Dissertations / Theses on the topic 'Binary model estimation'
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Boudineau, Mégane. "Vers la résolution "optimale" de problèmes inverses non linéaires parcimonieux grâce à l'exploitation de variables binaires sur dictionnaires continus : applications en astrophysique." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30020/document.
Full textThis thesis deals with solutions of nonlinear inverse problems using a sparsity prior; more specifically when the data can be modelled as a linear combination of a few functions, which depend non-linearly on a "location" parameter, i.e. frequencies for spectral analysis or time-delay for spike train deconvolution. These problems are generally reformulated as linear sparse approximation problems, thanks to an evaluation of the nonlinear functions at location parameters discretised on a thin grid, building a "discrete dictionary". However, such an approach has two major drawbacks. On the one hand, the discrete dictionary is highly correlated; classical sub-optimal methods such as L1- penalisation or greedy algorithms can then fail. On the other hand, the estimated location parameter, which belongs to the discretisation grid, is necessarily discrete and that leads to model errors. To deal with these issues, we propose in this work an exact sparsity model, thanks to the introduction of binary variables, and an optimal solution of the problem with a "continuous dictionary" allowing a continuous estimation of the location parameter. We focus on two research axes, which we illustrate with problems such as spike train deconvolution and spectral analysis of unevenly sampled data. The first axis focusses on the "dictionary interpolation" principle, which consists in a linearisation of the continuous dictionary in order to get a constrained linear sparse approximation problem. The introduction of binary variables allows us to reformulate this problem as a "Mixed Integer Program" (MIP) and to exactly model the sparsity thanks to the "pseudo-norm L0". We study different kinds of dictionary interpolation and constraints relaxation, in order to solve the problem optimally thanks to MIP classical algorithms. For the second axis, in a Bayesian framework, the binary variables are supposed random with a Bernoulli distribution and we model the sparsity through a Bernoulli-Gaussian prior. This model is extended to take into account continuous location parameters (BGE model). We then estimate the parameters from samples drawn using Markov chain Monte Carlo algorithms. In particular, we show that marginalising the amplitudes allows us to improve the sampling of a Gibbs algorithm in a supervised case (when the model's hyperparameters are known). In an unsupervised case, we propose to take advantage of such a marginalisation through a "Partially Collapsed Gibbs Sampler." Finally, we adapt the BGE model and associated samplers to a topical science case in Astrophysics: the detection of exoplanets from radial velocity measurements. The efficiency of our method will be illustrated with simulated data, as well as actual astrophysical data
Xu, Xingbai Xu. "Asymptotic Analysis for Nonlinear Spatial and Network Econometric Models." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461249529.
Full textFilippou, Panagiota. "Penalized likelihood estimation of trivariate additive binary models." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10042688/.
Full textTzamourani, Panagiota. "Robustness, semiparametric estimation and goodness-of-fit of latent trait models." Thesis, London School of Economics and Political Science (University of London), 1999. http://etheses.lse.ac.uk/1623/.
Full textSepato, Sandra Moepeng. "Generalized linear mixed model and generalized estimating equation for binary longitudinal data." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/43143.
Full textDissertation (MSc)--University of Pretoria, 2014.
lk2014
Statistics
MSc
Unrestricted
Asar, Ozgur. "On Multivariate Longitudinal Binary Data Models And Their Applications In Forecasting." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614510/index.pdf.
Full texts Stress and Children'
s Morbidity (MSCM) data are used to illustrate this comparison in real life. Results show that marginalized models yield better forecasting results compared to marginal models. Simulation results are in agreement with these results as well.
Polcer, James. "Generalized Bathtub Hazard Models for Binary-Transformed Climate Data." TopSCHOLAR®, 2011. http://digitalcommons.wku.edu/theses/1060.
Full textSchildcrout, Jonathan Scott. "Marginal modeling of longitudinal, binary response data : semiparametric and parametric estimation with long response series and an efficient outcome dependent sampling design /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/9540.
Full textAhmed, Mohamed Salem. "Contribution à la statistique spatiale et l'analyse de données fonctionnelles." Thesis, Lille 3, 2017. http://www.theses.fr/2017LIL30047/document.
Full textThis thesis is about statistical inference for spatial and/or functional data. Indeed, weare interested in estimation of unknown parameters of some models from random or nonrandom(stratified) samples composed of independent or spatially dependent variables.The specificity of the proposed methods lies in the fact that they take into considerationthe considered sample nature (stratified or spatial sample).We begin by studying data valued in a space of infinite dimension or so-called ”functionaldata”. First, we study a functional binary choice model explored in a case-controlor choice-based sample design context. The specificity of this study is that the proposedmethod takes into account the sampling scheme. We describe a conditional likelihoodfunction under the sampling distribution and a reduction of dimension strategy to definea feasible conditional maximum likelihood estimator of the model. Asymptotic propertiesof the proposed estimates as well as their application to simulated and real data are given.Secondly, we explore a functional linear autoregressive spatial model whose particularityis on the functional nature of the explanatory variable and the structure of the spatialdependence. The estimation procedure consists of reducing the infinite dimension of thefunctional variable and maximizing a quasi-likelihood function. We establish the consistencyand asymptotic normality of the estimator. The usefulness of the methodology isillustrated via simulations and an application to some real data.In the second part of the thesis, we address some estimation and prediction problemsof real random spatial variables. We start by generalizing the k-nearest neighbors method,namely k-NN, to predict a spatial process at non-observed locations using some covariates.The specificity of the proposed k-NN predictor lies in the fact that it is flexible and allowsa number of heterogeneity in the covariate. We establish the almost complete convergencewith rates of the spatial predictor whose performance is ensured by an application oversimulated and environmental data. In addition, we generalize the partially linear probitmodel of independent data to the spatial case. We use a linear process for disturbancesallowing various spatial dependencies and propose a semiparametric estimation approachbased on weighted likelihood and generalized method of moments methods. We establishthe consistency and asymptotic distribution of the proposed estimators and investigate thefinite sample performance of the estimators on simulated data. We end by an applicationof spatial binary choice models to identify UADT (Upper aerodigestive tract) cancer riskfactors in the north region of France which displays the highest rates of such cancerincidence and mortality of the country
Tuzilova, Kristyna. "Pre-play interactive trading in tennis: probability to win a match in Grand Slam tournaments." Master's thesis, Universidade de Évora, 2017. http://hdl.handle.net/10174/21760.
Full textLund-Jensen, Kasper. "Essays on forecast evaluation and financial econometrics." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:01fb58e7-c857-43ff-998f-7b8e928a49bf.
Full textRodrigues, José Tenylson Gonçalves. "Análise de dados longitudinais para variáveis binárias." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4531.
Full textFinanciadora de Estudos e Projetos
The objective of this work is to present techniques of regression analysis for longitudinal data when the response variable is binary. Initially, there is a review of generalized linear models, marginal models, transition models, mixed models, and logistic regression methods of estimation, which will be necessary for the development of work. In addition to the methods of estimation, some structures of correlation will be studied in an attempt to capture the intra-individual serial dependence over time. These methods were applied in two situations, one where the response variable is continuous and normal distribution, and another when the response variable has the Bernoulli distribution. It was also sought to explore and present techniques for selection of models and diagnostics for the two cases. Finally, an application of the above methodology will be presented using a set of real data.
O objetivo deste trabalho é apresentar técnicas de análise de regressão para dados longitudinais quando a variável resposta é binária. Inicialmente, é feita uma revisão sobre modelos lineares generalizados, modelos marginais, modelos de transição, modelos mistos, regressão logística e métodos de estimação, pois serão necessários para o desenvolvimento do trabalho. Além dos métodos de estimação, algumas estruturas de correlação serão estudadas, na tentativa de captar a dependência serial intra-indivíduo ao longo do tempo. Estes métodos foram aplicados em duas situações; uma quando a variável resposta é contínua, e se assume ter distribuição normal, e a outra quando a variável resposta assume ter distribuição de Bernoulli. Também se procurou pesquisar e apresentar técnicas de seleção de modelos e de diagnósticos para os dois casos. Ao final, uma aplicação com a metodologia pesquisada será apresentada utilizando um conjunto de dados reais.
Yung, Hsing Tang, and 唐永興. "Rank Estimation in the Binary Choice Model." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/35512613268564721418.
Full text國立中正大學
國際經濟研究所
89
Abstract Binary choice model has been widely used in analysis of a single dependent variable that is observed as a dichotomous value. In the thesis, we focus on the rank-based estimations of binary choice model. The conclusive objectives are as follows: Ⅰ. Introduce the rank-based estimation of the slope parameters in the binary choice model: maximum rank correlation (MRC) estimator. Ⅱ. Impose the rank-based estimation of location parameter in the binary choice model. Ⅲ. Compare with other research which is based on parametric estimation in binary choice model by using the Monte Carlo simulation and an empirical study to illustrate the differences between parametric and non-parametric estimation since the MRC estimator is non-parametric with respect to the model structure and non-parametric with respect to the error term.
Hsieh, Wei-shan, and 謝瑋珊. "Optimum Designs for Model Discrimination and Estimation in Binary Response Models." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/80409170048675751043.
Full text國立中山大學
應用數學系研究所
93
This paper is concerned with the problem of finding an experimental design for discrimination between two rival models and for model robustness that minimizing the maximum bias simultaneously in binary response experiments. The criterion for model discrimination is based on the $T$-optimality criterion proposed in Atkinson and Fedorov (1975), which maximizes the sum of squares of deviations between the two rival models while the criterion for model robustness is based on minimizing the maximum probability bias of the two rival models. In this paper we obtain the optimum designs satisfy the above two criteria for some commonly used rival models in binary response experiments such as the probit and logit models etc.
PAZIENZA, MARIA GRAZIA. "Determinanti finanziarie e fiscali delle decisioni di investimento: teorie, analisi empiriche e un'analisi con il modello probit." Doctoral thesis, 1996. http://hdl.handle.net/2158/676539.
Full textLombardi, Alexander L. "Bayesian Model Selection and Parameter Estimation for Gravitational Wave Signals from Binary Black Hole Coalescences." 2015. https://scholarworks.umass.edu/masters_theses_2/283.
Full textHatangala, Chinthana. "Identifying the Future Directions of Australian Excess Stock Returns and Their Determinants Using Binary Models." Thesis, 2016. https://vuir.vu.edu.au/32888/.
Full textRamadan, Anas. "Weighted Semiparametric Estimator for Binary Response Models." Thesis, 2013. http://hdl.handle.net/10214/7248.
Full textWu, Jia-Ling, and 吳嘉玲. "Generalized estimating equations approach with model selection for analyzing dependent binary agreement data." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/50078375565426313835.
Full text國立彰化師範大學
統計資訊研究所
97
In clinical studies, we usually concern agreement between different raters or methods. The kappa statistic is the most popular index for assessing agreement of categorical or ordinal outcomes. In this thesis, we propose the generalized estimating equation (GEE) approach to estimate kappa measures on inter and intra agreement for correlated dichotomous data and test the equality of the several dependent kappa coefficients. In addition, we apply the quasi-likelihood under the independence model criterion (QIC) measures and Pearson 2 statistics for model selection. Simulation studies are conducted to evaluate the performance of GEE under each model-selection criterion. We conclude that model selection is a crucial step before making inference with kappa estimates. Analyses of two studies of repeated binary ratings are used for illustration.
"Estimating the partition function of binary pairwise graphical models using generalized belief propagation." 2015. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291309.
Full textThesis M.Phil. Chinese University of Hong Kong 2015.
Includes bibliographical references (leaves 74-78).
Abstracts also in Chinese.
Title from PDF title page (viewed on 19, September, 2016).
Flimmel, Samuel. "Odhady parametrů useknutých časových řad." Master's thesis, 2015. http://www.nusl.cz/ntk/nusl-331689.
Full textLuh, Chii-Yun, and 陸啟芸. "Optimal designs for multiresponse models and robust optimal designs for estimating extreme quantiles in binary response experiment." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/67019883698821804535.
Full text國立中山大學
應用數學系
87
In Chapter 1, we consider D-, A- and Ds-optimal design problems in linear regression models with a one-dimensional control variable and k-dimensional response variable Y=(Y1,...,Yk) which have some common parameters. The D- and Ds-optimal designs obtained are independent of the covariance structure. The A-optimal designs in some low degree cases are also found explicitly which depend on the covariance structure. In Chapter 2, we consider model robust design criterion for estimating extreme quantiles in binary response experiment if there is uncertainly on the true model. We find the model robust optimal designs are very efficient for the cases studied. We also propose quantile robust design criterion as well as model and quantile robust design criterion, and examples are given to illustrate use of these criteria.
"Comparison of generalized estimating equations and random effects models for longitudinal binary outcomes: Application to speech delay in Thai children." Tulane University, 2010.
Find full textLi, Zhuokai. "Multivariate semiparametric regression models for longitudinal data." Thesis, 2014. http://hdl.handle.net/1805/6462.
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