Дисертації з теми "Sampling with loaded probabilities"
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1
Liao, Yijie. "Testing of non-unity risk ratio under inverse sampling." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/707.
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2
Schelin, 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.
Повний текст джерелаАнотація:
This thesis discusses two aspects of spatial statistics: sampling and prediction. In spatial statistics, we observe some phenomena in space. Space is typically of two or three dimensions, but can be of higher dimension. Questions in mind could be; What is the total amount of gold in a gold-mine? How much precipitation could we expect in a specific unobserved location? What is the total tree volume in a forest area? In spatial sampling the aim is to estimate global quantities, such as population totals, based on samples of locations (papers III and IV). In spatial prediction the aim is to estimate local quantities, such as the value at a single unobserved location, with a measure of uncertainty (papers I, II and V). In papers III and IV, we propose sampling designs for selecting representative probability samples in presence of auxiliary variables. If the phenomena under study have clear trends in the auxiliary space, estimation of population quantities can be improved by using representative samples. Such samples also enable estimation of population quantities in subspaces and are especially needed for multi-purpose surveys, when several target variables are of interest. In papers I and II, the objective is to construct valid prediction intervals for the value at a new location, given observed data. Prediction intervals typically rely on the kriging predictor having a Gaussian distribution. In paper I, we show that the distribution of the kriging predictor can be far from Gaussian, even asymptotically. This motivated us to propose a semiparametric method that does not require distributional assumptions. Prediction intervals are constructed from the plug-in ordinary kriging predictor. In paper V, we consider prediction in the presence of left-censoring, where observations falling below a minimum detection limit are not fully recorded. We review existing methods and propose a semi-naive method. The semi-naive method is compared to one model-based method and two naive methods, all based on variants of the kriging predictor.
3
Peng, Linghua. "Normalizing constant estimation for discrete distribution simulation /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.
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4
Lundquist, 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.
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5
Shen, Gang. "Bayesian predictive inference under informative sampling and transformation." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0429104-142754/.
Повний текст джерелаАнотація:
Thesis (M.S.) -- Worcester Polytechnic Institute.Keywords: Ignorable Model; Transformation; Poisson Sampling; PPS Sampling; Gibber Sampler; Inclusion Probabilities; Selection Bias; Nonignorable Model; Bayesian Inference. Includes bibliographical references (p.34-35).
6
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.
Повний текст джерелаАнотація:
The main objective in sampling is to select a sample from a population in order to estimate some unknown population parameter, usually a total or a mean of some interesting variable. When the units in the population do not have the same probability of being included in a sample, it is called unequal probability sampling. The inclusion probabilities are usually chosen to be proportional to some auxiliary variable that is known for all units in the population. When unequal probability sampling is applicable, it generally gives much better estimates than sampling with equal probabilities. This thesis consists of six papers that treat unequal probability sampling from a finite population of units. A random sample is selected according to some specified random mechanism called the sampling design. For unequal probability sampling there exist many different sampling designs. The choice of sampling design is important since it determines the properties of the estimator that is used. The main focus of this thesis is on evaluating and comparing different designs. Often it is preferable to select samples of a fixed size and hence the focus is on such designs. It is also important that a design has a simple and efficient implementation in order to be used in practice by statisticians. Some effort has been made to improve the implementation of some designs. In Paper II, two new implementations are presented for the Sampford design. In general a sampling design should also have a high level of randomization. A measure of the level of randomization is entropy. In Paper IV, eight designs are compared with respect to their entropy. A design called adjusted conditional Poisson has maximum entropy, but it is shown that several other designs are very close in terms of entropy. A specific situation called real time sampling is treated in Paper III, where a new design called correlated Poisson sampling is evaluated. In real time sampling the units pass the sampler one by one. Since each unit only passes once, the sampler must directly decide for each unit whether or not it should be sampled. The correlated Poisson design is shown to have much better properties than traditional methods such as Poisson sampling and systematic sampling.
7
Cancino, 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.
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8
Stevenson, 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.
Повний текст джерелаАнотація:
In this study I examine voter response at an interview level using a dataset of 7562 voter contacts (including responses and nonresponses) in the 2004 Utah Colleges Exit Poll. In 2004, 4908 of the 7562 voters approached responded to the exit poll for an overall response rate of 65 percent. Logistic regression is used to estimate factors that contribute to a success or failure of each interview attempt. This logistic regression model uses interviewer characteristics, voter characteristics (both respondents and nonrespondents), and exogenous factors as independent variables. Voter characteristics such as race, gender, and age are strongly associated with response. An interviewer's prior retail sales experience is associated with whether a voter will decide to respond to a questionnaire or not. The only exogenous factor that is associated with voter response is whether the interview occurred in the morning or afternoon.
9
Dubourg, 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.
Повний текст джерелаАнотація:
Cette thèse est une contribution à la résolution du problème d'optimisation sous contrainte de fiabilité. Cette méthode de dimensionnement probabiliste vise à prendre en compte les incertitudes inhérentes au système à concevoir, en vue de proposer des solutions optimales et sûres. Le niveau de sûreté est quantifié par une probabilité de défaillance. Le problème d'optimisation consiste alors à s'assurer que cette probabilité reste inférieure à un seuil fixé par les donneurs d'ordres. La résolution de ce problème nécessite un grand nombre d'appels à la fonction d'état-limite caractérisant le problème de fiabilité sous-jacent. Ainsi,cette méthodologie devient complexe à appliquer dès lors que le dimensionnement s'appuie sur un modèle numérique coûteux à évaluer (e.g. un modèle aux éléments finis). Dans ce contexte, ce manuscrit propose une stratégie basée sur la substitution adaptative de la fonction d'état-limite par un méta-modèle par Krigeage. On s'est particulièrement employé à quantifier, réduire et finalement éliminer l'erreur commise par l'utilisation de ce méta-modèle en lieu et place du modèle original. La méthodologie proposée est appliquée au dimensionnement des coques géométriquement imparfaites soumises au flambement.
10
Cumpston, John Richard. "New techniques for household microsimulation, and their application to Australia." Phd thesis, 2011. http://hdl.handle.net/1885/9046.
Повний текст джерелаАнотація:
Household microsimulation models are sometimes used by national governments to make long-term projections of proposed policy changes. They are costly to develop and maintain, and sometimes have short lifetimes. Most present national models have limited interactions between agents, few regions and long simulation cycles. Some models are very slow to run. Overcoming these limitations may open up a much wider range of government, business and individual uses.
This thesis suggests techniques to help make multi-purpose dynamic microsimulations of households, with fine spatial resolutions, high sampling densities and short simulation cycles. Techniques suggested are: * simulation by sampling with loaded probabilities
* proportional event alignment
* event alignment using random sampling
* immediate matching by probability-weighting
* immediate 'best of n' matching.
All of these techniques are tested in artificial situations. Three of them - sampling with loaded probabilities, alignment using random sampling and best of n matching - are successfully tested in the Cumpston model, a household microsimulation model developed for this thesis.
Sampling with loaded probabilities is found to give almost identical results to the traditional all-case sampling, but be quicker. The suggested alignment and matching techniques are shown to give less distortion and generally lower runtimes than some techniques currently in use.
The Cumpston model is based on a 1% sample from the 2001 Australian census. Individuals, families, households and dwellings are included. Immigration and emigration are separately simulated, together with internal migration between 57 statistical divisions. Transitions between 8 person types are simulated, and between 9 occupations. The model projects education, employment, earnings and retirement savings for each individual, and dwelling values, rents and housing loans for each household. The onset and development of diseases for each individual are simulated. Validation of the model was based on methods used by the Orcutt, CORSIM, DYNACAN and APPSIM models. Iterative methods for model calibration are described, together with a statistical test for creep in multiple runs.
The model takes about 85 seconds to make projections for 50 years with yearly simulation cycles. After standardizing for sample size and projection years, this is a little slower than the fastest national models currently operating. A planned extension of the model is to 2.2 million persons over 2,214 areas, synthesized from 2011 census tabulations. Using multithreading where feasible, a 50-year projection may take about 10 minutes.
11
Chen, Yen-An, and 陳彥安. "Importance Sampling for Estimating High Dimensional Joint Default Probabilities." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/hfqq5e.
Повний текст джерелаАнотація:
碩士國立臺灣大學
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.
12
Wang, Haiou. "Logic sampling, likelihood weighting and AIS-BN : an exploration of importance sampling." Thesis, 2001. http://hdl.handle.net/1957/28769.
Повний текст джерелаАнотація:
Logic Sampling, Likelihood Weighting and AIS-BN are three variants of
stochastic sampling, one class of approximate inference for Bayesian networks.
We summarize the ideas underlying each algorithm and the relationship among
them. The results from a set of empirical experiments comparing Logic Sampling,
Likelihood Weighting and AIS-BN are presented. We also test the impact
of each of the proposed heuristics and learning method separately and in combination
in order to give a deeper look into AIS-BN, and see how the heuristics
and learning method contribute to the power of the algorithm.
Key words: belief network, probability inference, Logic Sampling, Likelihood
Weighting, Importance Sampling, Adaptive Importance Sampling Algorithm for
Evidential Reasoning in Large Bayesian Networks(AIS-BN), Mean Percentage
Error (MPE), Mean Square Error (MSE), Convergence Rate, heuristic, learning
method.Graduation date: 2002
13
"Imprecise Prior for Imprecise Inference on Poisson Sampling Model." Thesis, 2014. http://hdl.handle.net/10388/ETD-2014-04-1495.
Повний текст джерелаАнотація:
Prevalence is a valuable epidemiological measure about the burden of disease in a community for planning health services; however, true prevalence is typically underestimated and there exists no
reliable method of confirming the estimate of this prevalence in question. This thesis studies imprecise priors for the development of a statistical reasoning framework regarding this epidemiological decision making problem. The concept of imprecise probabilities introduced by Walley (1991) is
adopted for the construction of this inferential framework in order to model prior ignorance and quantify the degree of imprecision associated with the inferential process.
The study is restricted to the standard and zero-truncated Poisson sampling models that give an exponential family with a canonical log-link function because of the mechanism involved with the estimation of population size. A three-parameter exponential family of posteriors which includes the normal and log-gamma as limiting cases is introduced by applying normal priors on the canonical parameter of the Poisson sampling models. The canonical parameters simplify dealing with families of priors as Bayesian updating corresponds to a translation of the family in the canonical hyperparameter space. The canonical link function creates a linear relationship between regression coefficients of explanatory variables and the canonical parameters of the sampling distribution. Thus, normal priors on the regression coefficients induce normal priors on the canonical parameters leading to a higher-dimensional exponential family of posteriors whose limiting cases are again normal or log-gamma.
All of these implementations are synthesized to build the ipeglim package (Lee, 2013) that
provides a convenient method for characterizing imprecise probabilities and visualizing their translation, soft-linearity, and focusing behaviours. A characterization strategy for imprecise priors is introduced for instances when there exists a state of complete ignorance. The learning process of an individual intentional unit, the agreement process between several intentional units, and situations concerning prior-data conflict are graphically illustrated. Finally, the methodology is applied for re-analyzing the data collected from the epidemiological disease surveillance of three specific cases – Cholera epidemic (Dahiya, 1973), Down’s syndrome (Zelterman, 1988), and the female users of methamphetamine and heroin (B ̈
ohning, 2009).
14
Ben, 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.
Повний текст джерелаАнотація:
Simulation methods are used when closed-form solutions do not exist. An interesting simulation method that has been widely used in many scientific fields is the Monte Carlo method. Not only it is a simple technique that enables to estimate the
quantity of interest, but it can also provide relevant information about the value to be
estimated through its confidence interval. However, the use of classical Monte Carlo
method is not a reasonable choice when dealing with rare event probabilities. In fact,
very small probabilities require a huge number of simulation runs, and thus, the computational time of the simulation increases significantly. This observation lies behind the main motivation of the present work. In this thesis, we propose efficient importance sampling estimators to evaluate rare events probabilities. In the first part of
the thesis, we consider a variety of turbulence regimes, and we study the outage probability of free-space optics communication systems under a generalized pointing error model with both a nonzero boresight component and different horizontal and vertical jitter effects. More specifically, we use an importance sampling approach,based on the exponential twisting technique to offer fast and accurate results. We also
show that our approach extends to the multihop scenario. In the second part of the
thesis, we are interested in assessing the outage probability achieved by some diversity techniques over generalized fading channels. In many circumstances, this is related to the difficult question of analyzing the statistics of the sum of random variables.
More specifically, we propose robust importance sampling schemes that efficiently evaluate the outage probability of diversity receivers over Gamma-Gamma, α − µ, κ − µ, and η − µ fading channels. The proposed estimators satisfy the well-known bounded relative error criterion for both maximum ratio combining and equal gain
combining cases. We show the accuracy and the efficiency of our approach compared
to naive Monte Carlo via some selected numerical simulations in both case studies.
In the last part of this thesis, we propose efficient importance sampling estimators
for the left tail of positive Gaussian quadratic forms in both real and complex settings. We show that these estimators possess the bounded relative error property.
These estimators are then used to estimate the outage probability of maximum ratio
combining diversity receivers over correlated Nakagami-m or correlated Rician fading
channels
15
Kassanjee, Reshma. "Characterisation and application of tests for recent infection for HIV incidence surveillance." Thesis, 2015. http://hdl.handle.net/10539/16837.
Повний текст джерелаАнотація:
A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. 21 October, 2014.Three 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.
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Yang, Jiaxi. "Sequential Rerandomization in the Context of Small Samples." Thesis, 2021. https://doi.org/10.7916/d8-80h5-zk34.
Повний текст джерелаАнотація:
Rerandomization (Morgan & Rubin, 2012) is designed for the elimination of covariate imbalance at the design stage of causal inference studies. By improving the covariate balance, rerandomization helps provide more precise and trustworthy estimates (i.e., lower variance) of the average treatment effect (ATE). However, there are only a limited number of studies considering rerandomization strategies or discussing the covariate balance criteria that are observed before conducting the rerandomization procedure. In addition, researchers may find more difficulty in ensuring covariate balance across groups with small-sized samples. Furthermore, researchers conducting experimental design studies in psychology and education fields may not be able to gather data from all subjects simultaneously. Subjects may not arrive at the same time and experiments can hardly wait until the recruitment of all subjects.
As a result, we have presented the following research questions:
1) How does the rerandomization procedure perform when the sample size is small?
2) Are there any other balancing criteria that may work better than the Mahalanobis distance in the context of small samples?
3) How well does the balancing criterion work in a sequential rerandomization design?
Based on the Early Childhood Longitudinal Study, Kindergarten Class, a Monte-Carlo simulation study is presented for finding a better covariate balance criterion with respect to small samples. In this study, the neural network predicting model is used to calculate missing counterfactuals. Then, to ensure covariate balance in the context of small samples, the rerandomization procedure uses various criteria measuring covariate balance to find the specific criterion for the most precise estimate of sample average treatment effect. Lastly, a relatively good covariate balance criterion is adapted to Zhou et al.’s (2018) sequential rerandomization procedure and we examined its performance.
In this dissertation, we aim to identify the best covariate balance criterion using the rerandomization procedure to determine the most appropriate randomized assignment with respect to small samples. On the use of Bayesian logistic regression with Cauchy prior as the covariate balance criterion, there is a 19% decrease in the root mean square error (RMSE) of the estimated sample average treatment effect compared to pure randomization procedures. Additionally, it is proved to work effectively in sequential rerandomization, thus making a meaningful contribution to the studies of psychology and education. It further enhances the power of hypothesis testing in randomized experimental designs.
17
Lefebvre, Isabelle. "Estimation simplifiée de la variance pour des plans complexes." Thèse, 2016. http://hdl.handle.net/1866/19112.
Повний текст джерелаАнотація:
En présence de plans de sondage complexes, les méthodes classiques d’estimation de la variance présentent certains défis. En effet, les estimateurs de variance usuels requièrent les probabilités d’inclusion d’ordre deux qui peuvent être complexes à obtenir pour certains plans de sondage. De plus, pour des raisons de confidentialité, les fichiers externes de microdonnées n’incluent généralement pas les probabilités d’inclusion d’ordre deux (souvent sous la forme de poids bootstrap). En s’inspirant d’une approche développée par Ohlsson (1998) dans le contexte de l’échantillonnage de Poisson séquentiel, nous proposons un estimateur ne requérant que les probabilités d’inclusion d’ordre un. L’idée est d’approximer la stratégie utilisée par l’enquête (consistant du choix d’un plan de sondage et d’un estimateur) par une stratégie équivalente dont le plan de sondage est le plan de Poisson. Nous discuterons des plans proportionnels à la taille avec ou sans grappes. Les résultats d’une étude par simulation seront présentés.In 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.
18
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.
Повний текст джерелаАнотація:
Les travaux portent sur l’estimation de la variance dans le cas d’une non- réponse partielle traitée par une procédure d’imputation. Traiter les valeurs imputées comme si elles avaient été observées peut mener à une sous-estimation substantielle de la variance des estimateurs ponctuels. Les estimateurs de variance usuels reposent sur la disponibilité des probabilités d’inclusion d’ordre deux, qui sont parfois difficiles (voire impossibles) à calculer. Nous proposons d’examiner les propriétés d’estimateurs de variance obtenus au moyen d’approximations des probabilités d’inclusion d’ordre deux. Ces approximations s’expriment comme une fonction des probabilités d’inclusion d’ordre un et sont généralement valides pour des plans à grande entropie. Les résultats d’une étude de simulation, évaluant les propriétés des estimateurs de variance proposés en termes de biais et d’erreur quadratique moyenne, seront présentés.Variance 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.