical form this set of seeds. We write in a compact form the set of
quadratic Casimir equations and proceed to solve them in closed form with the use of
an educated Ansatz. Various details on the form of the ansatz are deduced with the use
of the so called shadow formalism. The second part of this thesis deals with numerical
investigations of the bootstrap equation for external scalar operators. We compute
bounds on the OPE coefficients in 4D CFTs for theories with and without global
symmetries, and write the bootstrap equations for theories with SO(N ) × SO(M ) and
SU (N ) × SO(M ) symmetries. The last part of the thesis presents the Multipoint
bootstrap, a conformal-bootstrap method advocated in ref. [25]. In contrast to the
most used method based on derivatives evaluated at the symmetric point z = z = 1/2,
̄
we can consistently “integrate out" higher-dimensional operators and get a reduced,
simpler, and faster to solve, set of bootstrap equations. We test this “effective"
bootstrap by studying the 3D Ising and O(n) vector models and bounds on generic
4D CFTs, for which extensive results are already available in the literature. We also
determine the scaling dimensions of certain scalar operators in the O(n) vector models,
with n = 2, 3, 4, which have not yet been computed using bootstrap techniques.
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Przykucki, Michał Jan. "Extremal and probabilistic bootstrap percolation". Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/245349.
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In this dissertation we consider several extremal and probabilistic problems in bootstrap percolation on various families of graphs, including grids, hypercubes and trees. Bootstrap percolation is one of the simplest cellular automata. The most widely studied model is the so-called r-neighbour bootstrap percolation, in which we consider the spread of infection on a graph G according to the following deterministic rule: infected vertices of G remain infected forever and in successive rounds healthy vertices with at least r already infected neighbours become infected. Percolation is said to occur if eventually every vertex is infected. In Chapter 1 we consider a particular extremal problem in 2-neighbour bootstrap percolation on the n \times n square grid. We show that the maximum time an infection process started from an initially infected set of size n can take to infect the entire vertex set is equal to the integer nearest to (5n^2-2n)/8. In Chapter 2 we relax the condition on the size of the initially infected sets and show that the maximum time for sets of arbitrary size is 13n^2/18+O(n). In Chapter 3 we consider a similar problem, namely the maximum percolation time for 2-neighbour bootstrap percolation on the hypercube. We give an exact answer to this question showing that this time is \lfloor n^2/3 \rfloor. In Chapter 4 we consider the following probabilistic problem in bootstrap percolation: let T be an infinite tree with branching number \br(T) = b. Initially, infect every vertex of T independently with probability p > 0. Given r, define the critical probability, p_c(T,r), to be the value of p at which percolation becomes likely to occur. Answering a problem posed by Balogh, Peres and Pete, we show that if b \geq r then the value of b itself does not yield any non-trivial lower bound on p_c(T,r). In other words, for any \varepsilon > 0 there exists a tree T with branching number \br(T) = b and critical probability p_c(T,r) < \varepsilon. However, in Chapter 5 we prove that this is false if we limit ourselves to the well-studied family of Galton--Watson trees. We show that for every r \geq 2 there exists a constant c_r>0 such that if T is a Galton--Watson tree with branching number \br(T) = b \geq r then \[ p_c(T,r) > \frac{c_r}{b} e^{-\frac{b}{r-1}}. \] We also show that this bound is sharp up to a factor of O(b) by describing an explicit family of Galton--Watson trees with critical probability bounded from above by C_r e^{-\frac{b}{r-1}} for some constant C_r>0.
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Richard, Patrick. "Sieve bootstrap unit root tests". Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103285.
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We consider the use of a sieve bootstrap based on moving average (MA) and autoregressive moving average (ARMA) approximations to test the unit root hypothesis when the true Data Generating Process (DGP) is a general linear process. We provide invariance principles for these bootstrap DGPs and we prove that the resulting ADF tests are asymptotically valid. Our simulations indicate that these tests sometimes outperform those based on the usual autoregressive (AR) sieve bootstrap. We study the reasons for the failure of the AR sieve bootstrap tests and propose some solutions, including a modified version of the fast double bootstrap.We also argue that using biased estimators to build bootstrap DGPs may result in less accurate inference. Some simulations confirm this in the case of ADF tests. We show that one can use the GLS transformation matrix to obtain equations that can be used to estimate bias in general ARMA(p,q) models. We compare the resulting bias reduced estimator to a widely used bootstrap based bias corrected estimator. Our simulations indicate that the former has better finite sample properties then the latter in the case of MA models. Finally, our simulations show that using bias corrected or bias reduced estimators to build bootstrap DGP sometimes provides accuracy gains.
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鄧國良 y Kwok-leong Tang. "Sensitivity analysis of bootstrap methods". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977479.
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Yu, Zhuqing y 俞翥清. "Uniformly consistent bootstrap confidence intervals". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B47752993.
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The bootstrap methods are widely used for constructing confidence intervals.
However, the conventional bootstrap fails to be consistent under some nonstandard
circumstances. The m out of n bootstrap is usually adopted to restore
consistency, provided that a correct convergence rate can be specified for the
plug-in estimators. In this thesis, we re-investigate the asymptotic properties of
the bootstrap in a moving-parameter framework in which the underlying distribution
is allowed to depend on n. We consider the problem of setting uniformly
consistent confidence intervals for two non-regular cases: (1) the smooth function
models with vanishing derivatives; and (2) the M-estimation with non-regular
conditions.
Under the moving-parameter setup, neither the conventional bootstrap nor
the m out of n bootstrap is shown uniformly consistent over the whole parameter space. The results reflect to some extent finite-sample anomalies that cannot be
explained by conventional, fixed-parameter, asymptotics. We propose a weighted
bootstrap procedure for constructing uniformly consistent bootstrap confidence
intervals, which does not require explicit specification of the convergence rate
of the plug-in estimator. Under the smooth function models, we also propose
a modified n out of n bootstrap procedure in special cases where the smooth
function is applied to estimators that are uniformly bootstrappable. The estimating
function bootstrap is also successfully employed for the latter model
and enjoys computational advantages over the weighted bootstrap. We illustrate
our findings by comparing the finite-sample coverage performances of the different
bootstrap procedures. The stable performance of the proposed methods,
contrasts sharply with the erratic coverages of the n out of n and m out of n
bootstrap intervals, a result in agreement with our theoretical findings.published_or_final_version
Statistics and Actuarial Science
Master
Master of Philosophy
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Liljas, Erik. "3D-bootstrap - Konfidensintervall för guldfyndigheter". Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-86723.
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This paper deals with evaluating 3D-bootstrap for the mining company New Boliden in an attempt to revise their current method of calculating average gold riches in different areas. The purpose is to find one-sided 3D-bootstrap confidence bound of the average gold riches. There lacks well-defined theory behind using 3D-bootstrap, in this paper the variogram is used as an estimate of dependencies between the observations, and the block length is chosen to be higher than this estimate. In aid of this, a simulated data material is conducted to check the validity of 3D-bootstrap in a controlled area where the theoretical value is known. The results are inconclusive, and further studies are needed.
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Smith, Paul James. "Sharp thresholds in bootstrap percolation". Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610754.
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Buzun, Nazar. "Bootstrap in high dimensional spaces". Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22285.
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Ziel dieser Arbeit ist theoretische Eigenschaften verschiedener Bootstrap Methoden zu untersuchen. Als Ergebnis führen wir die Konvergenzraten des Bootstrap-Verfahrens ein, die sich auf die Differenz zwischen der tatsächlichen Verteilung einer Statistik und der Resampling-Näherung beziehen.
In dieser Arbeit analysieren wir die Verteilung der l2-Norm der Summe unabhängiger Vektoren, des Summen Maximums in hoher Dimension, des Wasserstein-Abstands zwischen empirischen Messungen und Wassestein-Barycenters. Um die Bootstrap-Konvergenz zu beweisen, verwenden wir die Gaussche Approximations technik. Das bedeutet dass man in der betrachteten Statistik eine Summe unabhängiger Vektoren finden muss, so dass Bootstrap eine erneute Abtastung dieser Summe ergibt. Ferner kann diese Summe durch Gaussche Verteilung angenähert und mit der Neuabtastung Verteilung als Differenz zwischen Kovarianzmatrizen verglichen werden.
Im Allgemeinen scheint es sehr schwierig zu sein, eine solche Summe unabhängiger Vektoren aufzudecken, da einige Statistiken (zum Beispiel MLE) keine explizite Gleichung haben und möglicherweise unendlich dimensional sind. Um mit dieser Schwierigkeit fertig zu werden, verwenden wir einige neuartige Ergebnisse aus der statistischen Lerntheorie.
Darüber hinaus wenden wir Bootstrap bei Methoden zur Erkennung von Änderungspunkten an. Im parametrischen Fall analysieren wir den statischen Likelihood Ratio Test (LRT). Seine hohen Werte zeigen Änderungen der Parameter Verteilung in der Datensequenz an. Das Maximum von LRT hat eine unbekannte Verteilung und kann mit Bootstrap kalibriert werden. Wir zeigen die Konvergenzraten zur realen maximalen LRT-Verteilung. In nicht parametrischen Fällen verwenden wir anstelle von LRT den Wasserstein-Abstand zwischen empirischen Messungen. Wir testen die Genauigkeit von Methoden zur Erkennung von Änderungspunkten anhand von synthetischen Zeitreihen und Elektrokardiographiedaten. Letzteres zeigt einige Vorteile des nicht parametrischen Ansatzes gegenüber komplexen Modellen und LRT.The objective of this thesis is to explore theoretical properties of various bootstrap methods. We introduce the convergence rates of the bootstrap procedure which corresponds to the difference between real distribution of some statistic and its resampling approximation. In this work we analyze the distribution of Euclidean norm of independent vectors sum, maximum of sum in high dimension, Wasserstein distance between empirical measures, Wassestein barycenters. In order to prove bootstrap convergence we involve Gaussian approximation technique which means that one has to find a sum of independent vectors in the considered statistic such that bootstrap yields a resampling of this sum. Further this sum may be approximated by Gaussian distribution and compared with the resampling distribution as a difference between variance matrices. In general it appears to be very difficult to reveal such a sum of independent vectors because some statistics (for example, MLE) don't have an explicit equation and may be infinite-dimensional. In order to handle this difficulty we involve some novel results from statistical learning theory, which provide a finite sample quadratic approximation of the Likelihood and suitable MLE representation. In the last chapter we consider the MLE of Wasserstein barycenters model. The regularised barycenters model has bounded derivatives and satisfies the necessary conditions of quadratic approximation. Furthermore, we apply bootstrap in change point detection methods. In the parametric case we analyse the Likelihood Ratio Test (LRT) statistic. Its high values indicate changes of parametric distribution in the data sequence. The maximum of LRT has a complex distribution but its quantiles may be calibrated by means of bootstrap. We show the convergence rates of the bootstrap quantiles to the real quantiles of LRT distribution. In non-parametric case instead of LRT we use Wasserstein distance between empirical measures. We test the accuracy of change point detection methods on synthetic time series and electrocardiography (ECG) data. Experiments with ECG illustrate advantages of the non-parametric approach versus complex parametric models and LRT.
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Lin, Ying-Hsuan. "Conformal Bootstrap in Two Dimensions". Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493283.
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In this dissertation, we study bootstrap constraints on conformal field theories in two dimensions.
The first half concerns two-dimensional (4,4) superconformal field theories of central charge c=6, corresponding to nonlinear sigma models on K3 surfaces. The superconformal bootstrap is made possible through a surprising relation between the BPS N=4 superconformal blocks with c=6 and bosonic Virasoro conformal blocks with c=28, and an exact moduli dependence of a certain integrated BPS four-point function. Nontrivial bounds on the non-BPS spectrum in the K3 CFT are obtained as functions of the CFT moduli, that interpolate between the free orbifold points and singular CFT points. We observe directly the signature of a continuous spectrum above a gap at the singular moduli, and find numerically an upper bound on this gap that is saturated by the A1 N=4 cigar CFT.
The second half concerns the semiclassical limit of two-dimensional CFTs, motivated by holography. In this limit, the conformal block decomposition of the four-point function is dominated a particular weight, and the crossing equation simplifies drastically. We find that if a certain "weakness" condition is satisfied, then the OPE coefficients follow a universal formula given by the semiclassical limit of the fusion kernel. This is matched with a bulk action evaluated on a geometry with three conical defects, analytically continued in the deficit angles beyond the range for which a metric with positive signature exists. The analytically continued geometry has a codimension-one coordinate singularity surrounding the heaviest conical defect. This singularity becomes a horizon after Wick-rotating to Lorentzian signature, suggesting a connection between universality and the existence of a horizon.Physics
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Tang, Kwok-leong. "Sensitivity analysis of bootstrap methods". [Hong Kong] : University of Hong Kong, 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13793792.
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Gandolfo, Enrico. "Metodo bootstrap e alcune applicazioni". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/2957/.
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Chaves, Inácio Felipe. "Bootstrap ponderado : uma avaliação numérica". Universidade Federal de Pernambuco, 2004. https://repositorio.ufpe.br/handle/123456789/6597.
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Previous issue date: 2004Em modelos de regressão linear em que os erros são heteroscedásticos, a prática comum é utilizar o estimador de mínimos quadrados ordinários para a estimação dos parâmetros juntamente com um estimador consistente da matriz de covariâncias dessas estimativas que, em geral, é o estimador desenvolvido por White (1980) ou uma de suas variantes. Entretanto, estimadores da matriz de covariâncias baseados em esquemas de bootstrap têm-se mostrado boas alternativas aos estimadores tradicionais. Em especial o estimador desenvolvido por Cribari?Neto & Zarkos (2004), em que a probabilidade de seleção dos resíduos é ponderada pelo inverso do grau de alavancagem, apresenta desempenho superior aos estimadores consistentes tradicionais, principalmente em situações não-balanceadas em que há observações potencialmente influentes. Utilizando simulações de Monte Carlo, foi analisada neste trabalho a sensibilidade desse estimador a diferentes formas de reamostragem através da análise do comportamento de novos estimadores que utilizam outras probabilidades de seleção dos resíduos. Adicionalmente, investigou-se a sensibilidade da inferência baseada neste e em outros estimadores a situações de não-normalidade dos erros
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Pavlíčková, Lucie. "Metoda bootstrap a její aplikace". Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228541.
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The diploma thesis describes the bootstrap method and its applications in the estimate accuracy statement, in the confidence intervals generation and in the testing of statistical hypotheses. Further the method of the discrete probability estimation of the categorical quantity is presented, making use the gradient of the quasi-norm hereof distribution. On concrete examples the bootstrap method is applied in the confidence intervals forming of the categorical quantity probability function. The diploma thesis was supported by the project of MŠMT of the Czech Republic no. 1M06047 "Centre for Quality and Reliability of Production", by the grant of Grant Agency of the Czech Republic (Czech Science Foundation) reg. no. 103/08/1658 "Advanced optimum design of composed concrete structures" and by the research plan of MŠMT of the Czech Republic no. MSM0021630519 "Progressive reliable and durable structures".
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Zhang, Guangjian. "Bootstrap procedures for dynamic factor analysis". Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1153782819.
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Chauvet, Guillaume. "Méthodes de Bootstrap en population finie". Phd thesis, Rennes 2, 2007. http://tel.archives-ouvertes.fr/tel-00267689.
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Cette thèse est consacrée aux méthodes de Bootstrap pour unepopulation ?nie. Le premier chapitre introduit quelques rappels sur l'échantillonnage et propose une présentation synthétique des principales méthodes d'estimation de précision. Le chapitre 2 rappelle les méthodes de Bootstrap proposées pour un sondage aléatoire simple et introduit deux nouvelles mé thodes. Le chapitre 3 donne un nouvel algorithme de Bootstrap, consistant pour l'estimation de variance d'un estimateur par substitution dans le cas d'un tirage à forte entropie. Dans le chapitre 4, nous introduisons la notion d'échantillonnage équilibré et proposons un algorithme rapide. Nous montrons que l'algorithme de Bootstrap proposé est également consistant pour l'estimation de variance d'un tirage équilibré à entropie maximale. Le cas d'un échantillonnage complexe et celui d'un redressement est traité au chapitre 5. Une application au Nouveau Recensement de la population est donnée dans le chapitre 6.
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Xu, Liqun. "Bootstrap for dual scaling of rankings". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0001/NQ35373.pdf.
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Wang, Qun. "Bootstrap techniques for statistical pattern recognition". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0027/MQ52407.pdf.
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Gredenhoff, Mikael. "Bootstrap inference in time series econometrics". Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1998. http://www.hhs.se/efi/summary/476.htm.
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Allison, James Samuel. "Bootstrap-based hypothesis testing / J.S. Allison". Thesis, North-West University, 2008. http://hdl.handle.net/10394/3701.
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One of the main objectives of this dissertation is the development of a new method of evaluating the performance of bootstrap-based tests. The evaluation method that is currently in use in the literature has some major shortcomings, for example, it does not allow one to determine the robustness of a bootstrap estimator of a critical value. This is because the evaluation and the estimation are based on the same data. This traditional method of evaluation often leads to too optimistic probability of type I errors when bootstrap critical values are used.
We show how this new, more robust, method can detect defects of bootstrap estimated critical values which cannot be observed if one uses the current evaluation method. Based on the new evaluation method, some theoretical properties regarding the bootstrap critical value are derived when testing for the mean in a univariate population. These theoretical findings again highlight the importance of the two guidelines proposed by Hall and Wilson (1991) for bootstrap-based testing, namely that resampling must be done in a way that reflects the null hypothesis and bootstrap tests should be based on test statistics that are pivotal (or asymptotically pivotal).
We also developed a new nonparametric bootstrap test for Spearman's rho and, based on the results obtained from a Monte-Carlo study, we recommend that this new test should be used when testing for Spearman's rho. A semiparametric test based on copulas was also developed as a useful benchmark tool for measuring the performance of the nonparametric test.
Other research objectives of this dissertation include, among others, a brief overview of the nonparametric bootstrap and a general formulation of methods which can be used to apply the bootstrap correctly when conducting hypothesis testing.Thesis (Ph.D. (Statistics))--North-West University, Potchefstroom Campus, 2009.
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Canepa, Alessandra. "Bootstrap inference in cointegrated VAR models". Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268384.
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Meinke, Alexander. "Applications of the Extremal Functional Bootstrap". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-26112018-120129/.
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The study of conformal symmetry is motivated through an example in statistical mechanics and then rigorously developed in quantum field theories in general spatial dimensions. In particular, primary fields are introduced as the fundamental objects of such theories and then studied in the formalism of radial quantization. The implications of conformal invariance on the functional form of correlation functions are studied in detail. Conformal blocks are defined and various approaches to their analytical and numerical calculation are presented with a special emphasis on the one-dimensional case. Building on these preliminaries, a modern formulation of the conformal bootstrap program and its various extensions are discussed. Examples are given in which bounds on the scaling dimensions in a one-dimensional theory are derived numerically. Using these results I motivate the technique of using the extremal functional bootstrap which I then develop in more detail. Many technical details are discussed and examples shown. After a brief discussion of conformal field theories with a boundary I apply numerical methods to find constraints on the spectrum of the 3D Ising model. Another application is presented in which I study the 4-point function on the boundary of a particular theory in Anti-de-Sitter space in order to approximate the mass spectrum of the theory.O estudo da simetria conforme é motivado através de um exemplo em mecânica estatística e em seguida rigorosamente desenvolvido em teorias de campos quânticos em dimensões espaciais gerais. Em particular, os campos primários são introduzidos como os objetos fundamentais de tais teorias e então estudados através do formalismo de quantização radial. As implicações da invariância conforme na forma funcional das funções de correlação são estudadas em detalhe. Blocos conformes são definidos e várias abordagens para seu cálculo analítico e numérico são apresentadas com uma ênfase especial no caso unidimensional. Com base nessas preliminares, uma formulação moderna do programa de bootstrap conforme e suas várias extensões são discutidas. Exemplos são dados em que limites nas dimensões de escala em uma teoria unidimensional são derivados numericamente. Usando esses resultados, motivei a técnica de usar o bootstrap funcional extremo, que depois desenvolvo em mais detalhes. Diversos detalhes técnicos são discutidos e exemplos são apresentados. Após uma breve discussão das teorias de campo conformes com fronteiras, eu aplico métodos numéricos para encontrar restrições no espectro do modelo de Ising em 3D. Outra aplicação é apresentada em que eu estudo a função de 4 pontos na fronteira de uma teoria particular no espaço Anti-de-Sitter, a fim de aproximar o espectro de massa da teoria.
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Dalposso, Gustavo Henrique. "Método Bootstrap na agricultura de precisão". Universidade Estadual do Oeste do Paraná, 2017. http://tede.unioeste.br/handle/tede/3075.
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Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Estado do Paraná (FA)
One issue in precision agriculture studies concerns about the statistical methods applied in inferential analysis, since they have required assumptions that, sometimes, cannot be assumed. A possibility to traditional methods is to use the bootstrap method, which consists in resampling and replacing the original data set to carry out inferences. The bootstrap methodology can be applied to independent sample data as well as in cases of dependence, such as in spatial statistics. However, adjustments are required during the resampling process in order to use the bootstrap method in spatial data. Thus, this trial aimed at applying the bootstrap method in precision agriculture studies, whose result was the preparation of three scientific papers. Soybean yield and soil attributes datasets formed with few samples were used in the first paper to determine a multiple linear regression model. Bootstrap methods were chosen to select variables, identify influential points and determine confidence intervals of the model parameters. The results showed that the bootstrap methods allowed selecting significant attributes to design a model, to build confidence intervals of the studied parameters and finally to indentify the influential points on the estimated parameters. Besides, spatial dependence of soybean yield data and soil attributes were studied in the second paper by bootstrap method in geostatistical analysis. The spatial bootstrap method was used to quantify the uncertainties associated with the spatial dependence structure, the fitted model parameter estimators, kriging predicted values and multivariate normality assumption of data. Thus, it was possible to quantify the uncertainties in all phases of geostatistical analysis. A spatial linear model was used to analyze soybean yield considering the soil attributes in the third paper. Spatial bootstrap methods were used to determine point and interval estimators associated with the studied model parameters. Hypothesis tests were carried out on the model parameters and probability plots were developed to identify data normality. These methods allowed to quantify the uncertainties associated to the structure of spatial dependence, as well as to evaluate the individual significance of the parameters associated with the average of the spatial linear model and to verify data multivariate normality assumption. Finally, it is concluded that bootstrap method is an effective alternative to make statistical inferences in precision agriculture studies.
Um problema que ocorre nos estudos vinculados à agricultura de precisão diz respeito aos métodos estatísticos utilizados nas análises inferenciais, pois eles requerem pressupostos que muitas vezes não podem ser assumidos. Uma alternativa aos métodos tradicionais é a utilização do método bootstrap, que utiliza reamostragens com reposição do conjunto de dados originais para realizar inferências. A metodologia bootstrap pode ser aplicada a dados amostrais independentes e também em casos de dependência, como na estatística espacial. No entanto, para se utilizar o método bootstrap em dados espaciais, são necessárias adaptações no processo de reamostragem. Este trabalho teve como objetivo utilizar o método bootstrap em estudos vinculados à agricultura de precisão, cujo resultado é a elaboração de três artigos. No primeiro artigo utilizou-se um conjunto de dados de produtividade de soja e atributos do solo formado com poucas amostras para determinar um modelo de regressão linear múltipla. Foram utilizados métodos bootstrap para a seleção de variáveis, identificação de pontos influentes e determinação de intervalos de confiança dos parâmetros do modelo. Os resultados mostraram que os métodos bootstrap permitiram selecionar os atributos que foram significativos na construção do modelo, construir os intervalos de confiança dos parâmetros e identificar os pontos que tiveram grande influência sobre os parâmetros estimados. No segundo artigo estudou-se a dependência espacial de dados de produtividade de soja e atributos do solo utilizando o método bootstrap na análise geoestatística. Utilizou-se o método bootstrap espacial para quantificar as incertezas associadas à caracterização das estruturas de dependência espacial, aos estimadores dos parâmetros dos modelos ajustados, aos valores preditos por krigagem e ao pressuposto de normalidade multivariada dos dados. Os resultados obtidos possibilitaram quantificar as incertezas em todas as fases da análise geoestatística. No terceiro artigo utilizou-se uma regressão espacial linear para modelar a produtividade de soja em função de atributos do solo. Foram utilizados métodos bootstrap espaciais para determinar estimadores pontuais e por intervalo associados aos parâmetros do modelo. Realizaram-se testes de hipóteses sobre os parâmetros do modelo e foram eleborados gráficos de probabilidade para identificar a normalidade dos dados. Os métodos permitiram quantificar as incertezas associadas à estrutura de dependência espacial, avaliar a significância individual dos parâmetros associados à média do modelo espacial linear e verificar a suposição de normalidade multivariada dos dados. Conclui-se, portanto, que o método bootstrap é uma eficaz alternativa para realizar inferências em estudos vinculados à agricultura de precisão.
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Meltzer, David H. "Topics in the Analytic Conformal Bootstrap". Thesis, Yale University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10957332.
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In this thesis, we explore analytical methods to study conformal field theories (CFTs) in a general number of spacetime dimensions. We first use the lightcone bootstrap to systematically study correlation functions of scalar operators charged under global symmetries. We then generalize existing techniques in the lightcone bootstrap to study four-point functions containing operators with spin. As an application, we observe a close connection between anomalous dimensions of large spin, double-twist operators and the conformal collider bounds. Through further refinement of these techniques and the application of known analyticity properties for four-point functions, we also present a proof for these bounds that relies on basic physical consistency conditions. We then generalize these techniques further to study large N CFTs in the Regge limit and the implications of crossing symmetry in this limit. By studying the Regge limit, we can make new predictions for the large twist, large spin spectrum of CFTs and derive new bounds on CFT data. In the final part of this thesis, we use the Regge limit and constraints from unitarity to derive new bounds for both large N and generic CFTs. For large N CFTs, we derive new constraints on theories dual to a weakly-coupled, gravitational theory in an Anti-deSitter (AdS) spacetime, and for generic CFTs we derive generalizations of the conformal collider bounds.
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Lacinová, Veronika. "Odhady diskrétního rozložení pravděpodobnosti a bootstrap". Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-234260.
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Doctoral thesis is focused on the unconventional methods of the discrete probability estimation of categorical quantity from its observed values. The gradient of quasinorm and so-called line estimation were emlopyed for these estimations. Bootstrap method was used for the improvement of accuracy. Theoretical results for selected quasinorms were illustrated on specific examples.
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Kusuki, Yuya. "Analytic Conformal Bootstrap in 2D CFT". Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263457.
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Kecojevic, Tatjana. "Bootstrap inference for parametric quantile regression". Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/bootstrap-inference-for-parametric-quantile-regression(194021d5-e03f-4f48-bfb8-5156819f5900).html.
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The motivation for this thesis came from the provision of a large data set from Saudi Arabia giving anthropometric measurements of children and adolescents from birth to eighteen years of age, with a requirement to construct growth charts. The construction of these growth charts revealed a number of issues particularly in the respect to statistical inference relating to quantile regression. To investigate a range of different statistical inference procedures in parametric quantile regression in particular the estimation of the confidence limits of the ?th (?? [0, 1]) quantile, a number of sets of simulated data in which various error structures are imposed including homoscedastic and heteroscedastic structures were developed. Methods from the statistical literature were then compared with a method proposed within this thesis based on the idea of Silverman's (1986) kernel smoothing. This proposed bootstrapping method requires the estimation of the conditional variance function of the fitted quantile. The performance of a variety of variance estimation methods combined within the proposed bootstrapping procedure are assessed under various data structures in order to examine the performance of the proposed bootstrapping approach. The validity of the proposed bootstrapping method is then illustrated using the Saudi Arabian anthropometric data.
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Yam, Chiu Yu. "Quasi-Monte Carlo methods for bootstrap". HKBU Institutional Repository, 2000. http://repository.hkbu.edu.hk/etd_ra/272.
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Gonçalves, Sílvia. "The bootstrap for dependent heterogeneous processes /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2000. http://wwwlib.umi.com/cr/ucsd/fullcit?p9987533.
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Chauvet, Guillaume Carbon Michel. "Méthodes de Bootstrap en population finie". Rennes : Université Rennes 2, 2008. http://tel.archives-ouvertes.fr/tel-00267689/fr.
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Barbosa, Eduardo Campana. "Inferência via Bootstrap na Conjoint Analysis". Universidade Federal de Viçosa, 2017. http://www.locus.ufv.br/handle/123456789/17847.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
A presente tese teve como objetivo introduzir o método de reamostragem com reposição ou Bootstrap na Conjoint Analysis. Apresenta-se no texto uma revisão conceitual (Revisão de Literatura) sobre a referida metodologia (Conjoint Analysis) e também sobre o método proposto (Bootstrap). Adicionalmente, no Capítulo I e II, define-se a parte teórica e metodológica da Conjoint Analysis e do método Bootstrap, ilustrando o funcionamento conjunto dessas abordagens via aplicação real, com dados da área de tecnologia de alimentos. Inferências adicionais que até então não eram fornecidas no contexto clássico ou frequentista podem agora ser obtidas via análise das distribuições empíricas dos estimadores das Importâncias Relativas (abordagem por notas) e das Probabilidades e Razão de Escolhas (abordagem por escolhas). De forma geral, os resultados demonstraram que o método Bootstrap forneceu estimativas pontuais mais precisas e tornou ambas as abordagens da Conjoint Analysis mais informativas, uma vez que medidas de erro padrão e, principalmente, intervalos de confiança puderam ser facilmente obtidos para certas quantidades de interesse, possibilitando a realização de testes ou comparações estatísticas sobre as mesmas.
The aim of this thesis was introduce the Booststrap resampling method in Conjoint Analysis. We present in the text a conceptual review (Literature Review) about this methodology (Conjoint Analysis) and also about the proposed method (Bootstrap). In addition, in Chapter I and II, the theoretical and methodological aspects of Conjoint Analysis and the Bootstrap method are defined, illustrating the joint operation of these approaches via real application, with data from the food technology area.. Additional inferences have not been provided in the classic or frequentist context can now be obtained by analyzing the empirical distributions of Relative Importance (ratings based approach) and Probability and Choice Ratio (choice based approach) estimators. Overall, the results demonstrated that the Bootstrap method provided more accurate point estimates and made both Conjoint Analysis approaches more informative, since standard error measures, and mainly confidence intervals, could be easily obtained for certain quantities of interest, making it possible to perform statistical tests or comparisons on them.
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Zhilova, Mayya. "Bootstrap confidence sets under model misspecification". Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2015. http://dx.doi.org/10.18452/17380.
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Diese Arbeit befasst sich mit einem Multiplier-Bootstrap Verfahren für die Konstruktion von Likelihood-basierten Konfidenzbereichen in zwei verschiedenen Fällen. Im ersten Fall betrachten wir das Verfahren für ein einzelnes parametrisches Modell und im zweiten Fall erweitern wir die Methode, um Konfidenzbereiche für eine ganze Familie von parametrischen Modellen simultan zu schätzen. Theoretische Resultate zeigen die Validität der Bootstrap-Prozedur für eine potenziell begrenzte Anzahl an Beobachtungen, eine große Anzahl an betrachteten parametrischen Modellen, wachsende Parameterdimensionen und eine mögliche Misspezifizierung der parametrischen Annahmen. Im Falle eines einzelnen parametrischen Modells funktioniert die Bootstrap-Approximation, wenn die dritte Potenz der Parameterdimension ist kleiner als die Anzahl an Beobachtungen. Das Hauptresultat über die Validität des Bootstrap gilt unter der sogenannten Small-Modeling-Bias Bedingung auch im Falle, dass das parametrische Modell misspezifiert ist. Wenn das wahre Modell signifikant von der betrachteten parametrischen Familie abweicht, ist das Bootstrap Verfahren weiterhin anwendbar, aber es führt zu etwas konservativeren Schätzungen: die Konfidenzbereiche werden durch den Modellfehler vergrößert. Für die Konstruktion von simultanen Konfidenzbereichen entwickeln wir ein Multiplier-Bootstrap Verfahren um die Quantile der gemeinsamen Verteilung der Likelihood-Quotienten zu schätzen und eine Multiplizitätskorrektur der Konfidenzlevels vorzunehmen. Theoretische Ergebnisse zeigen die Validität des Verfahrens; die resultierende Approximationsfehler hängt von der Anzahl an betrachteten parametrischen Modellen logarithmisch. Hier betrachten wir auch wieder den Fall, dass die parametrischen Modelle misspezifiziert sind. Wenn die Misspezifikation signifikant ist, werden Bootstrap-generierten kritischen Werte größer als die wahren Werte sein und die Bootstrap-Konfidenzmengen sind konservativ.The thesis studies a multiplier bootstrap procedure for construction of likelihood-based confidence sets in two cases. The first one focuses on a single parametric model, while the second case extends the construction to simultaneous confidence estimation for a collection of parametric models. Theoretical results justify the validity of the bootstrap procedure for a limited sample size, a large number of considered parametric models, growing parameters’ dimensions, and possible misspecification of the parametric assumptions. In the case of one parametric model the bootstrap approximation works if the cube of the parametric dimension is smaller than the sample size. The main result about bootstrap validity continues to apply even if the underlying parametric model is misspecified under a so-called small modelling bias condition. If the true model deviates significantly from the considered parametric family, the bootstrap procedure is still applicable but it becomes conservative: the size of the constructed confidence sets is increased by the modelling bias. For the problem of construction of simultaneous confidence sets we suggest a multiplier bootstrap procedure for estimating a joint distribution of the likelihood ratio statistics, and for adjustment of the confidence level for multiplicity. Theoretical results state the bootstrap validity; a number of parametric models enters a resulting approximation error logarithmically. Here we also consider the case when parametric models are misspecified. If the misspecification is significant, then the bootstrap critical values exceed the true ones and the bootstrap confidence set becomes conservative. The theoretical approach includes non-asymptotic square-root Wilks theorem, Gaussian approximation of Euclidean norm of a sum of independent vectors, comparison and anti-concentration bounds for Euclidean norm of Gaussian vectors. Numerical experiments for misspecified regression models nicely confirm our theoretical results.
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PORQUEDDU, MARIO. "Bootstrap methods for dynamic factor models". Doctoral thesis, Università Bocconi, 2009. https://hdl.handle.net/11565/4053467.
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Bergström, Gustav. "The Use of Importance Sampling in Bootstrap Simulations and in Moving Block Bootstrap Simulations for Efficient VaR Estimations". Thesis, Umeå universitet, Institutionen för fysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-66587.
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Sivasothinathan, Ramanan. "A bootstrap approach for constructing superconvergent interpolants". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ34042.pdf.
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Reesor, R. Mark. "Relative entropy, distortion, the bootstrap and risk". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ65258.pdf.
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Praestgaard, Jens Thomas. "General-weights bootstrap of the empirical process /". Thesis, Connect to this title online; UW restricted, 1991. http://hdl.handle.net/1773/8966.
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Kent, A. "Infinite dimensional algebras and the conformal bootstrap". Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383184.
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馮子豪 y Tze-ho Fung. "Bootstrap estimation of variance in survey sampling". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31208198.
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Chan, Tsz-hin y 陳子軒. "Hybrid bootstrap procedures for shrinkage-type estimators". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48521826.
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In statistical inference, one is often interested in estimating the distribution of a root, which is a function of the data and the parameters only. Knowledge of the distribution of a root is useful for inference problems such as hypothesis testing and the construction of a confidence set. Shrinkage-type estimators have become popular in statistical inference due to their smaller mean squared errors. In this thesis, the performance of different bootstrap methods is investigated for estimating the distributions of roots which are constructed based on shrinkage estimators. Focus is on two shrinkage estimation problems, namely the James-Stein estimation and the model selection problem in simple linear regression. A hybrid bootstrap procedure and a bootstrap test method are proposed to estimate the distributions of the roots of interest. In the two shrinkage problems, the asymptotic errors of the traditional n-out-of-n bootstrap, m-out-of-n bootstrap and the proposed methods are derived under a moving parameter framework. The problem of the lack of uniform consistency of the n-out-of-n and the m-out-of-n bootstraps is exposed. It is shown that the proposed methods have better overall performance, in the sense that they yield improved convergence rates over almost the whole range of possible values of the underlying parameters. Simulation studies are carried out to illustrate the theoretical findings.published_or_final_version
Statistics and Actuarial Science
Master
Master of Philosophy
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Buzun, Nazar [Verfasser]. "Bootstrap in high dimensional spaces / Nazar Buzun". Berlin : Humboldt-Universität zu Berlin, 2021. http://d-nb.info/1226153208/34.
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Coker, Thomas David. "Graph colouring and bootstrap percolation with recovery". Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610806.
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LIMA, Fábio Pereira. "Inferência bootstrap em modelos de regressão beta". Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/24578.
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CAPES
O método bootstrap, introduzido por Efron (1979), tornou-se uma importante ferramenta estatística para contornar problemas inferenciais em pequenas amostras ou quando a teoria assintótica é intratável, podendo ser utilizado, por exemplo, na construção de intervalos de confiança, para realizar correção de viés de estimadores e para realizar testes de hipóteses. Quando se trata da classe de modelos de regressão beta proposta em Ferrari e Cribari- Neto (2004), utilizada na modelagem de variáveis contínuas no intervalo (0,1), o método tem um importante papel na construção de intervalos de predição e na realização de testes de hipóteses. A presente tese tem como objetivo abordar os principais métodos bootstrap utilizados para realizar inferências sobre os parâmetros dessa classe de modelos, avaliando os desempenhos das principais variantes de tal método. Para tanto, inicialmente são expostas adaptações do método bootstrap tendo como objetivo a realização de testes de hipóteses encaixadas e não encaixadas. Nesse cenário, propomos uma versão bootstrap duplo rápido para os testes com o objetivo de obter maior precisão nos resultados sem alto custo computacional. Adicionalmente, um estudo sobre a construção de intervalos de predição em modelos de regressão beta é realizado. Levando em conta os métodos percentil e BCa adaptados em Espinheira et al. (2014), propomos uma adaptação do método t-bootstrap e as versões bootstrap duplo do mesmo e do método percentil. O desempenho de cada método é então avaliado na busca de determinar a melhor abordagem para cada situação.
Introduced by Efron (1979), the bootstrap became an important statistical tool, being used to overcome inference problems on small samples or when the asymptotic theory is intractable. The method can be used, for example, for constructing conhdence intervals, for performing bias correction of estimators and for carrying out hypothesis testing inference. In the beta regression model, proposed by Ferrari and Cribari-Neto (2004) which is used to model continuous variables in (0,1), the bootstrap method plays an important role in the construction of prediction intervals and hypothesis testing. This thesis deals with the use of bootstrap methods for perfoming statistical inference in beta regression models. We present adaptations of the bootstrap method for perfoming nested and nonnested hypothesis testing inference. Next, we propose fast double bootstrap variants of the tests in order to achieve more accurate inferences without the high computational cost required by the Standard double bootstrap. Additionally, a study of prediction intervals in the class of beta regression models is performed. We introduce f-bootstrap prediction interval and the double bootstrap versions of the percentil and f-bootstrap prediction intervals. The performance of each method is then evaluated in the quest to determine the best approach for each situation.
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Mendes, José Manuel Zorro. ""Bootstrap" iterativo: aplicação ao índice de Gini". Master's thesis, ISEG, 1990. http://hdl.handle.net/10400.5/21910.
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