Relevant bibliographies by topics / Heteroscedastic Multivariate Linear Regression

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Heteroscedastic Multivariate Linear Regression'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Heteroscedastic Multivariate Linear Regression"

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Zhang, X., C. E. Lee, and X. Shao. "Envelopes in multivariate regression models with nonlinearity and heteroscedasticity." Biometrika 107, no. 4 (June 17, 2020): 965–81. http://dx.doi.org/10.1093/biomet/asaa036.

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Summary Envelopes have been proposed in recent years as a nascent methodology for sufficient dimension reduction and efficient parameter estimation in multivariate linear models. We extend the classical definition of envelopes in Cook et al. (2010) to incorporate a nonlinear conditional mean function and a heteroscedastic error. Given any two random vectors ${X}\in\mathbb{R}^{p}$ and ${Y}\in\mathbb{R}^{r}$, we propose two new model-free envelopes, called the martingale difference divergence envelope and the central mean envelope, and study their relationships to the standard envelope in the context of response reduction in multivariate linear models. The martingale difference divergence envelope effectively captures the nonlinearity in the conditional mean without imposing any parametric structure or requiring any tuning in estimation. Heteroscedasticity, or nonconstant conditional covariance of ${Y}\mid{X}$, is further detected by the central mean envelope based on a slicing scheme for the data. We reveal the nested structure of different envelopes: (i) the central mean envelope contains the martingale difference divergence envelope, with equality when ${Y}\mid{X}$ has a constant conditional covariance; and (ii) the martingale difference divergence envelope contains the standard envelope, with equality when ${Y}\mid{X}$ has a linear conditional mean. We develop an estimation procedure that first obtains the martingale difference divergence envelope and then estimates the additional envelope components in the central mean envelope. We establish consistency in envelope estimation of the martingale difference divergence envelope and central mean envelope without stringent model assumptions. Simulations and real-data analysis demonstrate the advantages of the martingale difference divergence envelope and the central mean envelope over the standard envelope in dimension reduction.
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Shao, Jun, and J. N. K. Rao. "Jackknife inference for heteroscedastic linear regression models." Canadian Journal of Statistics 21, no. 4 (December 1993): 377–95. http://dx.doi.org/10.2307/3315702.

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Leslie, David S., Robert Kohn, and David J. Nott. "A general approach to heteroscedastic linear regression." Statistics and Computing 17, no. 2 (January 30, 2007): 131–46. http://dx.doi.org/10.1007/s11222-006-9013-8.

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Su, Li Yun, and Chun Hua Wang. "Two-Stage Local Polynomial Regression Method for Image Heteroscedastic Noise Removal." Advanced Materials Research 860-863 (December 2013): 2936–39. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2936.

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In this paper, we introduce the extension of local polynomial fitting to the linear heteroscedastic regression model and its applications in digital image heteroscedastic noise removal. For better image noise removal with heteroscedastic energy, firstly, the local polynomial regression is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. Due to non-parametric technique of local polynomial estimation, we do not need to know the heteroscedastic noise function. Therefore, we improve the estimation precision, when the heteroscedastic noise function is unknown. Numerical simulations results show that the proposed method can improve the image quality of heteroscedastic noise energy.
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Ounpraseuth, Songthip T., Phil D. Young, Johanna S. van Zyl, Tyler W. Nelson, and Dean M. Young. "Linear Dimension Reduction for Multiple Heteroscedastic Multivariate Normal Populations." Open Journal of Statistics 05, no. 04 (2015): 311–33. http://dx.doi.org/10.4236/ojs.2015.54033.

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Monteiro, Alessandra da Rocha Duailibe, Thiago de Sá Feital, and José Carlos Pinto. "A Numerical Procedure for Multivariate Calibration Using Heteroscedastic Principal Components Regression." Processes 9, no. 9 (September 21, 2021): 1686. http://dx.doi.org/10.3390/pr9091686.

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Many methods have been developed to allow for consideration of measurement errors during multivariate data analyses. The incorporation of the error structure into the analytical framework, usually described in terms of the covariance matrix of measurement errors, can provide better model estimation and prediction. However, little effort has been made to evaluate the effects of heteroscedastic measurement uncertainties on multivariate analyses when the covariance matrix of measurement errors changes with the measurement conditions. For this reason, the present work describes a new numerical procedure for analyses of heteroscedastic systems (heteroscedastic principal component regression or H-PCR) that takes into consideration the variations of the covariance matrix of measurement fluctuations. In order to illustrate the proposed approach, near infrared (NIR) spectra of xylene and toluene mixtures were measured at different temperatures and stirring velocities and the obtained data were used to build calibration models with different multivariate techniques, including H-PCR. Modeling of available xylene–toluene NIR data revealed that H-PCR can be used successfully for calibration purposes and that the principal directions obtained with the proposed approach can be quite different from the ones calculated through standard PCR, when heteroscedasticity is disregarded explicitly.
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Thinh, Raksmey, Klairung Samart, and Naratip Jansakul. "Linear regression models for heteroscedastic and non-normal data." ScienceAsia 46, no. 3 (2020): 353. http://dx.doi.org/10.2306/scienceasia1513-1874.2020.047.

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Gijbels, I., and I. Vrinssen. "Robust estimation and variable selection in heteroscedastic linear regression." Statistics 53, no. 3 (February 18, 2019): 489–532. http://dx.doi.org/10.1080/02331888.2019.1579215.

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Linke, Yu Yu. "Two-Step Estimation in a Heteroscedastic Linear Regression Model." Journal of Mathematical Sciences 231, no. 2 (April 27, 2018): 206–17. http://dx.doi.org/10.1007/s10958-018-3816-y.

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Faraway, Julian J., and Jiayang Sun. "Simultaneous Confidence Bands for Linear Regression with Heteroscedastic Errors." Journal of the American Statistical Association 90, no. 431 (September 1995): 1094–98. http://dx.doi.org/10.1080/01621459.1995.10476612.

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Dissertations / Theses on the topic "Heteroscedastic Multivariate Linear Regression"

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Kuljus, Kristi. "Rank Estimation in Elliptical Models : Estimation of Structured Rank Covariance Matrices and Asymptotics for Heteroscedastic Linear Regression." Doctoral thesis, Uppsala universitet, Matematisk statistik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9305.

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This thesis deals with univariate and multivariate rank methods in making statistical inference. It is assumed that the underlying distributions belong to the class of elliptical distributions. The class of elliptical distributions is an extension of the normal distribution and includes distributions with both lighter and heavier tails than the normal distribution. In the first part of the thesis the rank covariance matrices defined via the Oja median are considered. The Oja rank covariance matrix has two important properties: it is affine equivariant and it is proportional to the inverse of the regular covariance matrix. We employ these two properties to study the problem of estimating the rank covariance matrices when they have a certain structure. The second part, which is the main part of the thesis, is devoted to rank estimation in linear regression models with symmetric heteroscedastic errors. We are interested in asymptotic properties of rank estimates. Asymptotic uniform linearity of a linear rank statistic in the case of heteroscedastic variables is proved. The asymptotic uniform linearity property enables to study asymptotic behaviour of rank regression estimates and rank tests. Existing results are generalized and it is shown that the Jaeckel estimate is consistent and asymptotically normally distributed also for heteroscedastic symmetric errors.
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Bai, Xiuqin. "Robust mixtures of regression models." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18683.

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Doctor of Philosophy
Department of Statistics
Kun Chen and Weixin Yao
This proposal contains two projects that are related to robust mixture models. In the robust project, we propose a new robust mixture of regression models (Bai et al., 2012). The existing methods for tting mixture regression models assume a normal distribution for error and then estimate the regression param- eters by the maximum likelihood estimate (MLE). In this project, we demonstrate that the MLE, like the least squares estimate, is sensitive to outliers and heavy-tailed error distributions. We propose a robust estimation procedure and an EM-type algorithm to estimate the mixture regression models. Using a Monte Carlo simulation study, we demonstrate that the proposed new estimation method is robust and works much better than the MLE when there are outliers or the error distribution has heavy tails. In addition, the proposed robust method works comparably to the MLE when there are no outliers and the error is normal. In the second project, we propose a new robust mixture of linear mixed-effects models. The traditional mixture model with multiple linear mixed effects, assuming Gaussian distribution for random and error parts, is sensitive to outliers. We will propose a mixture of multiple linear mixed t-distributions to robustify the estimation procedure. An EM algorithm is provided to and the MLE under the assumption of t- distributions for error terms and random mixed effects. Furthermore, we propose to adaptively choose the degrees of freedom for the t-distribution using profile likelihood. In the simulation study, we demonstrate that our proposed model works comparably to the traditional estimation method when there are no outliers and the errors and random mixed effects are normally distributed, but works much better if there are outliers or the distributions of the errors and random mixed effects have heavy tails.
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Solomon, Mary Joanna. "Multivariate Analysis of Korean Pop Music Audio Features." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617105874719868.

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Zuber, Verena. "A Multivariate Framework for Variable Selection and Identification of Biomarkers in High-Dimensional Omics Data." Doctoral thesis, Universitätsbibliothek Leipzig, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-101223.

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In this thesis, we address the identification of biomarkers in high-dimensional omics data. The identification of valid biomarkers is especially relevant for personalized medicine that depends on accurate prediction rules. Moreover, biomarkers elucidate the provenance of disease, or molecular changes related to disease. From a statistical point of view the identification of biomarkers is best cast as variable selection. In particular, we refer to variables as the molecular attributes under investigation, e.g. genes, genetic variation, or metabolites; and we refer to observations as the specific samples whose attributes we investigate, e.g. patients and controls. Variable selection in high-dimensional omics data is a complicated challenge due to the characteristic structure of omics data. For one, omics data is high-dimensional, comprising cellular information in unprecedented details. Moreover, there is an intricate correlation structure among the variables due to e.g internal cellular regulation, or external, latent factors. Variable selection for uncorrelated data is well established. In contrast, there is no consensus on how to approach variable selection under correlation. Here, we introduce a multivariate framework for variable selection that explicitly accounts for the correlation among markers. In particular, we present two novel quantities for variable importance: the correlation-adjusted t (CAT) score for classification, and the correlation-adjusted (marginal) correlation (CAR) score for regression. The CAT score is defined as the Mahalanobis-decorrelated t-score vector, and the CAR score as the Mahalanobis-decorrelated correlation between the predictor variables and the outcome. We derive the CAT and CAR score from a predictive point of view in linear discriminant analysis and regression; both quantities assess the weight of a decorrelated and standardized variable on the prediction rule. Furthermore, we discuss properties of both scores and relations to established quantities. Above all, the CAT score decomposes Hotelling’s T 2 and the CAR score the proportion of variance explained. Notably, the decomposition of total variance into explained and unexplained variance in the linear model can be rewritten in terms of CAR scores. To render our approach applicable on high-dimensional omics data we devise an efficient algorithm for shrinkage estimates of the CAT and CAR score. Subsequently, we conduct extensive simulation studies to investigate the performance of our novel approaches in ranking and prediction under correlation. Here, CAT and CAR scores consistently improve over marginal approaches in terms of more true positives selected and a lower model error. Finally, we illustrate the application of CAT and CAR score on real omics data. In particular, we analyze genomics, transcriptomics, and metabolomics data. We ascertain that CAT and CAR score are competitive or outperform state of the art techniques in terms of true positives detected and prediction error.
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Mahmoud, Mahmoud A. "The Monitoring of Linear Profiles and the Inertial Properties of Control Charts." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/29544.

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The Phase I analysis of data when the quality of a process or product is characterized by a linear function is studied in this dissertation. It is assumed that each sample collected over time in the historical data set consists of several bivariate observations for which a simple linear regression model is appropriate, a situation common in calibration applications. Using a simulation study, the researcher compares the performance of some of the recommended approaches used to assess the stability of the process. Also in this dissertation, a method based on using indicator variables in a multiple regression model is proposed. This dissertation also proposes a change point approach based on the segmented regression technique for testing the constancy of the regression parameters in a linear profile data set. The performance of the proposed change point method is compared to that of the most effective Phase I linear profile control chart approaches using a simulation study. The advantage of the proposed change point method over the existing methods is greatly improved detection of sustained step changes in the process parameters. Any control chart that combines sample information over time, e.g., the cumulative sum (CUSUM) chart and the exponentially weighted moving average (EWMA) chart, has an ability to detect process changes that varies over time depending on the past data observed. The chart statistics can take values such that some shifts in the parameters of the underlying probability distribution of the quality characteristic are more difficult to detect. This is referred to as the "inertia problem" in the literature. This dissertation shows under realistic assumptions that the worst-case run length performance of control charts becomes as informative as the steady-state performance. Also this study proposes a simple new measure of the inertial properties of control charts, namely the signal resistance. The conclusions of this study support the recommendation that Shewhart limits should be used with EWMA charts, especially when the smoothing parameter is small. This study also shows that some charts proposed by Pignatiello and Runger (1990) and Domangue and Patch (1991) have serious disadvantages with respect to inertial properties.
Ph. D.
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Ramaboa, Kutlwano. "Contributions to Linear Regression diagnostics using the singular value decompostion: Measures to Indentify Outlying Observations, Influential Observations and Collinearity in Multivariate Data." Doctoral thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/4391.

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Souza, Aline Campos Reis de. "Modelos de regressão linear heteroscedásticos com erros t-Student : uma abordagem bayesiana objetiva." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/7540.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
In this work , we present an extension of the objective bayesian analysis made in Fonseca et al. (2008), based on Je reys priors for linear regression models with Student t errors, for which we consider the heteroscedasticity assumption. We show that the posterior distribution generated by the proposed Je reys prior, is proper. Through simulation study , we analyzed the frequentist properties of the bayesian estimators obtained. Then we tested the robustness of the model through disturbances in the response variable by comparing its performance with those obtained under another prior distributions proposed in the literature. Finally, a real data set is used to analyze the performance of the proposed model . We detected possible in uential points through the Kullback -Leibler divergence measure, and used the selection model criterias EAIC, EBIC, DIC and LPML in order to compare the models.
Neste trabalho, apresentamos uma extensão da análise bayesiana objetiva feita em Fonseca et al. (2008), baseada nas distribuicões a priori de Je reys para o modelo de regressão linear com erros t-Student, para os quais consideramos a suposicão de heteoscedasticidade. Mostramos que a distribuiçãoo a posteriori dos parâmetros do modelo regressão gerada pela distribuição a priori e própria. Através de um estudo de simulação, avaliamos as propriedades frequentistas dos estimadores bayesianos e comparamos os resultados com outras distribuições a priori encontradas na literatura. Além disso, uma análise de diagnóstico baseada na medida de divergência Kullback-Leiber e desenvolvida com analidade de estudar a robustez das estimativas na presença de observações atípicas. Finalmente, um conjunto de dados reais e utilizado para o ajuste do modelo proposto.
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Júnior, Antônio Carlos Pacagnella. "A inovação tecnológica nas indústrias do Estado de São Paulo: uma análise dos indicadores da PAEP." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/96/96132/tde-25072006-151430/.

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A inovação tecnológica desempenha um papel fundamental no desenvolvimento de empresas, regiões e mesmo de países. Especificamente no estado de São Paulo, estudar os aspectos relevantes a este tema é de suma importância por se tratar do estado mais industrializado e mais importante economicamente no Brasil. Dentro deste contexto, este estudo visa analisar especificamente aspectos ligados à inovação tecnológica nas empresas dos diversos setores de atividade industrial, utilizando para isto ndicadores de inovação tecnológica e de dados empresariais da Pesquisa de Atividade Econômica Paulista (PAEP), realizada pela fundação Sistema Estadual de Análise de Dados Estatísticos (SEADE), sobre o período de 1999 a 2001.
The technological innovation performs a fundamental part in the development process of companies, regions and even countries. Specifically in the state of São Paulo, the study of relevant aspects to this theme is of summary importance because it is the most industrialized and economically important in this country. Within of this context, this study aim to analyze specifically some aspects linked to the technological innovation in different sections of industrial activity, using to this, technological innovation indicators and business results obtained by the Paulista Research of Economic Activities (PAEP), that was realized by SEADE foundation over the period of 1999 to 2001.
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Delmonde, Marcelo Vinicius Felizatti. "Eletro-oxidação oscilatória de moléculas orgânicas pequenas: produção de espécies voláteis e desempenho catalítico." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/75/75134/tde-19042016-153123/.

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A emergência frequente de oscilações de corrente e potencial durante a eletro-oxidação de moléculas orgânicas pequenas tem implicações mecanísticas importantes, como por exemplo, na conversão reacional global e, portanto, no desempenho de dispositivos práticos de conversão de energia. Orientado nesse sentido, este trabalho desenvolveu-se por meio de duas frentes relacionadas: (a) utilizando-se medidas obtidas por meio do acoplamento de uma célula eletroquímica a um espectrômetro de massas, estudou-se a dinâmica da produção de espécies voláteis durante a eletro-oxidação oscilatória de ácido fórmico, metanol e etanol. Além da apresentação de resultados experimentais ainda não relatados, introduz-se o uso de regressão linear multivariada para se comparar a corrente faradaica total estimada, com a proveniente da produção de espécies voláteis detectáveis: dióxido de carbono para ácido fórmico, dióxido de carbono e metilformiato para metanol e, dióxido de carbono e acetaldeído para etanol. A análise fornece a melhor combinação das correntes iônicas detectadas para se representar a corrente global ou a máxima contribuição faradaica possível devido à produção de espécies voláteis. Os resultados foram discutidos em conexão com aspectos do mecanismo reacional de cada molécula. A incompatibilidade entre a corrente faradaica total estimada e a obtida pela melhor combinação das correntes parciais provenientes da produção de espécies voláteis foi pequena para ácido fórmico, quatro e cinco vezes maior para etanol e metanol, respectivamente, evidenciando, nestes dois últimos casos, o aumento do papel desempenhado por espécies solúveis parcialmente oxidadas; (b) investigou-se características gerais da eletro-oxidação de formaldeído, ácido fórmico e metanol sobre platina em meio ácido, com ênfase na comparação do desempenho eletrocatalítico global sob condições estacionária e oscilatória. A comparação procedeu-se por meio da interpretação de resultados tratados de diferentes formas e generalizada pela utilização das mesmas condições experimentais em todos os casos. Para todos os sistemas, o baixo potencial alcançado durante as oscilações evidenciou uma considerável diminuição do sobrepotencial associado à reação anódica, se comparado com o obtido na ausência de oscilações. Além do mais, o processo de reativação superficial do catalisador que ocorre durante as oscilações amplia o desempenho de todos os sistemas em termos de atividade eletrocatalítica. Por fim, também são discutidos alguns aspectos do mecanismo reacional das moléculas estudadas.
The frequent emergence of current/potential oscillations during the electrooxidation of small organic molecules has implications on mechanistic aspects such as, for example, on the overall reaction conversion, and thus on the performance of practical devices of energy conversion. In this direction, this work is divided in two parts: (a) by means of on line Differential Electrochemical Mass Spectrometry (DEMS) it was studied the production of volatile species during the electrooxidation of formic acid, methanol and ethanol. Besides the presentation of previously unreported DEMS results on the oscillatory dynamics of such systems, it was introduced the use of multivariate linear regression to compare the estimated total faradaic current with the one comprising the production of volatile detectable species, namely: carbon dioxide for formic acid, carbon dioxide and methylformate for methanol and, carbon dioxide and acetaldehyde for ethanol. The introduced analysis provided the best combination of the DEMS ion currents to represent the total faradaic current or the maximum possible faradaic contribution of the volatile products for the global current. The results were discussed in connection with mechanistic aspects for each system. The mismatch between estimated total current and the one obtained by the best combination of partial currents of volatile products was found to be small for formic acid, 4 and 5 times bigger for ethanol and methanol, respectively, evidencing the increasing role played by partially oxidized soluble species in each case; (b) it was investigated general features of the electro-oxidation of formaldehyde, formic acid and methanol on platinum and in acid media, with emphasis on the comparison of the performance under stationary and oscillatory regimes. The comparison is carried out by different means and generalized by the use of identical experimental conditions in all cases. In all three systems studied, the occurrence of potential oscillations is associated with excursions of the electrode potentials to lower values, which considerable decreases the overpotential of the anodic reaction, when compared to that in the absence of oscillations. In addition, the reactivation of catalyst surface benefits the performance of all systems in terms of electrocatalytic activity. Finally, some mechanistic aspects of the studied reactions are also discussed.
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Maier, Marco J. "DirichletReg: Dirichlet Regression for Compositional Data in R." WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4077/1/Report125.pdf.

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Dirichlet regression models can be used to analyze a set of variables lying in a bounded interval that sum up to a constant (e.g., proportions, rates, compositions, etc.) exhibiting skewness and heteroscedasticity, without having to transform the data. There are two parametrization for the presented model, one using the common Dirichlet distribution's alpha parameters, and a reparametrization of the alpha's to set up a mean-and-dispersion-like model. By applying appropriate link-functions, a GLM-like framework is set up that allows for the analysis of such data in a straightforward and familiar way, because interpretation is similar to multinomial logistic regression. This paper gives a brief theoretical foundation and describes the implementation as well as application (including worked examples) of Dirichlet regression methods implemented in the package DirichletReg (Maier, 2013) in the R language (R Core Team, 2013). (author's abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Books on the topic "Heteroscedastic Multivariate Linear Regression"

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Multivariate general linear models. Thousand Oaks, Calif: Sage, 2011.

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1951-, Christensen Ronald, ed. Advanced linear modeling: Multivariate, time series, and spatial data; nonparametric regression and response surface maximization. 2nd ed. New York: Springer, 2001.

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Gelman, Andrew. Regression and Other Stories. Cambridge, UK: Cambridge University Press, 2020.

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Young, Derek Scott. Handbook of Regression Methods: 1st edition. Boca Raton, Florida, USA: Chapman and Hall, CRC Press, 2017.

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Goodman, Leo A. Analyzing qualitative/categorical data: Log-linear models and latent structure analysis. Lanham, MD: University Press of America, 1985.

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Hoffmann, John P. Regression Models For Categorical, Count, And Related Variables: An Applied Approach. Oakland, California, USA: University of California Press, 2016.

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Structural equation modeling: A second course. 2nd ed. Charlotte, NC: Information Age Publishing, Inc., 2013.

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K, Neerchal Nagaraj, and SAS Institute, eds. Overdispersion models in SAS. Cary, N.C: SAS Institute, 2012.

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Moser, Barry Kurt. Linear models: A mean model approach. San Diego: Academic Press, 1996.

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Jiang, Jiming. Robust Mixed Model Analysis. Singapore: World Scientific Publishing Co Pte Ltd, 2019.

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Book chapters on the topic "Heteroscedastic Multivariate Linear Regression"

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Olive, David J. "Multivariate Models." In Linear Regression, 299–312. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_10.

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Olive, David J. "Multivariate Linear Regression." In Linear Regression, 343–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_12.

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Flury, Bernhard, and Hans Riedwyl. "Multiple linear regression." In Multivariate Statistics, 54–74. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1217-5_5.

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Reinsel, Gregory C., and Raja P. Velu. "Multivariate Linear Regression." In Multivariate Reduced-Rank Regression, 1–14. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2853-8_1.

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Oja, Hannu. "Multivariate linear regression." In Multivariate Nonparametric Methods with R, 183–200. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0468-3_13.

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Reinsel, Gregory C., Raja P. Velu, and Kun Chen. "Multivariate Linear Regression." In Multivariate Reduced-Rank Regression, 1–17. New York, NY: Springer New York, 2022. http://dx.doi.org/10.1007/978-1-0716-2793-8_1.

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Olive, David J. "Multivariate Linear Regression." In Robust Multivariate Analysis, 327–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68253-2_12.

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Wackernagel, Hans. "Linear Regression and Simple Kriging." In Multivariate Geostatistics, 15–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05294-5_3.

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Wackernagel, Hans. "Linear Regression and Simple Kriging." In Multivariate Geostatistics, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03550-4_3.

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Zelterman, Daniel. "Multivariable Linear Regression." In Applied Multivariate Statistics with R, 231–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14093-3_9.

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Conference papers on the topic "Heteroscedastic Multivariate Linear Regression"

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Buckley, J. J., T. Feuring, and Y. Hayashi. "Multivariate non-linear fuzzy regression." In Proceedings of 8th International Fuzzy Systems Conference. IEEE, 1999. http://dx.doi.org/10.1109/fuzzy.1999.793036.

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Ruuska, Jari, Eemeli Ruhanen, Janne Kauppi, Sakari Kauvosaari, and Mika Kosonen. "Multivariate linear regression model of paste thickener." In SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland. Linköping University Electronic Press, 2021. http://dx.doi.org/10.3384/ecp20176160.

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Gayathri, S., A. Saraswathi Priyadharshini, and P. T. V. Bhuvaneswari. "Multivariate linear regression based activity recognition and classification." In 2014 International Conference on Information Communication and Embedded Systems (ICICES). IEEE, 2014. http://dx.doi.org/10.1109/icices.2014.7034088.

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Maragos, Petros, and Emmanouil Theodosis. "Multivariate Tropical Regression and Piecewise-Linear Surface Fitting." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054058.

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Su, Yan, and Shao-Yue Kang. "Testing for multivariate normality of disturbances in the multivariate linear regression model." In 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/isrme-15.2015.90.

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Sun, Xiaokui, Zhiyou Ouyang, and Dong Yue. "Short-term load forecasting based on multivariate linear regression." In 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2017. http://dx.doi.org/10.1109/ei2.2017.8245401.

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Du, Wenliang, Yunghsiang S. Han, and Shigang Chen. "Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification." In Proceedings of the 2004 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2004. http://dx.doi.org/10.1137/1.9781611972740.21.

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Nasri, Mourad, and Mohamed Hamdi. "LTE QoS Parameters Prediction Using Multivariate Linear Regression Algorithm." In 2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN). IEEE, 2019. http://dx.doi.org/10.1109/icin.2019.8685914.

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Zhou, Yongdao, Shilong Gao, and Wangyong Lv. "Multivariate Local Linear Regression in the Prediction of ARFIMA Processes." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5517714.

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Wang, Haiqiang, Yu Zhang, Jing Jin, and Xingyu Wang. "SSVEP recognition using multivariate linear regression for brain computer interface." In 2015 IEEE International Conference on Computer and Communications (ICCC). IEEE, 2015. http://dx.doi.org/10.1109/compcomm.2015.7387563.

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