Relevant bibliographies by topics / Binary model estimation

Academic literature on the topic 'Binary model estimation'

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Published: 10 March 2023

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Journal articles on the topic "Binary model estimation"

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Nishizawa, Kazutomo, and Iwaro Takahashi. "ESTIMATION METHODS BY STOCHASTIC MODEL IN BINARY AND TERNARY AHP." Journal of the Operations Research Society of Japan 50, no. 2 (2007): 101–22. http://dx.doi.org/10.15807/jorsj.50.101.

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Allman, Elizabeth, Hector Banos Cervantes, Serkan Hosten, Kaie Kubjas, Daniel Lemke, John Rhodes, and Piotr Zwiernik. "Maximum likelihood estimation of the Latent Class Model through model boundary decomposition." Journal of Algebraic Statistics 10, no. 1 (April 10, 2019): 51–84. http://dx.doi.org/10.18409/jas.v10i1.75.

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The Expectation-Maximization (EM) algorithm is routinely used for the maximum likelihood estimation in the latent class analysis. However, the EM algorithm comes with no guarantees of reaching the global optimum. We study the geometry of the latent class model in order to understand the behavior of the maximum likelihood estimator. In particular, we characterize the boundary stratification of the binary latent class model with a binary hidden variable. For small models, such as for three binary observed variables, we show that this stratification allows exact computation of the maximum likelihood estimator. In this case we use simulations to study the maximum likelihood estimation attraction basins of the various strata. Our theoretical study is complemented with a careful analysis of the EM fixed point ideal which provides an alternative method of studying the boundary stratification and maximizing the likelihood function. In particular, we compute the minimal primes of this ideal in the case of a binary latent class model with a binary or ternary hidden random variable.
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Amin, Muhammad, Muhammad Nauman Akram, B. M. Golam Kibria, Huda M. Alshanbari, Nahid Fatima, and Ahmed Elhassanein. "On the Estimation of the Binary Response Model." Axioms 12, no. 2 (February 8, 2023): 175. http://dx.doi.org/10.3390/axioms12020175.

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The binary logistic regression model (LRM) is practical in situations when the response variable (RV) is dichotomous. The maximum likelihood estimator (MLE) is generally considered to estimate the LRM parameters. However, in the presence of multicollinearity (MC), the MLE is not the correct choice due to its inflated standard deviation (SD) and standard errors (SE) of the estimates. To combat MC, commonly used biased estimators, i.e., the Ridge estimators (RE) and Liu estimators (LEs), are preferred. However, most of the time, the traditional LE attains a negative value for its Liu parameter (LP), which is considered to be a major drawback. Therefore, to overcome this issue, we proposed a new adjusted LE for the binary LRM. Owing to numerical evaluation purposes, Monte Carlo simulation (MCS) study is performed under different conditions where bias and mean squared error are the performance criteria. Findings showed the superiority of our proposed estimator in comparison with the other estimation methods due to the existence of high but imperfect multicollinearity, which clearly means that it is consistent when the regressors are multicollinear. Furthermore, the findings demonstrated that whenever there is MC, the MLE is not the best choice. Finally, a real application is being considered to be evidence for the advantage of the intended estimator. The MCS and the application findings pointed out that the considered adjusted LE for the binary logistic regression model is a more efficient estimation method whenever the regressors are highly multicollinear.
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Assoudou, Souad, and Belkheir Essebbar. "A Bayesian model for binary Markov chains." International Journal of Mathematics and Mathematical Sciences 2004, no. 8 (2004): 421–29. http://dx.doi.org/10.1155/s0161171204202319.

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This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC) techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.
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Yildiz, Neşe. "ESTIMATION OF BINARY CHOICE MODELS WITH LINEAR INDEX AND DUMMY ENDOGENOUS VARIABLES." Econometric Theory 29, no. 2 (March 28, 2013): 354–92. http://dx.doi.org/10.1017/s0266466612000436.

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This paper presents computationally simple estimators for the index coefficients in a binary choice model with a binary endogenous regressor without relying on distributional assumptions or on large support conditions and yields root-n consistent and asymptotically normal estimators. We develop a multistep method for estimating the parameters in a triangular, linear index, threshold-crossing model with two equations. Such an econometric model might be used in testing for moral hazard while allowing for asymmetric information in insurance markets. In outlining this new estimation method two contributions are made. The first one is proposing a novel “matching” estimator for the coefficient on the binary endogenous variable in the outcome equation. Second, in order to establish the asymptotic properties of the proposed estimators for the coefficients of the exogenous regressors in the outcome equation, the results of Powell, Stock, and Stoker (1989, Econometrica 75, 1403–1430) are extended to cover the case where the average derivative estimation requires a first-step semiparametric procedure.
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de Jong, Robert M., and Tiemen Woutersen. "DYNAMIC TIME SERIES BINARY CHOICE." Econometric Theory 27, no. 4 (March 3, 2011): 673–702. http://dx.doi.org/10.1017/s0266466610000472.

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This paper considers dynamic time series binary choice models. It proves near epoch dependence and strong mixing for the dynamic binary choice model with correlated errors. Using this result, it shows in a time series setting the validity of the dynamic probit likelihood procedure when lags of the dependent binary variable are used as regressors, and it establishes the asymptotic validity of Horowitz’s smoothed maximum score estimation of dynamic binary choice models with lags of the dependent variable as regressors. For the semiparametric model, the latent error is explicitly allowed to be correlated. It turns out that no long-run variance estimator is needed for the validity of the smoothed maximum score procedure in the dynamic time series framework.
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Yanuar, Ferra, Rahmatika Fajriyah, and Dodi Devianto. "SMALL AREA ESTIMATION METHOD WITH EMPIRICAL BAYES BASED ON BETA BINOMIAL MODEL IN GENERATED DATA." MEDIA STATISTIKA 14, no. 1 (October 21, 2020): 1–9. http://dx.doi.org/10.14710/medstat.14.1.1-9.

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Small Area Estimation is one of the methods that can be used to estimate parameters in an area that has a small population. This study aims to estimate the value of the binary data parameter using the direct estimation method and an indirect estimation method by using the Empirical Bayes approach. To illustrate the method, we consider three conditions: direct estimator, empirical Bayes (EB) with auxiliary variables, and empirical Bayes without auxiliary variables. The smaller value of Mean Square Error is used to determine the better method. The results showed that the indirect estimation methods (EB method) gave the parameter value that was not much different from the direct estimation value. Then, the MSE values of indirect estimation with an auxiliary variable are smaller than the direct estimation method.
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Chen, Songnian, and Lung-Fei Lee. "EFFICIENT SEMIPARAMETRIC SCORING ESTIMATION OF SAMPLE SELECTION MODELS." Econometric Theory 14, no. 4 (August 1998): 423–62. http://dx.doi.org/10.1017/s026646669814402x.

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A semiparametric likelihood method is proposed for the estimation of sample selection models. The method is a two-step semiparametric scoring estimation procedure based on an index restriction and kernel estimation. Under some regularity conditions, the estimator is square-root n-consistent and asymptotically normal. The estimator is also asymptotically efficient in the sense that its asymptotic covariance matrix attains the semiparametric efficiency bound under the index restriction. For the binary choice sample selection model, it also attains the efficiency bound under the independence assumption. This method can be applied to the estimation of general sample selection models.
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Joebaedi, Khafsah, Iin Irianingsih, Badrulfalah Badrulfalah, Dwi Susanti, and Kankan Parmikanti. "Parameter Estimation STAR (1;1) Model Using Binary Weight." Eksakta : Berkala Ilmiah Bidang MIPA 20, no. 2 (August 31, 2019): 33–41. http://dx.doi.org/10.24036/eksakta/vol20-iss2/199.

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Space Time Auto Regressive(1;1) Model or STAR(1;1) model is a form of model that involves location and time. The STAR(1;1) model is a stationary space time model in mean and variance. The STAR model can be used to forecast future observations at these locations by involving the effects of observations at other nearby locations in spatial lag 1 and lag time 1 [2]. The STAR model can be written as a linear model assuming that error is normally distributed with zero mean and constant variance. In this research, the parameter estimation procedure for STAR model using binary weight, MKT method and STAR model for the estimation of petroleum production in 3 wells is assumed to be in a homogeneous reservoir.
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Benson, David, Matthew A. Masten, and Alexander Torgovitsky. "ivcrc: An instrumental-variables estimator for the correlated random-coefficients model." Stata Journal: Promoting communications on statistics and Stata 22, no. 3 (September 2022): 469–95. http://dx.doi.org/10.1177/1536867x221124449.

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We discuss the ivcrc command, which implements an instrumental-variables (IV) estimator for the linear correlated random-coefficients model. The correlated random-coefficients model is a natural generalization of the standard linear IV model that allows for endogenous, multivalued treatments and unobserved heterogeneity in treatment effects. The estimator implemented by ivcrc uses recent semiparametric identification results that allow for flexible functional forms and permit instruments that may be binary, discrete, or continuous. The ivcrc command also allows for the estimation of varying-coefficient regressions, which are closely related in structure to the proposed IV estimator. We illustrate the use of ivcrc by estimating the returns to education in the National Longitudinal Survey of Young Men.
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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.

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Cette thèse s'intéresse à la résolution de problèmes inverses non linéaires exploitant un a priori de parcimonie ; plus particulièrement, des problèmes où les données se modélisent comme la combinaison linéaire d'un faible nombre de fonctions non linéaires en un paramètre dit de " localisation " (par exemple la fréquence en analyse spectrale ou le décalage temporel en déconvolution impulsionnelle). Ces problèmes se reformulent classiquement en un problème d'approximation parcimonieuse linéaire (APL) en évaluant les fonctions non linéaires sur une grille de discrétisation arbitrairement fine du paramètre de localisation, formant ainsi un " dictionnaire discret ". Cependant, une telle approche se heurte à deux difficultés majeures. D'une part, le dictionnaire provenant d'une telle discrétisation est fortement corrélé et met en échec les méthodes de résolution sous-optimales classiques comme la pénalisation L1 ou les algorithmes gloutons. D'autre part, l'estimation du paramètre de localisation, appartenant nécessairement à la grille de discrétisation, se fait de manière discrète, ce qui entraîne une erreur de modélisation. Dans ce travail nous proposons des solutions pour faire face à ces deux enjeux, d'une part via la prise en compte de la parcimonie de façon exacte en introduisant un ensemble de variables binaires, et d'autre part via la résolution " optimale " de tels problèmes sur " dictionnaire continu " permettant l'estimation continue du paramètre de localisation. Deux axes de recherches ont été suivis, et l'utilisation des algorithmes proposés est illustrée sur des problèmes de type déconvolution impulsionnelle et analyse spectrale de signaux irrégulièrement échantillonnés. Le premier axe de ce travail exploite le principe " d'interpolation de dictionnaire ", consistant en une linéarisation du dictionnaire continu pour obtenir un problème d'APL sous contraintes. L'introduction des variables binaires nous permet de reformuler ce problème sous forme de " programmation mixte en nombres entiers " (Mixed Integer Programming - MIP) et ainsi de modéliser de façon exacte la parcimonie sous la forme de la " pseudo-norme L0 ". Différents types d'interpolation de dictionnaires et de relaxation des contraintes sont étudiés afin de résoudre de façon optimale le problème grâce à des algorithmes classiques de type MIP. Le second axe se place dans le cadre probabiliste Bayésien, où les variables binaires nous permettent de modéliser la parcimonie en exploitant un modèle de type Bernoulli-Gaussien. Ce modèle est étendu (modèle BGE) pour la prise en compte de la variable de localisation continue. L'estimation des paramètres est alors effectuée à partir d'échantillons tirés avec des algorithmes de type Monte Carlo par Chaîne de Markov. Plus précisément, nous montrons que la marginalisation des amplitudes permet une accélération de l'algorithme de Gibbs dans le cas supervisé (hyperparamètres du modèle connu). De plus, nous proposons de bénéficier d'une telle marginalisation dans le cas non supervisé via une approche de type " Partially Collapsed Gibbs Sampler. " Enfin, nous avons adapté le modèle BGE et les algorithmes associés à un problème d'actualité en astrophysique : la détection d'exoplanètes par la méthode des vitesses radiales. Son efficacité sera illustrée sur des données simulées ainsi que sur des données réelles
This 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
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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.

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Filippou, Panagiota. "Penalized likelihood estimation of trivariate additive binary models." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10042688/.

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In many empirical situations, modelling simultaneously three or more outcomes as well as their dependence structure can be of considerable relevance. Trivariate modelling is continually gaining in popularity (e.g., Genest et al., 2013; Król et al., 2016; Zhong et al., 2012) because of the appealing property to account for the endogeneity issue and non-random sample selection bias, two issues that commonly arise in empirical studies (e.g., Zhang et al., 2015; Radice et al., 2013; Marra et al., 2017; Bärnighausen et al., 2011). The applied and methodological interest in trivariate modelling motivates the current thesis and the aim is to develop and estimate a generalized trivariate binary regression model, which accounts for several types of covariate effects (such as linear, nonlinear, random and spatial effects), as well as error correlations. In particular, the thesis focuses on the following targets. First, we address the issue in estimating accurately the correlation coefficients, which characterize the dependence of the binary responses conditional on regressors. We found that this is not an unusual occurrence for trivariate binary models and as far as we know such a limitation is neither discussed nor dealt with. Based on this framework, we develop models for dealing with data suffering from endogeneity and/or nonrandom sample selection. Moreover, we propose trivariate Gaussian copula models where the link functions can in principle be derived from any parametric distribution and the parameters describing the association between the responses can be made dependent on several types of covariate effects. All the coefficients of the model are estimated simultaneously within a penalized likelihood framework based on a carefully structured trust region algorithm with integrated automatic multiple smoothing parameter selection. The developments have been incorporated in the function SemiParTRIV()/gjrm() in the R package GJRM (Marra & Radice, 2017). The extensive use of simulated data as well as real datasets illustrates each development in detail and completes the analysis.
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Tzamourani, 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/.

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This thesis studies the one-factor latent trait model for binary data. In examines the sensitivity of the model when the assumptions about the model are violated, it investigates the information about the prior distribution when the model is estimated semi-parametrically and it also examines the goodness-of-fit of the model using Monte-Carlo simulations. Latent trait models are applied to data arising from psychometric tests, ability tests or attitude surveys. The data are often contaminated by guessing, cheating, unwillingness to give the true answer or gross errors. To study the sensitivity of the model when the data are contaminated we derive the Influence Function of the parameters and the posterior means, a tool developed in the frame of robust statistics theory. We study the behaviour of the Influence Function for changes in the data and also the behaviour of the parameters and the posterior means when the data are artificially contaminated. We further derive the Influence Function of the parameters and the posterior means for changes in the prior distribution and study empirically the behaviour of the model when the prior is a mixture of distributions. Semiparametric estimation involves estimation of the prior together with the item parameters. A new algorithm for fully semiparametric estimation of the model is given. The bootstrap is then used to study the information on the latent distribution than can be extracted from the data when the model is estimated semiparametrically. The use of the usual goodness-of-fit statistics has been hampered for latent trait models because of the sparseness of the tables. We propose the use of Monte-Carlo simulations to derive the empirical distribution of the goodness-of-fit statistics and also the examination of the residuals as they may pinpoint to the sources of bad fit.
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Sepato, 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.

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The most common analysis used for binary data is generalised linear model (GLM) with either a binomial or bernoulli distribution using either a logit, probit, complementary log-log or other type of link functions. However, such analyses violate the independence assumption if the binary data are measured repeatedly over time at the same subject or site. Failure to take into account the correlation can lead to incorrect estimation of regression parameters and the estimates are less efficient, particularly when the correlations are large. Therefore, to obtain the most efficient estimates that are also unbiased the methods that incorporate correlations (McCullagh and Nelder, 1989) should be used. Two of the statistical methodologies that can be used to account for this correlation for the longitudinal data are the generalized linear mixed models (GLMMs) and generalized estimating equation (GEE). The GLMM method is based on extending the fixed effects GLM to include random effects and covariance patterns. Unlike the GLM and GLMM methods, the GEE method is based on the quasi-likelihood theory and no assumption is made about the distribution of response observations (Liang and Zeger, 1986). The main objective of the study is to investigate the statistical properties and limitations of these three approaches, i.e. GLM, GLMMs and GEE for analyzing longitudinal data through use of a binary data from an entomology study. The results reaffirms the point made by these authors that misspecification of working correlation in GEE approach would still give consistent regression parameter estimates. Further, the results of this study suggest that even with small correlation, ignoring a random effects in a binary model can lead to inconsistent estimation.
Dissertation (MSc)--University of Pretoria, 2014.
lk2014
Statistics
MSc
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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.

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Longitudinal data arise when subjects are followed over time. This type of data is typically dependent, due to including repeated observations and this type of dependence is termed as within-subject dependence. Often the scientific interest is on multiple longitudinal measurements which introduce two additional types of associations, between-response and cross-response temporal dependencies. Only the statistical methods which take these association structures might yield reliable and valid statistical inferences. Although the methods for univariate longitudinal data have been mostly studied, multivariate longitudinal data still needs more work. In this thesis, although we mainly focus on multivariate longitudinal binary data models, we also consider other types of response families when necessary. We extend a work on multivariate marginal models, namely multivariate marginal models with response specific parameters (MMM1), and propose multivariate marginal models with shared regression parameters (MMM2). Both of these models are generalized estimating equation (GEE) based, and are valid for several response families such as Binomial, Gaussian, Poisson, and Gamma. Two different R packages, mmm and mmm2 are proposed to fit them, respectively. We further develop a marginalized multilevel model, namely probit normal marginalized transition random effects models (PNMTREM) for multivariate longitudinal binary response. By this model, implicit function theorem is introduced to explicitly link the levels of marginalized multilevel models with transition structures for the first time. An R package, bf pnmtrem is proposed to fit the model. PNMTREM is applied to data collected through Iowa Youth and Families Project (IYFP). Five different models, including univariate and multivariate ones, are considered to forecast multivariate longitudinal binary data. A comparative simulation study, which includes a model-independent data simulation process, is considered for this purpose. Forecasting independent variables are taken into account as well. To assess the forecasts, several accuracy measures, such as expected proportion of correct prediction (ePCP), area under the receiver operating characteristic (AUROC) curve, mean absolute scaled error (MASE) are considered. Mother'
s 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.
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Polcer, James. "Generalized Bathtub Hazard Models for Binary-Transformed Climate Data." TopSCHOLAR®, 2011. http://digitalcommons.wku.edu/theses/1060.

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In this study, we use a hazard-based modeling as an alternative statistical framework to time series methods as applied to climate data. Data collected from the Kentucky Mesonet will be used to study the distributional properties of the duration of high and low-energy wind events relative to an arbitrary threshold. Our objectiveswere to fit bathtub models proposed in literature, propose a generalized bathtub model, apply these models to Kentucky Mesonet data, and make recommendations as to feasibility of wind power generation. Using two different thresholds (1.8 and 10 mph respectively), results show that the Hjorth bathtub model consistently performed better than all other models considered with coefficient of R-squared values at 0.95 or higher. However, fewer sites and months could be included in the analysis when we increased our threshold to 10 mph. Based on a 10 mph threshold, Bowling Green (FARM), Hopkinsville (PGHL), and Columbia (CMBA) posted the top 3 wind duration times in February of 2009. Further studies needed to establish long-term trends.
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Schildcrout, 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.

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Ahmed, Mohamed Salem. "Contribution à la statistique spatiale et l'analyse de données fonctionnelles." Thesis, Lille 3, 2017. http://www.theses.fr/2017LIL30047/document.

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Ce mémoire de thèse porte sur la statistique inférentielle des données spatiales et/ou fonctionnelles. En effet, nous nous sommes intéressés à l’estimation de paramètres inconnus de certains modèles à partir d’échantillons obtenus par un processus d’échantillonnage aléatoire ou non (stratifié), composés de variables indépendantes ou spatialement dépendantes.La spécificité des méthodes proposées réside dans le fait qu’elles tiennent compte de la nature de l’échantillon étudié (échantillon stratifié ou composé de données spatiales dépendantes).Tout d’abord, nous étudions des données à valeurs dans un espace de dimension infinie ou dites ”données fonctionnelles”. Dans un premier temps, nous étudions les modèles de choix binaires fonctionnels dans un contexte d’échantillonnage par stratification endogène (échantillonnage Cas-Témoin ou échantillonnage basé sur le choix). La spécificité de cette étude réside sur le fait que la méthode proposée prend en considération le schéma d’échantillonnage. Nous décrivons une fonction de vraisemblance conditionnelle sous l’échantillonnage considérée et une stratégie de réduction de dimension afin d’introduire une estimation du modèle par vraisemblance conditionnelle. Nous étudions les propriétés asymptotiques des estimateurs proposées ainsi que leurs applications à des données simulées et réelles. Nous nous sommes ensuite intéressés à un modèle linéaire fonctionnel spatial auto-régressif. La particularité du modèle réside dans la nature fonctionnelle de la variable explicative et la structure de la dépendance spatiale des variables de l’échantillon considéré. La procédure d’estimation que nous proposons consiste à réduire la dimension infinie de la variable explicative fonctionnelle et à maximiser une quasi-vraisemblance associée au modèle. Nous établissons la consistance, la normalité asymptotique et les performances numériques des estimateurs proposés.Dans la deuxième partie du mémoire, nous abordons des problèmes de régression et prédiction de variables dépendantes à valeurs réelles. Nous commençons par généraliser la méthode de k-plus proches voisins (k-nearest neighbors; k-NN) afin de prédire un processus spatial en des sites non-observés, en présence de co-variables spatiaux. La spécificité du prédicteur proposé est qu’il tient compte d’une hétérogénéité au niveau de la co-variable utilisée. Nous établissons la convergence presque complète avec vitesse du prédicteur et donnons des résultats numériques à l’aide de données simulées et environnementales.Nous généralisons ensuite le modèle probit partiellement linéaire pour données indépendantes à des données spatiales. Nous utilisons un processus spatial linéaire pour modéliser les perturbations du processus considéré, permettant ainsi plus de flexibilité et d’englober plusieurs types de dépendances spatiales. Nous proposons une approche d’estimation semi paramétrique basée sur une vraisemblance pondérée et la méthode des moments généralisées et en étudions les propriétés asymptotiques et performances numériques. Une étude sur la détection des facteurs de risque de cancer VADS (voies aéro-digestives supérieures)dans la région Nord de France à l’aide de modèles spatiaux à choix binaire termine notre contribution
This 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
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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.

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With the recent innovations in technology, sports betting became more accessible to any bettor, professional or not. An analysis of tennis and models applicable on the estimation of the result of men’s tennis matches in Grand Slam tournaments allowed us to identify a model with the capacity to predict the result with a 76,02% accuracy. The selected model was applied on a case study, using Betfair as an example of an ‘exchange’ platform. This approach allows us to compare the estimated odds and the odds present at the betting market in such a way that the predictive ability of the model is assessed. Further developments are suggested in the conclusion; Resumo: Negociação interativa pré-jogo no mercado de apostas de ténis: probabilidade de ganhar um jogo em torneios do Grand Slam Com os mais recentes avanços tecnológicos, a aposta desportiva tornou-se acessível para qualquer tipo de apostador, quer amador, quer profissional. Uma análise ao caso específico do ténis, baseada na aplicação de modelos para resposta binária ao resultado de um jogo de ténis masculino durante o torneio do Grand Slam, permitiu-nos identificar um modelo com a capacidade de prever o resultado para 76,02% dos jogos. O modelo seleccionado foi aplicado num estudo de caso, usando Betfair como exemplo de uma plataforma de apostas. O modelo permite-nos comparar as probabilidades estimadas e as probabilidades existentes no mercado de apostas, e identificar se a previsão do resultado de um determinado jogo vai ao encontro das expectativas do mercado. Desenvolvimentos adicionais são sugeridos na conclusão.
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Books on the topic "Binary model estimation"

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Tsai, Wei-Der. Hospital affiliation and the proportion of patient discharges to long-term care--best probit predictor estimation of the binary response model with an endogeneous treatment effect. Nankang, Taipei, Taiwan, Republic of China: Institute of Economics, Academia Sinica, 1996.

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Franzese, Robert J., and Jude C. Hays. Empirical Models of Spatial Inter‐Dependence. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0025.

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This article discusses the role of ‘spatial interdependence’ between units of analysis by using a symmetric weighting matrix for the units of observation whose elements reflect the relative connectivity between unit i and unit j. It starts by addressing spatial interdependence in political science. There are two workhorse regression models in empirical spatial analysis: spatial lag and spatial error models. The article then addresses OLS estimation and specification testing under the null hypothesis of no spatial dependence. It turns to the topic of assessing spatial lag models, and a discussion of spatial error models. Moreover, it reports the calculation of spatial multipliers. Furthermore, it presents several newer applications of spatial techniques in empirical political science research: SAR models with multiple lags, SAR models for binary dependent variables, and spatio-temporal autoregressive (STAR) models for panel data.
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Book chapters on the topic "Binary model estimation"

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Sarrafi, Aral, and Zhu Mao. "Using 2D Phase-Based Motion Estimation and Video Magnification for Binary Damage Identification on a Wind Turbine Blade." In Model Validation and Uncertainty Quantification, Volume 3, 145–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74793-4_19.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Overfitting, Model Tuning, and Evaluation of Prediction Performance." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 109–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_4.

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AbstractThe overfitting phenomenon happens when a statistical machine learning model learns very well about the noise as well as the signal that is present in the training data. On the other hand, an underfitted phenomenon occurs when only a few predictors are included in the statistical machine learning model that represents the complete structure of the data pattern poorly. This problem also arises when the training data set is too small and thus an underfitted model does a poor job of fitting the training data and unsatisfactorily predicts new data points. This chapter describes the importance of the trade-off between prediction accuracy and model interpretability, as well as the difference between explanatory and predictive modeling: Explanatory modeling minimizes bias, whereas predictive modeling seeks to minimize the combination of bias and estimation variance. We assess the importance and different methods of cross-validation as well as the importance and strategies of tuning that are key to the successful use of some statistical machine learning methods. We explain the most important metrics for evaluating the prediction performance for continuous, binary, categorical, and count response variables.
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Mallick, Taslim S., Patrick J. Farrell, and Brajendra C. Sutradhar. "Consistent Estimation in Incomplete Longitudinal Binary Models." In ISS-2012 Proceedings Volume On Longitudinal Data Analysis Subject to Measurement Errors, Missing Values, and/or Outliers, 117–38. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6871-4_6.

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Knuiman, M. W., and N. M. Laird. "Parameter Estimation in Variance Component Models for Binary Response Data." In Advances in Statistical Methods for Genetic Improvement of Livestock, 177–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74487-7_9.

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Ahonen, Timo, and Matti Pietikäinen. "Pixelwise Local Binary Pattern Models of Faces Using Kernel Density Estimation." In Advances in Biometrics, 52–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01793-3_6.

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Tanaka, Hisatoshi. "Local consistency of the iterative least-squares estimator for the semiparametric binary choice model." In Advances in Mathematical Economics, 139–61. Tokyo: Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54324-4_5.

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Laisney, François, Michael Lechner, and Winfried Pohlmeier. "Semi-Nonparametric Estimation of Binary Choice Models Using Panel Data: An Application to the Innovative Activity of German Firms." In Output and Employment Fluctuations, 87–101. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57989-9_7.

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Fuentes-Fino, Ricardo Javier, Saúl Calderón-Ramírez, Enrique Domínguez, Ezequiel López-Rubio, Marco A. Hernandez-Vasquez, and Miguel A. Molina-Cabello. "Feature Density as an Uncertainty Estimator Method in the Binary Classification Mammography Images Task for a Supervised Deep Learning Model." In Bioinformatics and Biomedical Engineering, 375–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07802-6_32.

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Carlin, John B., Rory Wolfe, Carolyn Coffey, and George C. Patton. "Survival Models: Analysis of Binary Outcomes in Longitudinal Studies Using Weighted Estimating Equations and Discrete-Time Survival Methods: Prevalence and Incidence of Smoking in An Adolescent Cohort." In Tutorials in Biostatistics, 161–85. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470023678.ch1d(ii).

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"Maximum likelihood estimation of the binary logit model." In Logit Models from Economics and Other Fields, 33–55. Cambridge University Press, 2003. http://dx.doi.org/10.1017/cbo9780511615412.004.

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Conference papers on the topic "Binary model estimation"

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Cui, Jing, Shanshe Wang, Nan Zhang, and Siwei Ma. "An optimized probability estimation model for binary arithmetic coding." In 2015 Visual Communications and Image Processing (VCIP). IEEE, 2015. http://dx.doi.org/10.1109/vcip.2015.7457823.

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Liu, Zhenyu, Sanchuan Guo, and Dongsheng Wang. "Binary classification based linear rate estimation model for HEVC RDO." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025746.

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Guo, Sanchuan, Zhenyu Liu, Dongsheng Wang, Qingrui Han, and Yang Song. "Linear Rate Estimation Model for HEVC RDO Using Binary Classification Based Regression." In 2014 Data Compression Conference (DCC). IEEE, 2014. http://dx.doi.org/10.1109/dcc.2014.17.

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Escoto, Esau Figueroa, and Fabio Bertequini Leao. "A nonlinear binary programming model for fault section estimation in electric power systems." In 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 2017. http://dx.doi.org/10.1109/ropec.2017.8261625.

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Escoto, Esau Figueroa, and Fabio Bertequini Leao. "A Binary Integer Linear Programming Model for Fault Section Estimation in Electric Power Systems." In 2019 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE, 2019. http://dx.doi.org/10.1109/ropec48299.2019.9057099.

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Luo, Hengheng, Yabin Zhang, Suyun Zhao, Hong Chen, and Cuiping Li. "Exploring Binary Classification Hidden within Partial Label Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/456.

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Partial label learning (PLL) is to learn a discriminative model under incomplete supervision, where each instance is annotated with a candidate label set. The basic principle of PLL is that the unknown correct label y of an instance x resides in its candidate label set s, i.e., P(y ∈ s | x) = 1. On which basis, current researches either directly model P(x | y) under different data generation assumptions or propose various surrogate multiclass losses, which all aim to encourage the model-based Pθ(y ∈ s | x)→1 implicitly. In this work, instead, we explicitly construct a binary classification task toward P(y ∈ s | x) based on the discriminative model, that is to predict whether the model-output label of x is one of its candidate labels. We formulate a novel risk estimator with estimation error bound for the proposed PLL binary classification risk. By applying logit adjustment based on disambiguation strategy, the practical approach directly maximizes Pθ(y ∈ s | x) while implicitly disambiguating the correct one from candidate labels simultaneously. Thorough experiments validate that the proposed approach achieves competitive performance against the state-of-the-art PLL methods.
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Jin, Yanhan, Yuexian Zou, and C. H. Ritz. "Robust speaker DOA estimation based on the inter-sensor data ratio model and binary mask estimation in the bispectrum domain." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952760.

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He, Zhengyou, Hsiao-Dong Chiang, Chaowen Li, and Qingfeng Zeng. "Fault-section estimation in power systems based on improved optimization model and binary particle swarm optimization." In Energy Society General Meeting (PES). IEEE, 2009. http://dx.doi.org/10.1109/pes.2009.5275866.

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Windarto, S. W. Indratno, N. Nuraini, and E. Soewono. "A comparison of binary and continuous genetic algorithm in parameter estimation of a logistic growth model." In SYMPOSIUM ON BIOMATHEMATICS (SYMOMATH 2013). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4866550.

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Chowdhury, Souma, Ali Mehmani, and Achille Messac. "Concurrent Surrogate Model Selection (COSMOS) Based on Predictive Estimation of Model Fidelity." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35358.

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One of the primary drawbacks plaguing wider acceptance of surrogate models is their low fidelity in general. This issue can be in a large part attributed to the lack of automated model selection techniques, particularly ones that do not make limiting assumptions regarding the choice of model types and kernel types. A novel model selection technique was recently developed to perform optimal model search concurrently at three levels: (i) optimal model type (e.g., RBF), (ii) optimal kernel type (e.g., multiquadric), and (iii) optimal values of hyper-parameters (e.g., shape parameter) that are conventionally kept constant. The error measures to be minimized in this optimal model selection process are determined by the Predictive Estimation of Model Fidelity (PEMF) method, which has been shown to be significantly more accurate than typical cross-validation-based error metrics. In this paper, we make the following important advancements to the PEMF-based model selection framework, now called the Concurrent Surrogate Model Selection or COSMOS framework: (i) The optimization formulation is modified through binary coding to allow surrogates with differing numbers of candidate kernels and kernels with differing numbers of hyper-parameters (which was previously not allowed). (ii) A robustness criterion, based on the variance of errors, is added to the existing criteria for model selection. (iii) A larger candidate pool of 16 surrogate-kernel combinations is considered for selection — possibly making COSMOS one of the most comprehensive surrogate model selection framework (in theory and implementation) currently available. The effectiveness of the COSMOS framework is demonstrated by successfully applying it to four benchmark problems (with 2–30 variables) and an airfoil design problem. The optimal model selection results illustrate how diverse models provide important tradeoffs for different problems.
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Reports on the topic "Binary model estimation"

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Hoderlein, Stefan, and Robert Sherman. Identification and estimation in a correlated random coefficients binary response model. Institute for Fiscal Studies, December 2012. http://dx.doi.org/10.1920/wp.cem.2012.4212.

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Lee, Sokbae (Simon), Le-Yu Chen, and Myung Jae Sung. Maximum score estimation of preference parameters for a binary choice model under uncertainty. Cemmap, April 2013. http://dx.doi.org/10.1920/wp.cem.2013.1413.

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Lubowa, Nasser, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Pharmaceutical Industry in Uganda: A Review of the Common GMP Non-conformances during Regulatory Inspections. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317442.

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The prevalence of substandard medicines in Africa is high but not well documented. Low and Middle-Income Countries (LMICs) are likely to face considerable challenges with substandard medications. Africa faces inadequate drug regulatory practices, and in general, compliance with Good Manufacturing Practices (GMP) in most of the pharmaceutical industries is lacking. The majority of pharmaceutical manufacturers in developing countries are often overwhelmed by the GMP requirements and therefore are unable to operate in line with internationally acceptable standards. Non-conformances observed during regulatory inspections provide the status of the compliance to GMP requirements. The study aimed to identify the GMP non-conformances during regulatory inspections and gaps in the production of pharmaceuticals locally manufactured in Uganda by review of the available 50 GMP reports of 21 local pharmaceutical companies in Uganda from 2016. The binary logistic generalized estimating equations (GEE) model was applied to estimate the association between odds of a company failing to comply with the GMP requirements and non-conformances under each GMP inspection parameter. Analysis using dummy estimation to linear regression included determination of the relationship that existed between the selected variables (GMP inspection parameters) and the production capacity of the local pharmaceutical industry. Oral liquids, external liquid preparations, powders, creams, and ointments were the main categories of products manufactured locally. The results indicated that 86% of the non-conformances were major, 11% were minor, and 3% critical. The majority of the non-conformances were related to production (30.1%), documentation (24.5%), and quality control (17.6%). Regression results indicated that for every non-conformance under premises, equipment, and utilities, there was a 7-fold likelihood of the manufacturer failing to comply with the GMP standards (aOR=6.81, P=0.001). The results showed that major non-conformances were significantly higher in industries of small scale (B=6.77, P=0.02) and medium scale (B=8.40, P=0.04), as compared to those of large scale. This study highlights the failures in quality assurance systems and stagnated GMP improvements in these industries that need to be addressed by the manufacturers with support from the regulator. The addition of risk assessment to critical production and quality control operations and establishment of appropriate corrective and preventive actions as part of quality management systems are required to ensure that quality pharmaceuticals are manufactured locally.
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