Dissertations / Theses on the topic 'Hypothesis sampling'
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Koziuk, Andzhey. "Re-sampling in instrumental variables regression." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/20869.
Full textInstrumental variables regression in the context of a re-sampling is considered. In the work a framework is built to identify an inferred target function. It attempts to approach an idea of a non-parametric regression and motivate instrumental variables regression from a new perspective. The framework assumes a target of estimation to be formed by two factors - an environment and an internal, model specific structure. Aside from the framework, the work develops a re-sampling method suited to test linear hypothesis on the target. Particular technical environment and procedure are given and explained in the introduction and in the body of the work. Specifically, following the work of Spokoiny, Zhilova 2015, the writing justifies and applies numerically 'multiplier bootstrap' procedure to construct confidence intervals for the testing problem. The procedure and underlying statistical toolbox were chosen to account for an issue appearing in the model and overlooked by asymptotic analysis, that is weakness of instrumental variables. The issue, however, is addressed by design of the finite sample approach by Spokoiny 2014.
Tucker, Joanne M. (Joanne Morris). "Robustness of the One-Sample Kolmogorov Test to Sampling from a Finite Discrete Population." Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc278186/.
Full textMichel, Frank [Verfasser], Carsten [Akademischer Betreuer] Rother, Stefan [Akademischer Betreuer] Gumhold, Carsten [Gutachter] Rother, and Carsten [Gutachter] Steger. "Hypothesis Generation for Object Pose Estimation From local sampling to global reasoning / Frank Michel ; Gutachter: Carsten Rother, Carsten Steger ; Carsten Rother, Stefan Gumhold." Dresden : Technische Universität Dresden, 2019. http://d-nb.info/1226897592/34.
Full textGrabaskas, David. "Efficient Approaches to the Treatment of Uncertainty in Satisfying Regulatory Limits." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345464067.
Full textCarroll, James Lamond. "A Bayesian Decision Theoretical Approach to Supervised Learning, Selective Sampling, and Empirical Function Optimization." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3413.pdf.
Full textLEONARD, ANTHONY CHARLES. "HYPOTHESIS TESTING WITH THE SIMILARITY INDEX." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1005680996.
Full textSilva, Ricardo Gonçalves da. ""Testes de hipótese e critério bayesiano de seleção de modelos para séries temporais com raiz unitária"." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-19082004-163615/.
Full textTesting for unit root hypothesis in non stationary autoregressive models has been a research topic disseminated along many academic areas. As a first step for approaching this issue, this dissertation includes an extensive review highlighting the main results provided by Classical and Bayesian inferences methods. Concerning Classical approach, the role of brownian motion is discussed in a very detailed way, clearly emphasizing its application for obtaining good asymptotic statistics when we are testing for the existence of a unit root in a time series. Alternatively, for Bayesian approach, a detailed discussion is also introduced in the main text. Then, exploring an empirical façade of this dissertation, we implemented a comparative study for testing unit root based on a posteriori model's parameter density probability, taking into account the following a priori densities: Flat, Jeffreys, Normal and Beta. The inference is based on the Metropolis-Hastings algorithm and on the Monte Carlo Markov Chains (MCMC) technique. Simulated time series are used for calculating size, power and confidence intervals for the developed unit root hypothesis test. Finally, we proposed a Bayesian criterion for selecting models based on the same a priori distributions used for developing the same hypothesis tests. Obviously, both procedures are empirically illustrated through application to macroeconomic time series.
Sun, Yiping. "Rank-sum test for two-sample location problem under order restricted randomized design." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180147276.
Full textErfmeier, Alexandra. "Ursachen des Invasionserfolges von Rhododendron ponticum L. auf den Britischen Inseln Einfluss von Habitat und Genotyp /." Doctoral thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=975033476.
Full textSu, Weizhe. "Bayesian Hidden Markov Model in Multiple Testing on Dependent Count Data." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613751403094066.
Full textMartinez, Maria. "Analyse genetique d'un trait pathologique a partir de familles selectionnees : consequences d'un ecart a certaines hypotheses du modele classique de recensement." Paris 7, 1988. http://www.theses.fr/1988PA077113.
Full text"Exact conditional tests under inverse sampling." 2005. http://library.cuhk.edu.hk/record=b5892696.
Full textThesis (M.Phil.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (leaves 88-90).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.iv
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Basic Concepts --- p.6
Chapter 2.1 --- Binomial vs Inverse Sampling --- p.6
Chapter 2.2 --- Equivalence / Non-inferiority Test --- p.7
Chapter 3 --- Testing Procedures --- p.9
Chapter 3.1 --- The Model --- p.9
Chapter 3.2 --- Asymptotic Behaviors of the Estimators --- p.10
Chapter 3.2.1 --- Asymptotic Test Statistic based on Unconditional Maximum Likelihood Estimate --- p.12
Chapter 3.2.2 --- Asymptotic Test Statistic based on restricted maximum likelihood estimate --- p.13
Chapter 3.3 --- Conditional Exact Procedures --- p.16
Chapter 3.3.1 --- Non-test-statistic-based procedure --- p.17
Chapter 3.3.2 --- Test-statistic-based procedure --- p.17
Chapter 4 --- Simulation Study --- p.19
Chapter 4.1 --- Simulation Results - Type I error rate --- p.21
Chapter 4.1.1 --- Asymptotic Test Statistic based on Unconditional MLE . . --- p.21
Chapter 4.1.2 --- Asymptotic Test Statistic based on Restricted MLE . . . . --- p.22
Chapter 4.1.3 --- Non-test-statistic-based Conditional Exact Test --- p.23
Chapter 4.1.4 --- Test-statistic-based Conditional Exact Test --- p.24
Chapter 4.2 --- Simulation Results - Power --- p.25
Chapter 4.2.1 --- Asymptotic Tests - Similarity and Difference between using Unconditional and Restricted MLE --- p.25
Chapter 4.2.2 --- Conditional Exact Tests - Similarity and Difference be- tween using Non-test-statistic-based and Test-statistic-based Procedures --- p.30
Chapter 4.2.3 --- Test-statistic-based Conditional Exact Tests - Similarity and Difference between using Unconditional and Restricted MLE --- p.31
Chapter 5 --- Conclusion --- p.32
Appendices --- p.36
Chapter A. --- Simulation Result - Type I error rate --- p.36
Chapter B. --- Simulation Result - Power value --- p.42
Bibliography --- p.88
Hsieh, Ching-Ying, and 謝靖瑩. "A Geometric Mean Approach to Sampling Size Determinationfor the Equivalence Hypothesis." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/26672461433552854457.
Full text國立臺灣大學
農藝學研究所
99
Equivalence hypothesis is the correct hypothesis to confirm whether the newly developed product conforms to the current standard product. It has great applications to evaluation of generic drug products and other new clinical modalities. Two one-sided tests (TOST) procedure was proposed to test the equivalence hypothesis for two treatments. When the difference in population means between two treatments is not 0, the power function is not symmetric, hence only approximate formulas are proposed to determine the sample size for the equivalence hypothesis. The resulting sample sizes may provide either insufficient power or unnecessarily high power. We suggest geometric mean approaches to determination of the sample size for equivalence hypothesis. A numerical study was conducted to compare the performance of our proposed method with other current methods. Numerical examples illustrate the applications to bioequivalence on the logarithmic scale and to clinical equivalence on the original scale. Remarks on the usage of different methods for sample size determination for equivalence hypothesis are made.
Michel, Frank. "Hypothesis Generation for Object Pose Estimation From local sampling to global reasoning." Doctoral thesis, 2017. https://tud.qucosa.de/id/qucosa%3A33169.
Full textMcDonald, Trent 1965. "Analysis of finite population surveys : sample size and testing considerations." Thesis, 1996. http://hdl.handle.net/1957/35277.
Full textGraduation date: 1996
"Small sample properties of transmission disequilibrium test and related tests." 2007. http://library.cuhk.edu.hk/record=b5893384.
Full textThesis (M.Phil.)--Chinese University of Hong Kong, 2007.
Includes bibliographical references (leaves 68-69).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Basic Concepts --- p.1
Chapter 1.2 --- Linkage Disequilibrium --- p.5
Chapter 1.3 --- Transmission Disequilibrium Test --- p.7
Chapter 1.4 --- Scope of Thesis --- p.8
Chapter 2 --- Transmission Disequilibrium Test --- p.9
Chapter 2.1 --- The Model --- p.9
Chapter 2.2 --- The Data Structure and The Statistic --- p.12
Chapter 3 --- Small Sample Properties of Transmission Disequilibrium Test --- p.16
Chapter 3.1 --- Exact Distribution of TDT Statistic --- p.16
Chapter 3.2 --- Power under Alternative Hypothesis --- p.20
Chapter 3.3 --- P-Value --- p.29
Chapter 4 --- Exact P-Value and Power --- p.35
Chapter 5 --- Haplotype Relative Risk --- p.61
Chapter 6 --- Conclusion --- p.66
References --- p.68
"Hypothesis Testing for High-Dimensional Regression Under Extreme Phenotype Sampling of Continuous Traits." Tulane University, 2018.
Find full textExtreme phenotype sampling (EPS) is a broadly-used design to identify candidate genetic factors contributing to the variation of quantitative traits. By enriching the signals in the extreme phenotypic samples within the top and bottom percentiles, EPS can boost the study power compared with the random sampling with the same sample size. The existing statistical methods for EPS data test the variants/regions individually. However, many disorders are caused by multiple genetic factors. Therefore, it is critical to simultaneously model the effects of genetic factors, which may increase the power of current genetic studies and identify novel disease-associated genetic factors in EPS. The challenge of the simultaneous analysis of genetic data is that the number (p ~10,000) of genetic factors is typically greater than the sample size (n ~1,000) in a single study. The standard linear model would be inappropriate for this p>n problem due to the rank deficiency of the design matrix. An alternative solution is to apply a penalized regression method – the least absolute shrinkage and selection operator (LASSO). LASSO can deal with this high-dimensional (p>n) problem by forcing certain regression coefficients to be zero. Although the application of LASSO in genetic studies under random sampling has been widely studied, its statistical inference and testing under EPS remain unknown. We propose a novel sparse model (EPS-LASSO) with hypothesis test for high-dimensional regression under EPS based on a decorrelated score function to investigate the genetic associations, including the gene expression and rare variant analyses. The comprehensive simulation shows EPS-LASSO outperforms existing methods with superior power when the effects are large and stable type I error and FDR control. Together with the real data analysis of genetic study for obesity, our results indicate that EPS-LASSO is an effective method for EPS data analysis, which can account for correlated predictors.
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Chao Xu
Servidea, James Dominic. "Bridge sampling with dependent random draws : techniques and strategy /." 2002. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3048422.
Full textKing, J. Patrick. "A microsatellite-based statistic for inferring patterns of population growth: Sampling properties and hypothesis testing." Thesis, 2000. http://hdl.handle.net/1911/19523.
Full textZhang, Wei. "A comparison of four estimators of a population measure of model misfit in covariance structure analysis." 2005. http://etd.nd.edu.lib-proxy.nd.edu/ETD-db/theses/available/etd-10272005-175023/.
Full textTran, Quoc Huy. "Robust parameter estimation in computer vision: geometric fitting and deformable registration." Thesis, 2014. http://hdl.handle.net/2440/86270.
Full textThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2014
"A New Method Of Resampling Testing Nonparametric Hypotheses: Balanced Randomization Tests." Tulane University, 2014.
Find full textacase@tulane.edu
Carland, Matthew A. "A theoretical and experimental dissociation of two models of decision‐making." Thèse, 2014. http://hdl.handle.net/1866/12038.
Full textDecision‐making is a computational process of fundamental importance to many aspects of animal behavior. The prevailing model in the experimental study of decision‐making is the drift‐diffusion model, which has a long history and accounts for a broad range of behavioral and neurophysiological data. However, an alternative model – called the urgency‐gating model – has been offered which can account equally well for much of the same data in a more parsimonious and theoretically‐sound manner. In what follows, we will first trace the origins and development of the DDM, as well as give a brief overview of the manner in which it has supplied an explanatory framework for a large number of behavioral and physiological studies in the domain of decision‐making. In so doing, we will attempt to build a strong and clear case for its strengths so that it can be fairly and rigorously compared to potential alternative models. We will then re‐examine a number of the implicit and explicit theoretical assumptions made by the drift‐diffusion model, as well as highlight some of its empirical shortcomings. This analysis will serve as the contextual backdrop for our introduction and discussion of the urgency‐gating model. Finally, we present a novel experiment, the methodological design of which uniquely affords a decisive empirical dissociation of the models, the results of which illustrate the empirical and theoretical shortcomings of the drift‐diffusion model and instead offer clear support for the urgency‐gating model. We finish by discussing the potential for the urgency gating model to shed light on a number of clinical disorders, highlighting a number of future directions for research.