Dissertations / Theses on the topic 'Sampling with loaded probabilities'
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Liao, Yijie. "Testing of non-unity risk ratio under inverse sampling." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/707.
Full textSchelin, Lina. "Spatial sampling and prediction." Doctoral thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-53286.
Full textPeng, Linghua. "Normalizing constant estimation for discrete distribution simulation /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
Full textLundquist, Anders. "Contributions to the theory of unequal probability sampling." Doctoral thesis, Umeå : Department of Mathematics and Mathematical Statistics, Umeå University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-22459.
Full textShen, Gang. "Bayesian predictive inference under informative sampling and transformation." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0429104-142754/.
Full textKeywords: Ignorable Model; Transformation; Poisson Sampling; PPS Sampling; Gibber Sampler; Inclusion Probabilities; Selection Bias; Nonignorable Model; Bayesian Inference. Includes bibliographical references (p.34-35).
Grafström, Anton. "On unequal probability sampling designs." Doctoral thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-33701.
Full textCancino, Cancino Jorge Orlando. "Analyse und praktische Umsetzung unterschiedlicher Methoden des Randomized Branch Sampling." Doctoral thesis, Göttingen, 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=969133375.
Full textStevenson, Clint W. "A Logistic Regression Analysis of Utah Colleges Exit Poll Response Rates Using SAS Software." BYU ScholarsArchive, 2006. https://scholarsarchive.byu.edu/etd/1116.
Full textDubourg, Vincent. "Méta-modèles adaptatifs pour l'analyse de fiabilité et l'optimisation sous contrainte fiabiliste." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2011. http://tel.archives-ouvertes.fr/tel-00697026.
Full textCumpston, John Richard. "New techniques for household microsimulation, and their application to Australia." Phd thesis, 2011. http://hdl.handle.net/1885/9046.
Full textChen, Yen-An, and 陳彥安. "Importance Sampling for Estimating High Dimensional Joint Default Probabilities." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/hfqq5e.
Full text國立臺灣大學
應用數學科學研究所
105
We discuss the simulation method for estimating the probabilities of rare events, especially the “joint default” probabilities under different models. The methods we provide are based on importance sampling, which is a variance re- duction technique used to increase the number of samples reaching the “rare” region. There are two parts in this thesis. For the first part, we provide several importance sampling schemes, where the “asymptotically optimal” (or efficient) property is proved by applying the large deviation theory. Also, the results could be applied to the multi-dimensional scene, where most of the probabilities interested do not have an explicit expression. For the second part, we apply our idea to the model that is more complicated. It is shown that we have some satisfying results while keeping the computation efficient.
Wang, Haiou. "Logic sampling, likelihood weighting and AIS-BN : an exploration of importance sampling." Thesis, 2001. http://hdl.handle.net/1957/28769.
Full textGraduation date: 2002
"Imprecise Prior for Imprecise Inference on Poisson Sampling Model." Thesis, 2014. http://hdl.handle.net/10388/ETD-2014-04-1495.
Full textBen, Issaid Chaouki. "Effcient Monte Carlo Simulations for the Estimation of Rare Events Probabilities in Wireless Communication Systems." Diss., 2019. http://hdl.handle.net/10754/660001.
Full textKassanjee, Reshma. "Characterisation and application of tests for recent infection for HIV incidence surveillance." Thesis, 2015. http://hdl.handle.net/10539/16837.
Full textThree decades ago, the discovery of the Human Immunodeficiency Virus (HIV) was announced. The subsequent HIV pandemic has continued to devastate the global community, and many countries have set ambitious HIV reduction targets over the years. Reliable methods for measuring incidence, the rate of new infections, are essential for monitoring the virus, allocating resources, and assessing interventions. The estimation of incidence from single cross-sectional surveys using tests that distinguish between ‘recent’ and ‘non-recent’ infection has therefore attracted much interest. The approach provides a promising alternative to traditional estimation methods which often require more complex survey designs, rely on poorly known inputs, and are prone to bias. More specifically, the prevalence of HIV and ‘recent’ HIV infection, as measured in a survey, are used together with relevant test properties to infer incidence. However, there has been a lack of methodological consensus in the field, caused by limited applicability of proposed estimators, inconsistent test characterisation (or estimation of test properties) and uncertain test performance. This work aims to address these key obstacles. A general theoretical framework for incidence estimation is developed, relaxing unrealistic assumptions used in earlier estimators. Completely general definitions of the required test properties emerge from the analysis. The characterisation of tests is then explored: a new approach, that utilises specimens from subjects observed only once after infection, is demonstrated; and currently-used approaches, that require that subjects are followed-up over time after infection, are systematically benchmarked. The first independent and consistent characterisation of multiple candidate tests is presented, and was performed on behalf of the Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA), which was established to provide guidance and foster consensus in the field. Finally, the precision of the incidence estimator is presented as an appropriate metric for evaluating, optimising and comparing tests, and the framework serves to counter existing misconceptions about test performance. The contributions together provide sound theoretical and methodological foundations for the application, characterisation and optimisation of recent infection tests for HIV incidence surveillance, allowing the focus to now shift towards practical application.
Yang, Jiaxi. "Sequential Rerandomization in the Context of Small Samples." Thesis, 2021. https://doi.org/10.7916/d8-80h5-zk34.
Full textLefebvre, Isabelle. "Estimation simplifiée de la variance pour des plans complexes." Thèse, 2016. http://hdl.handle.net/1866/19112.
Full textIn a complex design framework, standard variance estimation methods entail substantial challenges. As we know, conventional variance estimators involve second order inclusion probabilities, which can be difficult to compute for some sampling designs. Also, confidentiality standards generally prevent second order inclusion probabilities to be included in external microdata files (often in the form of bootstrap weights). Based on Ohlsson’s sequential Poisson sampling method (1998), we suggest a simplified estimator for which we only need first order inclusion probabilities. The idea is to approximate a survey strategy (which consists of a sampling design and an estimator) by an equivalent strategy for which a Poisson sampling design is used. We will discuss proportional to size sampling and proportional to size cluster sampling. Results of a simulation study will be presented.
Vallée, Audrey-Anne. "Estimation de la variance en présence de données imputées pour des plans de sondage à grande entropie." Thèse, 2014. http://hdl.handle.net/1866/11120.
Full textVariance estimation in the case of item nonresponse treated by imputation is the main topic of this work. Treating the imputed values as if they were observed may lead to substantial under-estimation of the variance of point estimators. Classical variance estimators rely on the availability of the second-order inclusion probabilities, which may be difficult (even impossible) to calculate. We propose to study the properties of variance estimators obtained by approximating the second-order inclusion probabilities. These approximations are expressed in terms of first-order inclusion probabilities and are usually valid for high entropy sampling designs. The results of a simulation study evaluating the properties of the proposed variance estimators in terms of bias and mean squared error will be presented.