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Dissertations / Theses on the topic 'Finite mixture of quantile regression'

Author: Grafiati

Published: 22 February 2023

Last updated: 19 July 2025

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1

Sánchez, Luis Enrique Benites. "Finite mixture of regression models." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-10052018-131627/.

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This dissertation consists of three articles, proposing extensions of finite mixtures in regression models. Here we consider a flexible class of both univariate and multivariate distributions, which allow adequate modeling of asymmetric data that have multimodality, heavy tails and outlying observations. This class has special cases such as skew-normal, skew-t, skew-slash and skew normal contaminated distributions, as well as symmetric cases. Initially, a model is proposed based on the assumption that the errors follow a finite mixture of scale mixture of skew-normal (FM-SMSN) distribution rat

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2

Qarmalah, Najla Mohammed A. "Finite mixture models : visualisation, localised regression, and prediction." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12486/.

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Initially, this thesis introduces a new graphical tool, that can be used to summarise data possessing a mixture structure. Computation of the required summary statistics makes use of posterior probabilities of class membership obtained from a fitted mixture model. In this context, both real and simulated data are used to highlight the usefulness of the tool for the visualisation of mixture data in comparison to the use of a traditional boxplot. This thesis uses localised mixture models to produce predictions from time series data. Estimation method used in these models is achieved using a kern

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3

Zhang, Dengfeng. "Latent Class Model in Transportation Study." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/51203.

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Statistics, as a critical component in transportation research, has been widely used to analyze driver safety, travel time, traffic flow and numerous other problems. Many of these popular topics can be interpreted as to establish the statistical models for the latent structure of data. Over the past several years, the interest in latent class models has continuously increased due to their great potential in solving practical problems. In this dissertation, I developed several latent class models to quantitatively analyze the hidden structure of transportation data and addressed related applica

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4

Li, Xiongya. "Robust multivariate mixture regression models." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38427.

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Doctor of Philosophy<br>Department of Statistics<br>Weixing Song<br>In this dissertation, we proposed a new robust estimation procedure for two multivariate mixture regression models and applied this novel method to functional mapping of dynamic traits. In the first part, a robust estimation procedure for the mixture of classical multivariate linear regression models is discussed by assuming that the error terms follow a multivariate Laplace distribution. An EM algorithm is developed based on the fact that the multivariate Laplace distribution is a scale mixture of the multivariate standard no

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5

Schoen, Stephanie. "Individual and Cumulative Effects of a Mixture of Phthalates and Children's Intellectual Abilities: A Secondary Analysis of Data from the MIREC Study." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42674.

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Phthalates, chemicals found in a variety of consumer goods and personal care products, may adversely affect fetal neurodevelopment. Women are exposed to a mixture of phthalates during pregnancy because of the common presence of these chemicals in consumer goods. The aim of this study is to investigate potential associations between phthalate exposure during the first trimester of gestation and Intelligence Quotient (IQ) scores of 3-year old children.

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6

Leisch, Friedrich. "FlexMix: A general framework for finite mixture models and latent class regression in R." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/712/1/document.pdf.

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Flexmix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment: three variants of the EM algorithm can be used for parameter estimation, regressors and responses may be multivariate with arbitrary dimension, data may be grouped, e.g., to account for multiple observations per individual, the usual formula interface of the S language is used for convenient model specification, and a modular concept of driver functions allows to interface many di_erent types of regression models. Existing drivers implement mixtures of standard

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7

Xu, Li. "Statistical Methods for Variability Management in High-Performance Computing." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104184.

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High-performance computing (HPC) variability management is an important topic in computer science. Research topics include experimental designs for efficient data collection, surrogate models for predicting the performance variability, and system configuration optimization. Due to the complex architecture of HPC systems, a comprehensive study of HPC variability needs large-scale datasets, and experimental design techniques are useful for improved data collection. Surrogate models are essential to understand the variability as a function of system parameters, which can be obtained by mathematic

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8

Gogonel, Adriana Geanina. "Statistical Post-Processing Methods And Their Implementation On The Ensemble Prediction Systems For Forecasting Temperature In The Use Of The French Electric Consumption." Phd thesis, Université René Descartes - Paris V, 2012. http://tel.archives-ouvertes.fr/tel-00798576.

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The thesis has for objective to study new statistical methods to correct temperature predictionsthat may be implemented on the ensemble prediction system (EPS) of Meteo France so toimprove its use for the electric system management, at EDF France. The EPS of Meteo Francewe are working on contains 51 members (forecasts by time-step) and gives the temperaturepredictions for 14 days. The thesis contains three parts: in the first one we present the EPSand we implement two statistical methods improving the accuracy or the spread of the EPS andwe introduce criteria for comparing results. In the seco

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9

Falk, Matthew Gregory. "Incorporating uncertainty in environmental models informed by imagery." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/33235/1/Matthew_Falk_Thesis.pdf.

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In this thesis, the issue of incorporating uncertainty for environmental modelling informed by imagery is explored by considering uncertainty in deterministic modelling, measurement uncertainty and uncertainty in image composition. Incorporating uncertainty in deterministic modelling is extended for use with imagery using the Bayesian melding approach. In the application presented, slope steepness is shown to be the main contributor to total uncertainty in the Revised Universal Soil Loss Equation. A spatial sampling procedure is also proposed to assist in implementing Bayesian melding given th

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10

SABBI, ALBERTO. "Mixed effect quantile and M-quantile regression for spatial data." Doctoral thesis, 2020. http://hdl.handle.net/11573/1456341.

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Observed data are frequently characterized by a spatial dependence; that is the observed values can be influenced by the "geographical" position. In such a context it is possible to assume that the values observed in a given area are similar to those recorded in neighboring areas. Such data is frequently referred to as spatial data and they are frequently met in epidemiological, environmental and social studies, for a discussion see Haining, (1990). Spatial data can be multilevel, with samples being composed of lower level units (population, buildings) nested within higher level units (census

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11

Lin, Wei-Te, and 林唯德. "Variable selection on the mixture of additive quantile regression models." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nma266.

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碩士<br>國立東華大學<br>應用數學系<br>106<br>When observations come from the mixture of additive quantile regression models, some unreasonable results of variable selection could happen if the existing quantile approaches are applied directly. In this work, we attempt to develop an algorithm to cluster data, select relevant variables, and identify the related structures simultaneously. In the proposed algorithm, B-spline function is utilized to approximate the additive model and the quantile regression with the Lasso-type penalty is employed for the variable selection and structure detection. The performa

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12

Huang, Mian Li Runze. "Nonparametric techniques in finite mixture of regression models." 2009. http://etda.libraries.psu.edu/theses/approved/PSUonlyIndex/ETD-4191/index.html.

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13

Li, Na. "Essays on Trade Agreements, Agricultural Commodity Prices and Unconditional Quantile Regression." Thesis, 2013. http://hdl.handle.net/10214/7741.

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My dissertation consists of three essays in three different areas: international trade; agricultural markets; and nonparametric econometrics. The first and third essays are theoretical papers, while the second essay is empirical. In the first essay, I developed a political economy model of trade agreements where the set of policy instruments are endogenously determined, providing a rationale for countervailing duties (CVDs). Trade-related policy intervention is assumed to be largely shaped in response to rent seeking demand as is often shown empirically. Consequently, the uncertain circumstanc

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14

Park, Byung Jung. "Application of Finite Mixture Models for Vehicle Crash Data Analysis." 2010. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-7667.

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Developing sound or reliable statistical models for analyzing vehicle crashes is very important in highway safety studies. A difficulty arises when crash data exhibit overdispersion. Over-dispersion caused by unobserved heterogeneity is a serious problem and has been addressed in a variety ways within the negative binomial (NB) modeling framework. However, the true factors that affect heterogeneity are often unknown to researchers, and failure to accommodate such heterogeneity in the model can undermine the validity of the empirical results. Given the limitations of the NB regression model for

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15

YuanFeng and 馮元. "Robust Bayesian Variable Selection in Finite Mixture Regression Model with an Application to Financial Crisis Data." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/00801592208583022173.

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碩士<br>國立成功大學<br>統計學系<br>103<br>A Bayesian variable selection approach for finite mixture regression model is proposed,which is able to simultaneously accommodate model uncertainty, population heterogeneity and outlier effect. Variable selection is mainly accomplished through the idea of data augmentation and special spike and slab prior specification, and model inference is based on MCMC output. The proposed method is further applied to analyze the global financial crises data. Under two-subpopulation setting, some important covariates for each group are found, as well as several countries tha

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16

Zhu, Jiachi. "Hedging the Return on Equity and Firm Profit: Evidence from Canadian Oil and Gas Companies." Thesis, 2012. http://hdl.handle.net/10222/15416.

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In this thesis, we analyse the relationship between the hedging activities and return on equity, and the relationship between profit on hedging and other factors. Fully conditional specification is used to impute the missing values. Instrumental variable estimation and finite mixture of regression models are then used to predict the return on equity and hedging gain. We find the instrumental variable estimation is better than the OLS estimation to deal with the hedging data since it eliminates the endogeneity. By finite mixture of regression models, we show that different firms have different

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17

Pontes, Leandro Manuel Branco Pequito. "Novas empresas e criação de emprego: dois ensaios com modelos de mistura." Master's thesis, 2008. http://hdl.handle.net/10071/2626.

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JEL: C25, L26, M13, J23<br>As novas empresas e o sector das pequenas e médias empresas constituem vectores fundamentais para o desenvolvimento das economias ocidentais. Neste contexto, o empresário responsável pela constituição de uma nova empresa desempenha um papel importante, com impacto no desempenho da economia. Com base em dados recolhidos para a economia portuguesa sobre os empresários que constituíram empresas em 2002, procede-se à construção de uma tipologia de empresários baseada nas suas motivações. Os resultados indicam a existência de três segmentos, com motivações e perfis dist

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18

El-Khatib, Mayar. "Highway Development Decision-Making Under Uncertainty: Analysis, Critique and Advancement." Thesis, 2010. http://hdl.handle.net/10012/5741.

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While decision-making under uncertainty is a major universal problem, its implications in the field of transportation systems are especially enormous; where the benefits of right decisions are tremendous, the consequences of wrong ones are potentially disastrous. In the realm of highway systems, decisions related to the highway configuration (number of lanes, right of way, etc.) need to incorporate both the traffic demand and land price uncertainties. In the literature, these uncertainties have generally been modeled using the Geometric Brownian Motion (GBM) process, which has been used extens

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