Tese (doutorado)—Universidade de Brasília, Universidade Federal da Paraíba, Universidade Federal do Rio Grande do Norte, Programa Multi-Institucional e Inter-Regional de Pós-Graduação em Ciências Contábeis, 2017. / Submitted by Raquel Almeida (raquel.df13@gmail.com) on 2018-02-27T16:30:48Z No. of bitstreams: 1 2017_AlineMouraCostadaSilva.pdf: 2061144 bytes, checksum: 7bbb473ff7ffbaef08720ac9941667bf (MD5) / Approved for entry into archive by Raquel Viana (raquelviana@bce.unb.br) on 2018-03-14T19:26:27Z (GMT) No. of bitstreams: 1 2017_AlineMouraCostadaSilva.pdf: 2061144 bytes, checksum: 7bbb473ff7ffbaef08720ac9941667bf (MD5) / Made available in DSpace on 2018-03-14T19:26:27Z (GMT). No. of bitstreams: 1 2017_AlineMouraCostadaSilva.pdf: 2061144 bytes, checksum: 7bbb473ff7ffbaef08720ac9941667bf (MD5) Previous issue date: 2018-03-14 / Esta tese teve por objetivo desenvolver um modelo econométrico estrutural para o mercado acionário brasileiro, de modo a explicar a determinação dos retornos de suas ações, por meio de uma modelagem denominada MIDAS. Para tal, foram utilizadas variáveis explanatórias que sintetizam as especificidades das empresas analisadas, assim como do ambiente econômico brasileiro. Com o propósito de realizar um teste de robustez do modelo MIDAS desenvolvido, um modelo de regressão convencional para dados em painel também foi estimado com as mesmas variáveis presentes naquele modelo. Posteriormente, buscou-se analisar as projeções dos retornos acionários desenvolvidas pelo modelo MIDAS, comparando-as com as projeções advindas do modelo convencional e da série histórica. Carteiras de ativos foram montadas com base no modelo MIDAS, ainda com o intuito de analisar as suas projeções. A amostra contemplou as instituições não financeiras listadas na BM&FBovespa (atual B3) e o período de análise compreendeu de 2010 a 2016. Os resultados indicaram que o modelo MIDAS desenvolvido nesta tese se mostrou robusto para a explicação e projeção dos retornos trimestrais das ações listadas no mercado acionário brasileiro, permitindo, inclusive, a construção de carteiras de ativos para investimento. Esse modelo superou o modelo convencional para dados em painel na explicação dos retornos acionários e, no que tange à projeção dos retornos das ações, o modelo MIDAS mostrou-se mais preciso estatisticamente do que a média histórica. Os resultados apresentados nesta tese reforçam a importância de estudos relacionados à modelagem dos retornos acionários em mercados emergentes, ao desenvolver um modelo robusto para a análise e a tomada de decisões de investimento no Brasil, o que corrobora para uma melhor compreensão e desenvolvimento de seu mercado acionário. / The purpose of this thesis was to develop a structural econometric model for the Brazilian stock market, in order to explain the determination of the returns of its shares, utilizing a model known as MIDAS. To accomplish that, explanatory variables that synthesize the fundamentals of the companies analyzed and other variables associated with the Brazilian economic environment were included. In order to perform a robustness test of the MIDAS model proposed, a conventional panel data regression model was also estimated with the same variables included in the first model. Subsequently, we sought to analyze stock return forecasts generated by the MIDAS model, by comparing them with forecasts generated by the conventional model and with the historical series as well. Asset portfolios were built based on the MIDAS model, also with the purpose of analyzing its forecasts. The sample includes the non-financial institutions listed on the BM&FBovespa (current B3) within the period comprised from 2010 to 2016. The results indicate that the MIDAS model developed in this thesis is robust for explaining and forecasting the quarterly returns of shares listed in the stock market including the construction of investment portfolios. This model overcomes the conventional panel data model in explaining stock returns and, regarding the forecasting of stock returns, the MIDAS model was also statistically more robust than the historical average. The results presented in this thesis strengthen the importance of studies related to the modeling of stock returns in emerging markets, by developing a robust model for investment analysis and decision-making in Brazil, which contributes to a better understanding and development of its stock market.

Clustering is an important analytical technique that has proven to affect human life positively through its application in cancer research, market segmentation, city planning etc. In this time of growing technological systems, mixed data has seen another face of longitudinal, directional and functional attributes which is worth paying attention to and analyzing. Previous research works on clustering relied largely on the inverse weight technique and B-spline in smoothing data and assessing the performance of various clustering algorithms. In 1971, Gower proposed a method of clustering for mixed variable types which has been extended to include functional and directional variables by Hendrickson (2014). In this study, we will do a comparative analysis of the performance of the hierarchical clustering mechanism using a simulated Functional data with mixed structure. We will adopt the Fourier basis smoothing procedure and use the Rand index (Rand 1971) and adjusted Rand index for the comparison of the various clustering algorithms.

<p>Multivariate or high-dimensional data with mixed types are ubiquitous in many fields of studies, including science, engineering, social science, finance, health and medicine, and joint analysis of such data entails both statistical models flexible enough to accommodate them and novel methodologies for computationally efficient inference. Such joint analysis is potentially advantageous in many statistical and practical aspects, including shared information, dimensional reduction, efficiency gains, increased power and better control of error rates.</p><p>This thesis mainly focuses on two types of mixed data: (i) mixed discrete and continuous outcomes, especially in a dynamic setting; and (ii) multivariate or high dimensional continuous data with potential non-normality, where each dimension may have different degrees of skewness and tail-behaviors. Flexible Bayesian models are developed to jointly model these types of data, with a particular interest in exploring and utilizing the factor models framework. Much emphasis has also been placed on the ability to scale the statistical approaches and computation efficiently up to problems with long mixed time series or increasingly high-dimensional heavy-tailed and skewed data.</p><p>To this end, in Chapter 1, we start with reviewing the mixed data challenges. We start developing generalized dynamic factor models for mixed-measurement time series in Chapter 2. The framework allows mixed scale measurements in different time series, with the different measurements having distributions in the exponential family conditional on time-specific dynamic latent factors. Efficient computational algorithms for Bayesian inference are developed that can be easily extended to long time series. Chapter 3 focuses on the problem of jointly modeling of high-dimensional data with potential non-normality, where the mixed skewness and/or tail-behaviors in different dimensions are accurately captured via the proposed heavy-tailed and skewed factor models. Chapter 4 further explores the properties and efficient Bayesian inference for the generalized semiparametric Gaussian variance-mean mixtures family, and introduce it as a potentially useful family for modeling multivariate heavy-tailed and skewed data.</p> / Dissertation

Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. Information visualization provides methods for visual analysis of complex data but, as the amounts of gathered data increase, the challenges of visual analysis become more complex. This thesis presents work utilizing algorithmically extracted patterns as guidance during interactive data exploration processes, employing information visualization techniques. It provides efficient analysis by taking advantage of fast pattern identification techniques as well as making use of the domain expertise of the analyst. In particular, the presented research is concerned with the issues of analysing categorical data, where the values are names without any inherent order or distance; mixed data, including a combination of categorical and numerical data; and high dimensional data, including hundreds or even thousands of variables. The contributions of the thesis include a quantification method, assigning numerical values to categorical data, which utilizes an automated method to define category similarities based on underlying data structures, and integrates relationships within numerical variables into the quantification when dealing with mixed data sets. The quantification is incorporated in an interactive analysis pipeline where it provides suggestions for numerical representations, which may interactively be adjusted by the analyst. The interactive quantification enables exploration using commonly available visualization methods for numerical data. Within the context of categorical data analysis, this thesis also contributes the first user study evaluating the performance of what are currently the two main visualization approaches for categorical data analysis. Furthermore, this thesis contributes two dimensionality reduction approaches, which aim at preserving structure while reducing dimensionality, and provide flexible and user-controlled dimensionality reduction. Through algorithmic quality metric analysis, where each metric represents a structure of interest, potentially interesting variables are extracted from the high dimensional data. The automatically identified structures are visually displayed, using various visualization methods, and act as guidance in the selection of interesting variable subsets for further analysis. The visual representations furthermore provide overview of structures within the high dimensional data set and may, through this, aid in focusing subsequent analysis, as well as enabling interactive exploration of the full high dimensional data set and selected variable subsets. The thesis also contributes the application of algorithmically guided approaches for high dimensional data exploration in the rapidly growing field of microbiology, through the design and development of a quality-guided interactive system in collaboration with microbiologists.

Available statistical methodologies focus more on accommodating continuous variables, however recently dealing with count data has received high interest in the statistical literature. In this dissertation, we propose some statistical approaches to investigate linear and nonlinear dependencies between two discrete random variables, or between a discrete and continuous random variables. Copula functions are powerful tools for modeling dependencies between random variables. We derive copula-based population version of Spearman’s rho when at least one of the marginal distribution is discrete. In each case, the functional relationship between Kendall’s tau and Spearman’s rho is obtained. The asymptotic distributions of the proposed estimators of these association measures are derived and their corresponding confidence intervals are constructed, and tests of independence are derived. Then, we propose a Bayesian copula factor autoregressive model for time series mixed data. This model assumes conditional independence and shares latent factors in both mixed-type response and multivariate predictor variables of the time series through a quadratic timeseries regression model. This model is able to reduce the dimensionality by accommodating latent factors in both response and predictor variables of the high-dimensional time series data. A semiparametric time series extended rank likelihood technique is applied to the marginal distributions to handle mixed-type predictors of the high-dimensional time series, which decreases the number of estimated parameters and provides an efficient computational algorithm. In order to update and compute the posterior distributions of the latent factors and other parameters of the models, we propose a naive Bayesian algorithm with Metropolis-Hasting and Forward Filtering Backward Sampling methods. We evaluate the performance of the proposed models and methods through simulation studies. Finally, each proposed model is applied to a real dataset.

One of the concerns in the field of statistics is the presence of missing data, which leads to bias in parameter estimation and inaccurate results. However, the multiple imputation procedure is a remedy for handling missing data. This study looked at the best multiple imputation methods used to handle mixed variable datasets with different sample sizes and variability along with different levels of missingness. The study employed the predictive mean matching, classification and regression trees, and the random forest imputation methods. For each dataset, the multiple regression parameter estimates for the complete datasets were compared to the multiple regression parameter estimates found with the imputed dataset. The results showed that the random forest imputation method was the best for mostly a sample of 150 and 500 irrespective of the variability. The classification and regression tree imputation methods worked best mostly on sample of 30 irrespective of the variability.

Sorting out data into partitions is increasing becoming complex as the constituents of data is growing outward everyday. Mixed data comprises continuous, categorical, directional functional and other types of variables. Clustering mixed data is based on special dissimilarities of the variables. Some data types may influence the clustering solution. Assigning appropriate weight to the functional data may improve the performance of the clustering algorithm. In this paper we use the extension of the Gower coefficient with judciously chosen weight for the L2 to cluster mixed data.The benefits of weighting are demonstrated both in in applications to the Buoy data set as well simulation studies. Our studies show that clustering algorithms with application of proper weight give superior recovery level when a set of data with mixed continuous, categorical directional and functional attributes is clustered. We discuss open problems for future research in clustering mixed data.

Clustering is a widely used technique in data mining applications to source, manage, analyze and extract vital information from large amounts of data. Most clustering procedures are limited in their performance when it comes to data with mixed attributes. In recent times, mixed data have evolved to include directional and functional data. In this study, we will give an introduction to clustering with an eye towards the application of the extended Gower coefficient by Hendrickson (2014). We will conduct a simulation study to assess the performance of this coefficient on mixed data whose functional component has strictly-decreasing signal curves and also those whose functional component has a mixture of strictly-decreasing signal curves and periodic tendencies. We will assess how four different hierarchical clustering algorithms perform on mixed data simulated under varying conditions with and without weights. The comparison of the various clustering solutions will be done using the Rand Index.

Motivated by real-time monitoring of dialysis, we aim at detecting difference between groups of Raman spectra generated from dialyzates at different time in one session. Baseline correction being a critical procedure in use of Raman Spectra, existing methods may not perform well on dialysis spectra due to nature of dialyzates, which contain numerous chemicals compounds. We first developed a new baseline correction method, Iterative Smoothing-spline with Root Error Adjustment (ISREA), which automatically adjusts intensities and employs smoothing-spline to produce a baseline in each iteration, providing better performance on dialysis spectra than a popular method Goldindec, and better accuracy regardless of types of samples. We proposed a two sample hypothesis testing on groups of baseline-corrected Raman spectra with ISREA. The uniqueness of the test lies in nature of the tested data. Instead of using Raman spectra as curves, we also consider a vector whose elements are peak intensities of biomarkers, meaning the data is regarded as mixed data and that a spectrum curve and a vector compose one observation. Our method tests on equality of the means of the two groups of mixed data. This method is based on asymptotic properties of the covariance of mixed data and FPCA. Simulation studies shows that our method is applicable to small sample size with proper power and size control. Meanwhile, to locate regions that contribute most to significant difference between two groups of univariate functional data, we developed a method to estimate the a sparse coefficient function by using a L1 norm penalty in functional logistic regression, and compared its performance with other methods. / Doctor of Philosophy / In U.S., there are more than 709,501 patients with End-Stage Renal Disease (ESRD). For those patients, dialysis is a standard treatment. While dialysis is time-consuming, expensive, and uncomfortable, it requires patients to take three sessions every week in facilities, and each session lasts for four hours regardless of patients' condition. An affordable, fast, and widely-applied technique called Raman spectroscopy draws attention. Spectral data from used dialysate samples collected at different time in one session can give information on the dialysis process and thus make real-time monitoring possible. With spectral data, we want to develop a statistical method that helps real-time monitoring on dialysis. This method can provide physicians with statistical evidence on dialysis process to improve their decision making, therefore increases efficiency of dialysis and better serve patients. On the other hand, Raman spectroscopy demands preprocessing called baseline correction on the raw spectra. A baseline is generated because of the nature of Raman technique and its instrumentation, which adds complexity to the spectra and interfere with analysis. Despite popularity of this technique and many existing baseline correction method, we found performance on dialysate spectra under expectation. Hence, we proposed a baseline correction method called Iterative Smoothing-spline with Root Error Adjustment (ISREA) and ISREA can provide better performance than existing methods. In addition, we come up with a method that is able to detect difference between the two groups of ISREA baseline-corrected spectra from dialysate collected at different time. Furthermore, we proposed and applied sparse functional logistic regression on two groups to locate regions where the significant difference comes from.

With the introduction of the UK Government’s inclusive ideology in the late 20th century, increased pressure was put on schools serving adolescent psychiatric units to support their pupils to return to mainstream education. However, there is a perception that a factor that makes the transition process difficult is the attitude of mainstream pupils towards their peers with mental health problems. The purpose of this research was to explore mainstream pupils’ perceptions of mental health problems and the extent to which their understandings might lead to stigmatising attitudes. A theoretical perspective encompassing the ideas of social constructionism, interpretavism and symbolic interactionism, combined with a linguistic based approach, underpinned the development of an empathetic methodological approach to researching sensitive topics with adolescents. The research involved collecting data using a sequence of questionnaires, individual interviews and group interviews with pupils in three secondary schools within socially diverse communities. The questionnaire was presented in comic booklet form and included such techniques as cartoons, vignettes, and adapted familiarity and social distance scales. This dissertation reveals ways in which young people create their personal constructs around mental health and the complexities of the nature of stigma. It also highlights the implications that these findings have for staff and pupils involved in the transition process and for the development of practice in this field.