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1

Mak, Carmen. "Polychotomous logistic regression via the Lasso." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0004/NQ41227.pdf.

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Olaya, Bucaro Orlando. "Predicting risk of cyberbullying victimization using lasso regression." Thesis, Uppsala universitet, Statistiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767.

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The increased online presence and use of technology by today’s adolescents has created new places where bullying can occur. The aim of this thesis is to specify a prediction model that can accurately predict the risk of cyberbullying victimization. The data used is from a survey conducted at five secondary schools in Pereira, Colombia. A logistic regression model with random effects is used to predict cyberbullying exposure. Predictors are selected by lasso, tuned by cross-validation. Covariates included in the study includes demographic variables, dietary habit variables, parental mediation v
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Patnaik, Kaushik. "Adaptive learning in lasso models." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54353.

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Regression with L1-regularization, Lasso, is a popular algorithm for recovering the sparsity pattern (also known as model selection) in linear models from observations contaminated by noise. We examine a scenario where a fraction of the zero co-variates are highly correlated with non-zero co-variates making sparsity recovery difficult. We propose two methods that adaptively increment the regularization parameter to prune the Lasso solution set. We prove that the algorithms achieve consistent model selection with high probability while using fewer samples than traditional Lasso. The algorithm c
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Caster, Ola. "Mining the WHO Drug Safety Database Using Lasso Logistic Regression." Thesis, Uppsala University, Department of Mathematics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120981.

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Chen, Xiaohui. "Lasso-type sparse regression and high-dimensional Gaussian graphical models." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42271.

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High-dimensional datasets, where the number of measured variables is larger than the sample size, are not uncommon in modern real-world applications such as functional Magnetic Resonance Imaging (fMRI) data. Conventional statistical signal processing tools and mathematical models could fail at handling those datasets. Therefore, developing statistically valid models and computationally efficient algorithms for high-dimensional situations are of great importance in tackling practical and scientific problems. This thesis mainly focuses on the following two issues: (1) recovery of sparse regressi
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6

He, Shiquan. "A Review of Linear Regression and some Basic Proofs for Lasso." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/88.

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The goal of this paper is to do some basic proofs for lasso and have a deep understanding of linear regression. In this paper, firstly I give a review of methods in linear regression, and most concerns with the method of lasso. Lasso for ¡®least absolute shrinkage and selection operator¡¯ is a regularized version of method adds a constraint which uses norm less or equal to a given value t. By doing so, some predictor coefficients would be shrank and some others might be set to 0. We can attain good interpretation and prediction accuracy by using lasso method. Secondly, I provide some basic pro
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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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8

Mo, Lili. "A class of operator splitting methods for least absolute shrinkage and selection operator (LASSO) models." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1391.

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9

Hashem, Hussein Abdulahman. "Regularized and robust regression methods for high dimensional data." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/9197.

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Recently, variable selection in high-dimensional data has attracted much research interest. Classical stepwise subset selection methods are widely used in practice, but when the number of predictors is large these methods are difficult to implement. In these cases, modern regularization methods have become a popular choice as they perform variable selection and parameter estimation simultaneously. However, the estimation procedure becomes more difficult and challenging when the data suffer from outliers or when the assumption of normality is violated such as in the case of heavy-tailed errors.
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10

Al-Kenani, Ali J. Kadhim. "Some statistical methods for dimension reduction." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7727.

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The aim of the work in this thesis is to carry out dimension reduction (DR) for high dimensional (HD) data by using statistical methods for variable selection, feature extraction and a combination of the two. In Chapter 2, the DR is carried out through robust feature extraction. Robust canonical correlation (RCCA) methods have been proposed. In the correlation matrix of canonical correlation analysis (CCA), we suggest that the Pearson correlation should be substituted by robust correlation measures in order to obtain robust correlation matrices. These matrices have been employed for producing
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11

Pelawa, Watagoda Lasanthi Chathurika Ranasinghe. "INFERENCE AFTER VARIABLE SELECTION." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1424.

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This thesis presents inference for the multiple linear regression model Y = beta_1 x_1 + ... + beta_p x_p + e after model or variable selection, including prediction intervals for a future value of the response variable Y_f, and testing hypotheses with the bootstrap. If n is the sample size, most results are for n/p large, but prediction intervals are developed that may increase in average length slowly as p increases for fixed n if the model is sparse: k predictors have nonzero coefficients beta_i where n/k is large.
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12

Meneghel, Danilevicz Ian. "Robust linear mixed models, alternative methods to quantile regression for panel data, and adaptive LASSO quantile regression with fixed effects." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST176.

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La thèse est constituée de trois chapitres. Le premier s'intéresse au lien entre l’exposition à la pollution de l’air et les affections respiratoires chez les enfants et les adolescents. La cohorte comprend 82 individus observés mensuellement pendant 6 mois. Nous proposons un modèle linéaire mixte robuste combiné à une analyse en composantes principales afin de gérer la multicolinéarité entre les covariables et l’impact des observations extrêmes sur les estimations. Le deuxième chapitre analyse des données de panel au moyen de modèles à effets fixes et utilisant différentes fonction de perte.
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13

Edin, Moa. "Outcome regression methods in causal inference : The difference LASSO and selection of effect modifiers." Thesis, Umeå universitet, Statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149423.

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In causal inference, a central aim of covariate selection is to provide a subset of covariates, that is sufficient for confounding adjustment. One approach for this is to construct a subset of covariates associated with the outcome. This is sometimes referred to as the outcome approach, which is the subject for this thesis. Apart from confounding, there may exist effect modification. This occurs when a treatment has different effect on the outcome, among different subgroups, defined by effect modifiers. We describe how the outcome approach implemented by regression models, can be used for esti
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14

Breheny, Patrick John. "Regularized methods for high-dimensional and bi-level variable selection." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/325.

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Many traditional approaches cease to be useful when the number of variables is large in comparison with the sample size. Penalized regression methods have proved to be an attractive approach, both theoretically and empirically, for dealing with these problems. This thesis focuses on the development of penalized regression methods for high-dimensional variable selection. The first part of this thesis deals with problems in which the covariates possess a grouping structure that can be incorporated into the analysis to select important groups as well as important members of those groups. I introd
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15

Vasquez, Monica M., and Monica M. Vasquez. "Penalized Regression Methods in the Study of Serum Biomarkers for Overweight and Obesity." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625637.

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The study of circulating biomarkers and their association with disease outcomes has become progressively complex due to advances in the measurement of these biomarkers through multiplex technologies. Although the availability of numerous serum biomarkers is highly promising, multiplex assays present statistical challenges due to the high dimensionality of these data. In this dissertation, three studies are presented that address these challenges using L1 penalized regression methods. In the first part of the dissertation, an extensive simulation study is performed for the logistic regression
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16

Ehrlinger, John M. "Regularization: Stagewise Regression and Bagging." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1300817082.

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Shi, Shujing. "Tuning Parameter Selection in L1 Regularized Logistic Regression." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2940.

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Variable selection is an important topic in regression analysis and is intended to select the best subset of predictors. Least absolute shrinkage and selection operator (Lasso) was introduced by Tibshirani in 1996. This method can serve as a tool for variable selection because it shrinks some coefficients to exact zero by a constraint on the sum of absolute values of regression coefficients. For logistic regression, Lasso modifies the traditional parameter estimation method, maximum log likelihood, by adding the L1 norm of the parameters to the negative log likelihood function, so it turns a
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18

Hu, Qing. "Predictor Selection in Linear Regression: L1 regularization of a subset of parameters and Comparison of L1 regularization and stepwise selection." Link to electronic thesis, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-051107-154052/.

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Strandberg, Rickard, and Johan Låås. "A comparison between Neural networks, Lasso regularized Logistic regression, and Gradient boosted trees in modeling binary sales." Thesis, KTH, Optimeringslära och systemteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252556.

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The primary purpose of this thesis is to predict whether or not a customer will make a purchase from a specific item category. The historical data is provided by the Nordic online-based IT-retailer Dustin. The secondary purpose is to evaluate how well a fully connected feed forward neural network performs as compared to Lasso regularized logistic regression and gradient boosted trees (XGBoost) on this task. This thesis finds XGBoost to be superior to the two other methods in terms of prediction accuracy, as well as speed.<br>Det primära syftet med denna uppsats är att förutsäga huruvida en
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20

Wang, Fan. "Penalised regression for high-dimensional data : an empirical investigation and improvements via ensemble learning." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289419.

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In a wide range of applications, datasets are generated for which the number of variables p exceeds the sample size n. Penalised likelihood methods are widely used to tackle regression problems in these high-dimensional settings. In this thesis, we carry out an extensive empirical comparison of the performance of popular penalised regression methods in high-dimensional settings and propose new methodology that uses ensemble learning to enhance the performance of these methods. The relative efficacy of different penalised regression methods in finite-sample settings remains incompletely underst
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21

SINGH, KEVIN. "Comparing Variable Selection Algorithms On Logistic Regression – A Simulation." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446090.

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When we try to understand why some schools perform worse than others, if Covid-19 has struck harder on some demographics or whether income correlates with increased happiness, we may turn to regression to better understand how these variables are correlated. To capture the true relationship between variables we may use variable selection methods in order to ensure that the variables which have an actual effect have been included in the model. Choosing the right model for variable selection is vital. Without it there is a risk of including variables which have little to do with the dependent va
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Miller, Ryan. "Marginal false discovery rate approaches to inference on penalized regression models." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6474.

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Data containing large number of variables is becoming increasingly more common and sparsity inducing penalized regression methods, such the lasso, have become a popular analysis tool for these datasets due to their ability to naturally perform variable selection. However, quantifying the importance of the variables selected by these models is a difficult task. These difficulties are compounded by the tendency for the most predictive models, for example those which were chosen using procedures like cross-validation, to include substantial amounts of noise variables with no real relationship wit
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Liu, Li. "Grouped variable selection in high dimensional partially linear additive Cox model." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/847.

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In the analysis of survival outcome supplemented with both clinical information and high-dimensional gene expression data, traditional Cox proportional hazard model fails to meet some emerging needs in biological research. First, the number of covariates is generally much larger the sample size. Secondly, predicting an outcome with individual gene expressions is inadequate because a gene's expression is regulated by multiple biological processes and functional units. There is a need to understand the impact of changes at a higher level such as molecular function, cellular component, biological
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Shah, Smit. "Comparison of Some Improved Estimators for Linear Regression Model under Different Conditions." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/1853.

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Multiple linear regression model plays a key role in statistical inference and it has extensive applications in business, environmental, physical and social sciences. Multicollinearity has been a considerable problem in multiple regression analysis. When the regressor variables are multicollinear, it becomes difficult to make precise statistical inferences about the regression coefficients. There are some statistical methods that can be used, which are discussed in this thesis are ridge regression, Liu, two parameter biased and LASSO estimators. Firstly, an analytical comparison on the basis o
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25

Vital, Clément. "Scoring pour le risque de crédit : variable réponse polytomique, sélection de variables, réduction de la dimension, applications." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S111.

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Le but de cette thèse était d'explorer la thématique du scoring dans le cadre de son utilisation dans le monde bancaire, et plus particulièrement pour contrôler le risque de crédit. En effet, la diversification et la globalisation des activités bancaires dans la deuxième moitié du XXe siècle ont conduit à l'instauration d'un certain nombre de régulations, afin de pouvoir s'assurer que les établissements bancaires disposent de capitaux nécessaires à couvrir le risque qu'ils prennent. Cette régulation impose ainsi la modélisation de certains indicateurs de risque, dont la probabilité de défaut,
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Jansson, Daniel, and Nils Niklasson. "En analys av statens samhällssatsningar och dess effektivitet för att reducera brottslighet." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275665.

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Through an analysis of the Swedish state budget, models have been developed to deepen the understanding of the effects that government expenditures have on reducing crime. This has been modeled by examining selected crime categories using the mathematical methods Ridge Regression, Lasso Regression and Principal Component Analysis. Combined with a qualitative study of previous research on the economic aspects of crime, an analysis has been conducted. The mathematical methods indicate that it may be more effective to invest in crime prevention measures, such as increased social protection and fo
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Zhai, Jing, Chiu-Hsieh Hsu, and Z. John Daye. "Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer." BIOMED CENTRAL LTD, 2017. http://hdl.handle.net/10150/622811.

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Background: Many questions in statistical genomics can be formulated in terms of variable selection of candidate biological factors for modeling a trait or quantity of interest. Often, in these applications, additional covariates describing clinical, demographical or experimental effects must be included a priori as mandatory covariates while allowing the selection of a large number of candidate or optional variables. As genomic studies routinely require mandatory covariates, it is of interest to propose principled methods of variable selection that can incorporate mandatory covariates. Method
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Hermann, Philipp [Verfasser], and Hajo [Akademischer Betreuer] Holzmann. "High-dimensional, robust, heteroscedastic variable selection with the adaptive LASSO, and applications to random coefficient regression / Philipp Hermann ; Betreuer: Hajo Holzmann." Marburg : Philipps-Universität Marburg, 2021. http://d-nb.info/1236692187/34.

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Marques, Matheus Augustus Pumputis. "Análise e comparação de alguns métodos alternativos de seleção de variáveis preditoras no modelo de regressão linear." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-23082018-210710/.

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Neste trabalho estudam-se alguns novos métodos de seleção de variáveis no contexto da regressão linear que surgiram nos últimos 15 anos, especificamente o LARS - Least Angle Regression, o NAMS - Noise Addition Model Selection, a Razão de Falsa Seleção - RFS (FSR em inglês), o LASSO Bayesiano e o Spike-and-Slab LASSO. A metodologia foi a análise e comparação dos métodos estudados e aplicações. Após esse estudo, realizam-se aplicações em bases de dados reais e um estudo de simulação, em que todos os métodos se mostraram promissores, com os métodos Bayesianos apresentando os melhores resultados.<
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30

Karlsson, Jonas, and Roger Karlsson. "Inkrementell responsanalys : Vilka kunder bör väljas vid riktad marknadsföring?" Thesis, Linköpings universitet, Statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-96593.

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If customers respond differently to a campaign, it is worthwhile to find those customers who respond most positively and direct the campaign towards them. This can be done by using so called incremental response analysis where respondents from a campaign are compared with respondents from a control group. Customers with the highest increased response from the campaign will be selected and thus may increase the company’s return. Incremental response analysis is applied to the mobile operator Tres historical data. The thesis intends to investigate which method that best explain the incremental r
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31

Aghi, Nawar, and Ahmad Abdulal. "House Price Prediction." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-20945.

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This study proposes a performance comparison between machine learning regression algorithms and Artificial Neural Network (ANN). The regression algorithms used in this study are Multiple linear, Least Absolute Selection Operator (Lasso), Ridge, Random Forest. Moreover, this study attempts to analyse the correlation between variables to determine the most important factors that affect house prices in Malmö, Sweden. There are two datasets used in this study which called public and local. They contain house prices from Ames, Iowa, United States and Malmö, Sweden, respectively.The accuracy of the
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Tian, Shaonan. "Essays on Corporate Default Prediction." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1352403546.

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PLAKSIENKO, ANNA. "Joint estimation of multiple graphical models." Doctoral thesis, Gran Sasso Science Institute, 2021. http://hdl.handle.net/20.500.12571/21632.

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The fast development of high-throughput technologies such as microarray or next-generation sequencing, and the consequent in-depth investigation of the genome in several international large scale projects, have led to the generation of large amounts of high-dimensional omics datasets. Scientists can use such data to acquire a deep understanding of complex cellular mechanisms, the molecular basis of diseases’ development, etc. Among other questions, relationships between different genes or other similar units can reveal regulatory mechanisms whose disruption can be associated with diseases. Net
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34

GALLI, FABIAN. "Predicting PV self-consumption in villas with machine learning." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300433.

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In Sweden, there is a strong and growing interest in solar power. In recent years, photovoltaic (PV) system installations have increased dramatically and a large part are distributed grid connected PV systems i.e. rooftop installations. Currently the electricity export rate is significantly lower than the import rate which has made the amount of self-consumed PV electricity a critical factor when assessing the system profitability. Self-consumption (SC) is calculated using hourly or sub-hourly timesteps and is highly dependent on the solar patterns of the location of interest, the PV system co
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Avgan, Nesli. "The genetic basis of human cognition: Intelligence, learning and memory." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/122903/1/Nesli_Avgan_Thesis.pdf.

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The complex and highly polygenic traits of intelligence, learning and memory are fundamental functions of neurocognition. Despite improvements in neurogenetics, our knowledge on the genetic architecture of these functions remains poorly understood. This research investigated the contribution of genetic variation to cognitive performance variability in relation to intelligence, learning and memory using a well-defined, healthy and unrelated cohort via a gene-centric and a genome-wide approach. Results of this study validated several previously identified genes, provided new knowledge on various
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Yu, Lili. "Variable selection in the general linear model for censored data." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1173279515.

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Meziani, Mohamed Aymen. "Estimation paramétrique et non-paramétrique en utilisant une approche de régression quantile." Electronic Thesis or Diss., Paris Est, 2019. http://www.theses.fr/2019PESC0084.

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Le quantile périodogramme développé par Li (2012) est une nouvelle approche qui fournit une information plus étendue et plus riche par rapport au périodogramme ordinaire. Cependant, il souffre d'instabilité de performance dans le cas de présence de plusieurs périodicités dominantes ou dans le cas de données bruitées. Cela est du à la fuite spectrale (Leakage) produite sous forme de piques supplémentaires. Afin de remédier à ce problème, une version régularisée du quantile périodogramme est proposée. Les propriétés asymptotiques du nouvel estimateur sont développées. De plus, des simulations ap
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38

Anderskär, Erika, and Frida Thomasson. "Inkrementell responsanalys av Scandnavian Airlines medlemmar : Vilka kunder ska väljas vid riktad marknadsföring?" Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139465.

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Scandinavian Airlines has a large database containing their Eurobonus members. In order to analyze which customers they should target with direct marketing, such as emails, uplift models have been used. With a binary response variable that indicates whether the customer has bought or not, and a binary dummy variable that indicates if the customer has received the campaign or not conclusions can be drawn about which customers are persuadable. That means that the customers that buy when they receive a campaign and not if they don't are spotted. Analysis have been done with one campaign for Swede
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Choiruddin, Achmad. "Sélection de variables pour des processus ponctuels spatiaux." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM045/document.

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Les applications récentes telles que les bases de données forestières impliquent des observations de données spatiales associées à l'observation de nombreuses covariables spatiales. Nous considérons dans cette thèse le problème de l'estimation d'une forme paramétrique de la fonction d'intensité dans un tel contexte. Cette thèse développe les procédures de sélection des variables et donne des garanties quant à leur validité. En particulier, nous proposons deux approches différentes pour la sélection de variables : les méthodes de type lasso et les procédures de type Sélecteur de Dantzig. Pour l
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40

Song, Song. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16341.

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In vielen Anwendungen ist es notwendig, die stochastische Schwankungen der maximalen Abweichungen der nichtparametrischen Schätzer von Quantil zu wissen, zB um die verschiedene parametrische Modelle zu überprüfen. Einheitliche Konfidenzbänder sind daher für nichtparametrische Quantil Schätzungen der Regressionsfunktionen gebaut. Die erste Methode basiert auf der starken Approximation der empirischen Verfahren und Extremwert-Theorie. Die starke gleichmäßige Konsistenz liegt auch unter allgemeinen Bedingungen etabliert. Die zweite Methode beruht auf der Bootstrap Resampling-Verfahren. Es ist bew
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41

Casagrande, Marcelo Henrique. "Comparação de métodos de estimação para problemas com colinearidade e/ou alta dimensionalidade (p > n)." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/7954.

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Submitted by Bruna Rodrigues (bruna92rodrigues@yahoo.com.br) on 2016-10-06T11:48:12Z No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T13:58:41Z (GMT) No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T13:58:47Z (GMT) No. of bitstreams: 1 DissMHC.pdf: 1077783 bytes, checksum: c81f777131e6de8fb219b8c34c4337df (MD5)<br>Made a
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Sawert, Marcus. "Predicting deliveries from suppliers : A comparison of predictive models." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39314.

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In the highly competitive environment that companies find themselves in today, it is key to have a well-functioning supply chain. For manufacturing companies, having a good supply chain is dependent on having a functioning production planning. The production planning tries to fulfill the demand while considering the resources available. This is complicated by the uncertainties that exist, such as the uncertainty in demand, in manufacturing and in supply. Several methods and models have been created to deal with production planning under uncertainty, but they often overlook the complexity in th
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Wilhelmsson, Kasper, and Ludvig Kroon. "En analys av bränslefraktioners påverkan på ett kraftverks emissioner." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149044.

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Tekniska verken är en regional koncern som verkar inom många områden. Den här rapporten specificerar sig på avfallshantering och emissionerna av dessa. Tekniska verken har som mål att bli så miljövänliga som möjligt och med hjälp av denna rapport få en bättre insikt i vilka avfall som är bättre och sämre för miljön. Rapporten använder statistiska metoder för att visa vilka avfall eller bränslen som ger upphov till höga eller låga halter av farliga emissioner samt vilka av dem som har högt respektive lågt energiinnehåll. Metoder som används är Lasso-regression och ko
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Kim, Byung-Jun. "Semiparametric and Nonparametric Methods for Complex Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99155.

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A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect
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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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Taha, May. "Probing sequence-level instructions for gene expression." Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTT096/document.

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La régulation des gènes est fortement contrôlée afin d’assurer une large variété de types cellulaires ayant des fonctions spécifiques. Ces contrôles prennent place à différents niveaux et sont associés à différentes régions génomiques régulatrices. Il est donc essentiel de comprendre les mécanismes à la base des régulations géniques dans les différents types cellulaires, dans le but d’identifier les régulateurs clés. Plusieurs études tentent de mieux comprendre les mécanismes de régulation en modulant l’expression des gènes par des approches épigénétiques. Cependant, ces approches sont basées
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Lundberg, Jacob. "Resource Efficient Representation of Machine Learning Models : investigating optimization options for decision trees in embedded systems." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162013.

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Combining embedded systems and machine learning models is an exciting prospect. However, to fully target any embedded system, with the most stringent resource requirements, the models have to be designed with care not to overwhelm it. Decision tree ensembles are targeted in this thesis. A benchmark model is created with LightGBM, a popular framework for gradient boosted decision trees. This model is first transformed and regularized with RuleFit, a LASSO regression framework. Then it is further optimized with quantization and weight sharing, techniques used when compressing neural networks. Th
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Jung, Nicolas. "Modélisation de phénomènes biologiques complexes : application à l'étude de la réponse antigénique de lymphocytes B sains et tumoraux." Thesis, Strasbourg, 2014. http://www.theses.fr/2014STRAJ067/document.

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La biologie des systèmes complexes est le cadre idéal pour l'interdisciplinarité. Dans cette thèse, les modèles et les théories statistiques répondent aux modèles et aux expérimentations biologiques. Nous nous sommes intéressés au cas particulier de la leucémie lymphoïde chronique à cellules B, qui est une forme de cancer des cellules du sang. Nous avons commencé par modéliser le programme génique tumoral sous-jacent à cette maladie et nous l'avons comparé au programme génique d'individus sains. Pour ce faire, nous avons introduit la notion de réseau en cascade. Nous avons ensuite démontré not
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Stasinakis, Andreas. "Analyzing the effect of competition in the hospitality industry." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166053.

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Maximizing hotel's revenue is a hard and complicated task. A lot of aspects have to be taken into consideration during this procedure. One major variable is the competitors. Identifying hotel's competitors and using their behavior could be a crucial advantage for maximizing hotel's revenue. For this reason, in this thesis the effect of competitors' pricing in the demand forecasting is being studied. Five hotels with approximately 13 months of historical data are available. During the study two GLM models were tested, Poisson and Negative Binomial regression. As a baseline, the models were mode
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Rahman, Md Abdur. "Statistical and Machine Learning for assessment of Traumatic Brain Injury Severity and Patient Outcomes." Thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37710.

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Traumatic brain injury (TBI) is a leading cause of death in all age groups, causing society to be concerned. However, TBI diagnostics and patient outcomes prediction are still lacking in medical science. In this thesis, I used a subset of TBIcare data from Turku University Hospital in Finland to classify the severity, patient outcomes, and CT (computerized tomography) as positive/negative. The dataset was derived from the comprehensive metabolic profiling of serum samples from TBI patients. The study included 96 TBI patients who were diagnosed as 7 severe (sTBI=7), 10 moderate (moTBI=10), and
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