Relevant bibliographies by topics / Linear regression / Dissertations / Theses
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Dissertations / Theses on the topic 'Linear regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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1

Bai, Xue. "Robust linear regression." Kansas State University, 2012. http://hdl.handle.net/2097/14977.

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Master of Science<br>Department of Statistics<br>Weixin Yao<br>In practice, when applying a statistical method it often occurs that some observations deviate from the usual model assumptions. Least-squares (LS) estimators are very sensitive to outliers. Even one single atypical value may have a large effect on the regression parameter estimates. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we review various robust regression methods including: M-estimate, LMS estimat
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2

Hernandez, Erika Lyn. "Parameter Estimation in Linear-Linear Segmented Regression." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3551.pdf.

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3

Ollikainen, Kati. "PARAMETER ESTIMATION IN LINEAR REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4138.

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Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist
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4

Chen, Xinyu. "Inference in Constrained Linear Regression." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/405.

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Regression analyses constitutes an important part of the statistical inference and has great applications in many areas. In some applications, we strongly believe that the regression function changes monotonically with some or all of the predictor variables in a region of interest. Deriving analyses under such constraints will be an enormous task. In this work, the restricted prediction interval for the mean of the regression function is constructed when two predictors are present. I use a modified likelihood ratio test (LRT) to construct prediction intervals.
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Waterman, Megan Janet Tuttle. "Linear Mixed Model Robust Regression." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27708.

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Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a nonpara
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Ratnasingam, Suthakaran. "Sequential Change-point Detection in Linear Regression and Linear Quantile Regression Models Under High Dimensionality." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu159050606401363.

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7

Rettes, Julio Alberto Sibaja. "Robust algorithms for linear regression and locally linear embedding." reponame:Repositório Institucional da UFC, 2017. http://www.repositorio.ufc.br/handle/riufc/22445.

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RETTES, Julio Alberto Sibaja. Robust algorithms for linear regression and locally linear embedding. 2017. 105 f. Dissertação (Mestrado em Ciência da Computação)- Universidade Federal do Ceará, Fortaleza, 2017.<br>Submitted by Weslayne Nunes de Sales (weslaynesales@ufc.br) on 2017-03-30T13:15:27Z No. of bitstreams: 1 2017_dis_rettesjas.pdf: 3569500 bytes, checksum: 46cedc2d9f96d0f58bcdfe3e0d975d78 (MD5)<br>Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2017-04-04T11:10:44Z (GMT) No. of bitstreams: 1 2017_dis_rettesjas.pdf: 3569500 bytes, checksum: 46cedc2d9f96d0f58bcdfe3e0
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Peraça, Maria da Graça Teixeira. "Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação." reponame:Repositório Institucional da FURG, 2009. http://repositorio.furg.br/handle/1/3436.

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Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009.<br>Submitted by Lilian M. Silva (lilianmadeirasilva@hotmail.com) on 2013-04-22T19:51:54Z No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5)<br>Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2013-06-03T19:20:55Z (GMT) No. of bitstreams: 1 Modelos para estimativa do Grau
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9

Bocci, Cynthia Jacqueline. "Linear regression with spatially correlated data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0012/NQ52271.pdf.

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10

Mahmood, Nozad. "Sparse Ridge Fusion For Linear Regression." Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5986.

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For a linear regression, the traditional technique deals with a case where the number of observations n more than the number of predictor variables p (n>p). In the case n<p, the classical method fails to estimate the coefficients. A solution of this problem in the case of correlated predictors is provided in this thesis. A new regularization and variable selection is proposed under the name of Sparse Ridge Fusion (SRF). In the case of highly correlated predictor , the simulated examples and a real data show that the SRF always outperforms the lasso, elastic net, and the S-Lasso, and the result
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11

Cao, Chendi. "Linear regression with Laplace measurement error." Kansas State University, 2016. http://hdl.handle.net/2097/32719.

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Master of Science<br>Statistics<br>Weixing Song<br>In this report, an improved estimation procedure for the regression parameter in simple linear regression models with the Laplace measurement error is proposed. The estimation procedure is made feasible by a Tweedie type equality established for E(X|Z), where Z = X + U, X and U are independent, and U follows a Laplace distribution. When the density function of X is unknown, a kernel estimator for E(X|Z) is constructed in the estimation procedure. A leave-one-out cross validation bandwidth selection method is designed. The finite sample perform
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Gündüz, Necla. "D-optimal designs for weighted linear regression and binary regression models." Thesis, University of Glasgow, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301629.

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13

Rodrigues, Cátia Sofia Martins. "Quais os fatores que determinam o rendimento dos indivíduos em Portugal? - Regressão de Quantis." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/23425.

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Mestrado Bolonha em Métodos Quantitativos para a Decisão Económica e Empresarial<br>Apesar de se ter vindo a verificar, ao longo dos anos, um decréscimo significativo na desigualdade entre rendimentos, este tema ainda é alvo de estudo, principalmente numa abordagem econométrica, onde o principal objetivo passa por identificar e perceber os principais fatores que estão por detrás das desigualdades sentidas. Desta forma, o presente projeto destina-se ao estudo dos fatores que determinam o rendimento dos indivíduos residentes em Portugal, adotando uma abordagem de regressão de quantis, uma vez q
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14

Edlund, Ove. "Solution of linear programming and non-linear regression problems using linear M-estimation methods /." Luleå, 1999. http://epubl.luth.se/1402-1544/1999/17/index.html.

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15

Bullas, J. M. David. "K-nearest neighbours with weighted linear regression." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ34340.pdf.

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16

Hamzah, Nor Aishah. "Robust regression estimation in generalized linear models." Thesis, University of Bristol, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.294372.

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17

Ah-Kine, Pascal Soon Shien. "Simultaneous confidence bands in linear regression analysis." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/167557/.

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A simultaneous confidence band provides useful information on the plausible range of an unknown regression model. For a simple linear regression model, the most frequently quoted bands in the statistical literature include the two-segment band, the three-segment band and the hyperbolic band, and for a multiple linear regression model, the most com- mon bands in the statistical literature include the hyperbolic band and the constant width band. The optimality criteria for confidence bands include the Average Width criterion considered by Gafarian (1964) and Naiman (1984) among others, and the M
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18

Essomba, Rene Franck. "An investigation into Functional Linear Regression Modeling." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15591.

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Functional data analysis, commonly known as FDA", refers to the analysis of information on curves of functions. Key aspects of FDA include the choice of smoothing techniques, data reduction, model evaluation, functional linear modeling and forecasting methods. FDA is applicable in numerous applications such as Bioscience, Geology, Psychology, Sports Science, Econometrics, Meteorology, etc. This dissertation main objective is to focus more specifically on Functional Linear Regression Modelling (FLRM), which is an extension of Multivariate Linear Regression Modeling. The problem of constructing
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19

Gormley, Nolan D. "Knotilus: A Differentiable Piecewise Linear Regression Framework." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617222994436272.

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20

Khogasteh, Sam, and Edvin Wiorek. "Predicting Influencer Actual Reach Using Linear Regression." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299339.

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The influencer marketing industry has seen a tremendous growth in recent years, yet the effectiveness of this marketing form is still largely unexplored. This report aims to explore how various performance measures are linked to the reach of social media pages, utilizing the linear regression model. Three different data sets were collected manually, or using web scraping. By splitting these data sets to training- and test data we examined the degree to which the linear regression model can predict the actual reach, the page views and the weekly growth of an influencer. We concluded that there
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21

Mirzayeva, Hijran. "Nonsmooth optimization algorithms for clusterwise linear regression." Thesis, University of Ballarat, 2013. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/41975.

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Data mining is about solving problems by analyzing data that present in databases. Supervised and unsupervised data classification (clustering) are among the most important techniques in data mining. Regression analysis is the process of fitting a function (often linear) to the data to discover how one or more variables vary as a function of another. The aim of clusterwise regression is to combine both of these techniques, to discover trends within data, when more than one trend is likely to exist. Clusterwise regression has applications for instance in market segmentation, where it allows one
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22

Smith, David McCulloch. "Regression using QR decomposition methods." Thesis, University of Kent, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303532.

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23

Möls, Märt. "Linear mixed models with equivalent predictors /." Online version, 2004. http://dspace.utlib.ee/dspace/bitstream/10062/1339/5/Mols.pdf.

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24

Forslund, Gustaf, and David Åkesson. "Predicting share price by using Multiple Linear Regression." Thesis, KTH, Farkost och flyg, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140645.

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The aim of the project was to design a multiple linear regression model and use it to predict the share’s closing price for 44 companies listed on the OMX Stockholm stock exchange’s Large Cap list. The model is intended to be used as a day trading guideline i.e. today’s information is used to predict tomorrow’s closing price. The regression was done in Microsoft Excel 2010[18] by using its built-in function LINEST. The LINEST-function uses the dependent variable y and all the covariates x to calculate the β-value belonging to each covariate. Several multiple linear regression models were creat
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25

Aldahmani, Saeed. "High-dimensional linear regression problems via graphical models." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/19207/.

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This thesis introduces a new method for solving the linear regression problem where the number of observations n is smaller than the number of variables (predictors) v. In contrast to existing methods such as ridge regression, Lasso and Lars, the proposed method uses the idea of graphical models and provides unbiased parameter estimates under certain conditions. In addition, the new method provides a detailed graphical conditional correlation structure for the predictors, whereby the real causal relationship between predictors can be identified. Furthermore, the proposed method is extended to
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26

Saleem, Aban, and Jacob Blomgren. "Modelling Pupils’ Grades with Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275672.

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This thesis was based on the subjects of mathematical statistics and industrial economics and management in order to analyze the grades of pupils in the final year of elementary school. The purpose was to find out what variables had a statistically significant impact on pupils’ final grades so that municipalities and schools could better understand what variables are important when trying to improve the average school results. A multiple regression model was used on data, obtained from the database of Skolverket, in order to examine what variables were statistically important. The final regres
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Brodbeck, William Joseph. "The Effect of Readability on Simple Linear Regression." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1591867761661656.

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Bunea, Florentina. "A model selection approach to partially linear regression /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/8971.

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Mahmood, Arshad. "Rainfall prediction in Australia : Clusterwise linear regression approach." Thesis, Federation University Australia, 2017. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/159251.

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Accurate rainfall prediction is a challenging task because of the complex physical processes involved. This complexity is compounded in Australia as the climate can be highly variable. Accurate rainfall prediction is immensely benecial for making informed policy, planning and management decisions, and can assist with the most sustainable operation of water resource systems. Short-term prediction of rainfall is provided by meteorological services; however, the intermediate to long-term prediction of rainfall remains challenging and contains much uncertainty. Many prediction approaches have been
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Sardy, Sylvain. "A Comparison of Two Linear Nonparametric Regression Techniques." DigitalCommons@USU, 1992. https://digitalcommons.usu.edu/etd/7123.

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This thesis presented a useful tool in regression. Nonparametric linear regression techniques were described in the general context of regression. A comparison of two of these techniques, kernel regression and iterative regression, showed various aspects of nonparametric linear regressors.
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Nunes, Hélio Rubens de Carvalho. "Ponderação Bayesiana de modelos em regressão linear clássica." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-16112005-155133/.

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Este trabalho tem o objetivo de divulgar a metodologia de ponderação de modelos ou Bayesian Model Averaging (BMA) entre os pesquisadores da área agronômica e discutir suas vantagens e limitações. Com o BMA é possível combinar resultados de diferentes modelos acerca de determinada quantidade de interesse, com isso, o BMA apresenta-se como sendo uma metodologia alternativa de análise de dados frente os usuais métodos de seleção de modelos tais como o Coeficiente de Determinação Múltipla (R2 ), Coeficiente de Determinação Múltipla Ajustado (R2), Estatística de Mallows ( Cp) e Soma de Quadrados de
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Bowtell, Philip. "Non-linear functional relationships." Thesis, University of Reading, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284183.

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Lawrence, David E. "Cluster-Based Bounded Influence Regression." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/28455.

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In the field of linear regression analysis, a single outlier can dramatically influence ordinary least squares estimation while low-breakdown procedures such as M regression and bounded influence regression may be unable to combat a small percentage of outliers. A high-breakdown procedure such as least trimmed squares (LTS) regression can accommodate up to 50% of the data (in the limit) being outlying with respect to the general trend. Two available one-step improvement procedures based on LTS are Mallows 1-step (M1S) regression and Schweppe 1-step (S1S) regression (the current state-of-the-
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Taga, Marcel Frederico de Lima. "Regressão linear com medidas censuradas." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05122008-005901/.

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Consideramos um modelo de regressão linear simples, em que tanto a variável resposta como a independente estão sujeitas a censura intervalar. Como motivação utilizamos um estudo em que o objetivo é avaliar a possibilidade de previsão dos resultados de um exame audiológico comportamental a partir dos resultados de um exame audiológico eletrofisiológico. Calculamos intervalos de previsão para a variável resposta, analisamos o comportamento dos estimadores de máxima verossimilhança obtidos sob o modelo proposto e comparamos seu desempenho com aquele de estimadores obtidos de um modelo de regressã
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Januario, Ana Paula Ferrari. "Análise estatística da produção de vitelão Mertolengo." Master's thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/29316.

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The work was intended to support the Association of mertolenga cattle breed in its breeding process and decision making, namely in modeling the cost per day of production of the male mertolenga cattle, and in identifying the variables that favor the sale of the animal as a product with a protected designation of origin (PDO) seal. The database contained information on 716 male animals, of which 54 % went to the slaughter that guarantees the PDO seal. We also had data on the cost structure production of the animals from when it enters into the CTR to slaughter, in addition to the individual cha
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Rodriguez, Mary Ana Petersen. "Parâmetros genéticos e fenotípicos do perfil de ácidos graxos do leite de vacas da raça holandesa." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/11/11139/tde-30102013-110828/.

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Durante as últimas décadas, o melhoramento genético em bovinos leiteiros no Brasil baseou-se somente na importação de material genético, resultando em ganhos genéticos de pequena magnitude para as características de interesse econômico. Dessa forma, existe a necessidade eminente de avaliações genéticas dos animais sob condições nacionais de ambiente, de maneira a se prover um aumento na produção de leite aliado à qualidade. Neste contexto, o conhecimento sobre a composição do leite é de extrema importância para o entendimento de como alguns fatores ambientais e, principalmente genéticos podem
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Medeiros, Patrick Valverde. "Análise da evapotranspiração de referência a partir de medidas lisimétricas e ajuste estatístico de estimativas de nove equações empírico-teóricas com base na equação de Penman-Monteith." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/18/18138/tde-21052008-090008/.

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A quantificação da evapotranspiração é uma tarefa essencial para a determinação do balanço hídrico em uma bacia hidrográfica e para o estabelecimento do déficit hídrico de uma cultura. Nesse sentido, o presente trabalho aborda a análise da evapotranspiração de referência (ETo) para a região de Jaboticabal-SP. O comportamento do fenômeno na região foi estudado a partir da interpretação de dados de uma bateria de 12 lisímetros de drenagem (EToLis) e estimativas teóricas por 10 equações diferentes disponíveis na literatura. A análise estatística de correlação indica que as estimativas da ETo por
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Kartal, Elcin. "Metamodeling Complex Systems Using Linear And Nonlinear Regression Methods." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608930/index.pdf.

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Metamodeling is a very popular approach for the approximation of complex systems. Metamodeling techniques can be categorized according to the type of regression method employed as linear and nonlinear models. The Response Surface Methodology (RSM) is an example of linear regression. In classical RSM metamodels, parameters are estimated using the Least Squares (LS) Method. Robust regression techniques, such as Least Absolute Deviation (LAD) and M-regression, are also considered in this study due to the outliers existing in data sets. Artificial Neural Networks (ANN) and Multivariate Adaptive Re
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Bentley, Jason Phillip. "Exact Markov chain Monte Carlo and Bayesian linear regression." Thesis, University of Canterbury. Mathematics and Statistics, 2009. http://hdl.handle.net/10092/2534.

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In this work we investigate the use of perfect sampling methods within the context of Bayesian linear regression. We focus on inference problems related to the marginal posterior model probabilities. Model averaged inference for the response and Bayesian variable selection are considered. Perfect sampling is an alternate form of Markov chain Monte Carlo that generates exact sample points from the posterior of interest. This approach removes the need for burn-in assessment faced by traditional MCMC methods. For model averaged inference, we find the monotone Gibbs coupling from the past (CFTP) a
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Crews, Hugh Bates. "Fast FSR Methods for Second-Order Linear Regression Models." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-04282008-151809/.

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Many variable selection techniques have been developed that focus on first-order linear regression models. In some applications, such as modeling response surfaces, fitting second-order terms can improve predictive accuracy. However, the number of spurious interactions can be large leading to poor results with many methods. We focus on forward selection, describing algorithms that use the natural hierarchy existing in second-order linear regression models to limit spurious interactions. We then develop stopping rules by extending False Selection Rate methodology to these algorithms. In additio
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Tsakonas, Efthymios. "Convex Optimization for Assignment and Generalized Linear Regression Problems." Doctoral thesis, KTH, Signalbehandling, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-150338.

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This thesis considers optimization techniques with applications in assignment and generalized linear regression problems. The first part of the thesis investigates the worst-case robust counterparts of combinatorial optimization problems with least squares (LS) cost functions, where the uncertainty lies on the linear transformation of the design variables. We consider the case of ellipsoidal uncertainty, and prove that the worst case robust LS optimization problem, although NP-hard, is still amenable to convexrelaxation based on semidefinite optimization. We motivate our proposed relaxation us
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Gustafsson, Alexander, and Sebastian Wogenius. "Modelling Apartment Prices with the Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146735.

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This thesis examines factors that are of most statistical significance for the sales prices of apartments in the Stockholm City Centre. Factors examined are address, area, balcony, construction year, elevator, fireplace, floor number, maisonette, monthly fee, penthouse and number of rooms. On the basis of this examination, a model for predicting prices of apartments is constructed. In order to evaluate how the factors influence the price, this thesis analyses sales statistics and the mathematical method used is the multiple linear regression model. In a minor case-study and literature review,
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Lin, Shan. "Simultaneous confidence bands for linear and logistic regression models." Thesis, University of Southampton, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443030.

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"Supervised ridge regression in high dimensional linear regression." 2013. http://library.cuhk.edu.hk/record=b5549319.

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在機器學習領域，我們通常有很多的特徵變量，以確定一些回應變量的行為。例如在基因測試問題，我們有數以萬計的基因用來作為特徵變量，而它們與某些疾病的關係需要被確定。沒有提供具體的知識，最簡單和基本的方法來模擬這種問題會是一個線性的模型。有很多現成的方法來解決線性回歸問題，像傳統的普通最小二乘回歸法，嶺回歸和套索回歸。設 N 為樣本數和，p 為特徵變量數，在普通的情況下，我們通常有足夠的樣本（N> P）。 在這種情況下，普通線性回歸的方法，例如嶺回歸通常會給予合理的對未來的回應變量測值的預測。隨著現代統計學的發展，我們經常會遇到高維問題（N << P），如 DNA 芯片數據的測試問題。在這些類型的高維問題中，確定特徵變量和回應變量之間的關係在沒有任何進一步的假設的情況下是相當困難的。在很多現實問題中，儘管有大量的特徵變量存在，但是完全有可能只有極少數的特徵變量和回應變量有直接關係，而大部分其他的特徵變量都是無效的。 套索和嶺回歸等傳統線性回歸在高維問題中有其局限性。套索回歸在應用於高維問題時，會因為測量噪聲的存在而表現得很糟糕，這將導致非常低的預測準確率。嶺回歸也有其明顯的局限性。它不能夠分開真正的特徵變量和無效的特徵變量。我提出的新方法的目的就是在高維線性回歸中克服以上兩種方法的局限性，從而導致更精確和穩定的預測。想法其實很簡單，與其做一個單一步驟的線性回歸，我們將回歸過程分成兩個步驟
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45

"Benchmarking non-linear series with quasi-linear regression." 2012. http://library.cuhk.edu.hk/record=b5549055.

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一個社會經濟學的目標變量，經常存在兩種不同收集頻率的數據。由於較低頻率的一組數據通常由大型普查中所獲得，其準確度及可靠性會較高。因此較低頻率的一組數據一般會視作基準，用作對頻率較高的另一組數據進行修正。<br>在基準修正過程中，一般會假設調查誤差及目標數據的大小互相獨立，即「累加模型」。然而，現實中兩者通常是相關的，目標變量越大，調查誤差亦會越大，即「乘積模型」。對此問題，陳兆國及胡家浩提出了利用準線性回歸手法對乘積模型進行基準修正。在本論文中，假設調查誤差服從AR(1)模型，首先我們會示範如何利用準線性回歸手法及默認調查誤差模型進行基準數據修正。然後，運用基準預測的方式，提出一個對調查誤差模型的估計辦法。最後我們會比較兩者的表現以及一些選擇誤差模型的指引。<br>For a target socio-economic variable, two sources of data with different collecting frequencies may be available in survey data analysis. In general, due to the difference of sample size or the data source, two sets of data do not agree with each other. Usually,
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46

Lu, QiQi. "Linear regression under multiple changepoints." 2004. http://purl.galileo.usg.edu/uga%5Fetd/lu%5Fqiqi%5F200408%5Fphd.

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47

曾麗齡. "Linear Regression with Censored Data." Thesis, 1990. http://ndltd.ncl.edu.tw/handle/73824948008674721841.

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Dias, Sónia Manuela Mendes. "Linear regression with empirical distributions." Doctoral thesis, 2014. https://repositorio-aberto.up.pt/handle/10216/74191.

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Huang, Min Ching. "Piecewise linear tree-structured regression." 1989. http://catalog.hathitrust.org/api/volumes/oclc/21951798.html.

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Thesis (Ph. D.)--University of Wisconsin--Madison, 1989.<br>Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 101-104).
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Dias, Sónia Manuela Mendes. "Linear regression with empirical distributions." Tese, 2014. https://repositorio-aberto.up.pt/handle/10216/74191.

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