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 Sweden and Scandinavia. The methods that have been used are logistic regression with Lasso and logistic regression with Penalized Net Information Value. The best method for predicting purchases is Lasso regression when comparing with a confusion matrix. The variable that best describes persuadable customers in logistic regression with PNIV is Flown (customers that have own with SAS within the last six months). In Lassoregression the variable that describes a persuadable customer in Sweden is membership level1 (the rst level of membership) and in Scandinavia customers that receive campaigns with delivery code 13 are persuadable, which is a form of dispatch.

In this thesis the heterogeneity of regional real estate prices in United States is investigated. A high dimensional VAR model with additional exogenous predictors, originally introduced by \cite{fan11}, is adopted. In this framework, the common factor in regional house prices dynamics is explained by exogenous predictors and the spatial dependencies are captured by lagged house prices in other regions. For the purpose of estimation and variable selection under high-dimensional setting the concept of Penalized Least Squares (PLS) with different penalty functions (e.g. LASSO penalty) is studied in detail and implemented. Moreover, clustering methods are employed to identify subsets of statistical regions with similar house prices dynamics. It is demonstrated that these clusters are well geographically defined and contribute to a better interpretation of the VAR model. Next, we make use of the LASSO variable selection property in order to construct the impulse response functions and to simulate the prices behavior when a shock occurs. And last but not least, one-period-ahead forecasts from VAR model are compared to those from the Diffusion Index Factor Model by \cite{stock02}, a commonly used model for forecasts.

Orientador: Carlos Fernando Fiorini / "O segundo volume apresenta uma nova edição, crítica e revisada, de Lagrime di San Pietro" / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Artes / Made available in DSpace on 2018-08-22T03:24:23Z (GMT). No. of bitstreams: 2 Popolin_DanielaFrancineLino_D.pdf: 30668844 bytes, checksum: 792422ea820dade8c485cb8c13f1f408 (MD5) Popolin_DanielaFrancineLino_D_ANEXO.zip: 1638597 bytes, checksum: 643d2c51b49b32f196e1cd7ab01bbb9c (MD5) Previous issue date: 2013 / Resumo: Lagrime di San Pietro é uma das obras mais relevantes dentro da vasta produção de Orlando di Lasso, composta por um conjunto de vinte Madrigais Espirituais e um Moteto. Esta tese teve como principal objetivo um estudo sobre a preparação e a execução desta obra sob a ótica do regente, embasado por um conjunto de pesquisas adjacentes que proporcionassem um suporte teórico, como análise musical, textual e a relação entre ambos, contextualização histórica da obra e do compositor, além do levantamento das dificuldades encontradas durante o processo de execução da obra, partindo da aplicação prática da proposta apresentada. Devido à dificuldade de se encontrar uma edição ideal, foi necessário confeccionar uma nova partitura da obra que pudesse ser utilizada tanto como base para esse estudo como para a sua execução. Assim, o segundo volume apresenta uma nova edição, crítica e revisada, de Lagrime di San Pietro / Abstract: Lagrime di San Pietro is among the most significant works in the huge production of Orlando di Lasso, consisting of a set of twenty Spiritual Madrigals and a Motet. This thesis aimed to a study on the preparation and performance of the work from the conductor's perspective, based on a set of adjacent researches that provide a theoretical foundation and practical implementation of the proposals, such as musical and textual analysis and the relationship between both, a historical background of the work and the composer, and a survey of the difficulties found during the performance of the work, based on the implementation of the proposal. Given the difficulty of finding a right edition, it was necessary to prepare a new score of the work which could be used both as the basis for this study and for its performance. Thus, the second volume presents a new reviewed and critical edition of Lagrime di San Pietro / Doutorado / Praticas Interpretativas / Doutora em Música

Dengue fever affects over 390 million people annually worldwide and is of particu- lar concern in Southeast Asia where it is one of the leading causes of hospitalization. Modeling trends in dengue occurrence can provide valuable information to Public Health officials, however many challenges arise depending on the data available. In Thailand, reporting of dengue cases is often delayed by more than 6 weeks, and a small fraction of cases may not be reported until over 11 months after they occurred. This study shows that incorporating data on Google Search trends can improve dis- ease predictions in settings with severely underreported data. We compare penalized regression approaches to seasonal baseline models and illustrate that incorporation of search data can improve prediction error. This builds on previous research show- ing that search data and recent surveillance data together can be used to create accurate forecasts for diseases such as influenza and dengue fever. This work shows that even in settings where timely surveillance data is not available, using search data in real-time can produce more accurate short-term forecasts than a seasonal baseline prediction. However, forecast accuracy degrades the further into the future the forecasts go. The relative accuracy of these forecasts compared to a seasonal average forecast varies depending on location. Overall, these data and models can improve short-term public health situational awareness and should be incorporated into larger real-time forecasting efforts.

With the increasing demand and interest in wind power worldwide, it is interesting to study the effects of running windfarms on the activity of reindeers and estimate the associated Zone of Influence (ZOI) relative to these disturbances. Through simulation, Hierarchical Likelihood (HL) and adaptive Lasso methods are used to estimate the ZOI of windfarms and catching the correct threshold at which the negative effect of the disturbances on the reindeer behaviour disappears. The results found some merit to the explanation that the negative effect may not disappear abruptly and more merit to the fact that a linear model was still a better choice than the smooth polynomial models used. A real-life data related to reindeer faecal pellet counts from an area in northern Sweden were windfarms were running were analyzed. The yearly time series data was divided into three periods : before construction, during construction and during operation of the windfarms. Logistic regression, segmented model, and HL methods were implemented for data analysis by using covariates as distance from wind turbine, vegetation type, the interaction between distance to wind turbine and time period. A significant breakpoint could be estimated using the segmented model at a distance of 2.8 km from running windfarm, after which the negative effects of the windfarm on the reindeer activity disappeared. However, further work is needed for estimation of ZOI using HL method and considering other possible factors causing disturbances to the reindeer habitat and behaviour.

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 the supply uncertainty, by considering it as a stochastic uncertainty. To improve these models, a prediction based on earlier data regarding the supplier or item could be used to see when the delivery is likely to arrive. This study looked to compare different predictive models to see which one could best be suited for this purpose. Historic data regarding earlier deliveries was gathered from a large international manufacturing company and was preprocessed before used in the models. The target value that the models were to predict was the actual delivery time from the supplier. The data was then tested with the following four regression models in Python: Linear regression, ridge regression, Lasso and Elastic net. The results were calculated by cross-validation and presented in the form of the mean absolute error together with the standard deviation. The results showed that the Elastic net was the overall best performing model, and that the linear regression performed worst.

MSc (Statistics) / Department of Statistics / Selection of variables is vital in high dimensional statistical modelling as it aims to identify the right subset model. However, variable selection for discrete survival analysis poses many challenges due to a complicated data structure. Survival data might have unobserved heterogeneity leading to biased estimates when not taken into account. Conventional variable selection methods have stability problems. A simulation approach was used to assess and compare the performance of Least Absolute Shrinkage and Selection Operator (Lasso) and gradient boosting on discrete survival data. Parameter related mean squared errors (MSEs) and false positive rates suggest Lasso performs better than gradient boosting. Frailty models outperform discrete survival models that do not account for unobserved heterogeneity. The two methods were also applied on Zimbabwe Demographic Health Survey (ZDHS) 2016 data on age at first marriage and did not select exactly the same variables. Gradient boosting retained more variables into the model. Place of residence, highest educational level attained and age cohort are the major influential factors of age at first marriage in Zimbabwe based on Lasso. / NRF

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 prediction is evaluated by checking the root square and root mean square error scores of the training model. The test is performed after applying the required pre-processing methods and splitting the data into two parts. However, one part will be used in the training and the other in the test phase. We have also presented a binning strategy that improved the accuracy of the models.This thesis attempts to show that Lasso gives the best score among other algorithms when using the public dataset in training. The correlation graphs show the variables' level of dependency. In addition, the empirical results show that crime, deposit, lending, and repo rates influence the house prices negatively. Where inflation, year, and unemployment rate impact the house prices positively.

Previous research has tested the lasso method for DIF detection in dichotomous items, but limited research is available on this technique for polytomous items. This simulation study compares the lasso method to hybrid ordinal logistic regression to test performance in terms of TP and FP rates when considering sample size, test length, number of response categories, group balance, DIF proportion, and DIF magnitude. Results showed better Type I error control with the lasso, with smaller sample sizes, unbalanced groups, and weak DIF. The lasso also exhibited more stable Type I error control when DIF was weak, and groups were unbalanced. Lastly, low DIF proportion contributed to better Type I error control and higher TP rates with both methods.

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