# A melhor ferramenta para a sua pesquisa, trabalho e TCC!

- Escola de Pós-Graduação em Economia da FGV
- European University Institute
- Instituto Universitário Europeu
- Elsevier Science Bv
- Universidade Carlos III de Madrid
- Monterey, California. Naval Postgraduate School
- Université de Montréal
- Universidade de São Paulo, Faculdade de Economia, Administração e Contabilidade, Departamento de Contabilidade e Atuária
- Faculty of Economics, University of Cambridge, UK
- Universidade Duke
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## Simulation-based smoothing and filtering in factor stochastic volatility models : two econometric applications

## Using irregularly spaced returns to estimate multi-factor models : application to Brazilian equity data

## Factor-Augmented Error Correction Models

## Dynamic factor models in estimation and forecasting

## Forecasting with Factor-augmented Error Correction Models

## Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models

## Factor Augmented Error Correction Models

## Forecasting with factor-augmented error correction models

## Detecting big structural breaks in large factor models

## Essays in high dimensional factor models

## Latent Factor Models and Analyses for Operator Response Times

## Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP

## Dynamic Factor Models in Macro-finance

## Latent Variable Models for Stochastic Discount Factors.

## Factor models, VARMA processes and parameter instability with applications in macroeconomics

## The Earnings/Price Risk Factor in Capital Asset Pricing Models

## Infinite Dimensional VARs and Factor Models

## Factor Models to Describe Linear and Non-linear Structure in High Dimensional Gene Expression Data

An important problem in the analysis of gene expression data is the identification of groups of features that are coherently expressed. For example, one often wishes to know whether a group of genes, clustered because of correlation in one data set, is still highly co-expressed in another data set. For some microarray platforms there are many, relatively short, probes for each gene of interest. In this case, it is possible that a given probe is not measuring its targeted transcript, but rather a different gene with a similar region (called cross-hybridization). Similarly, the incorrect mapping of short nucleotide sequences to a target gene is a common issue related to the young technology producing RNA-Seq data. The expression pattern across samples is a valuable source of information, which can be used to address distinct problems through the application of factor models. Our first study is focused on the identification of the presence/absence status of a gene in a sample. We compare our factor model to state-of-the-art detection methods; the results suggest superior performance of the factor analysis for detecting transcripts. In the second study, we apply factor models to investigate gene modules (groups of coherently expressed genes). Variation in the number of copies of regions of the genome is a well known and important feature of most cancers. Copy number alteration is detected for a group of genes in breast cancer; our goal is to examine this abnormality in the same chromosomal region for other types of tumors (Ovarian...

## Bayesian Semi-parametric Factor Models

Identifying a lower-dimensional latent space for representation of high-dimensional observations is of significant importance in numerous biomedical and machine learning applications. In many such applications, it is now routine to collect data where the dimensionality of the outcomes is comparable or even larger than the number of available observations. Motivated in particular by the problem of predicting the risk of impending diseases from massive gene expression and single nucleotide polymorphism profiles, this dissertation focuses on building parsimonious models and computational schemes for high-dimensional continuous and unordered categorical data, while also studying theoretical properties of the proposed methods. Sparse factor modeling is fast becoming a standard tool for parsimonious modeling of such massive dimensional data and the content of this thesis is specifically directed towards methodological and theoretical developments in Bayesian sparse factor models.

The first three chapters of the thesis studies sparse factor models for high-dimensional continuous data. A class of shrinkage priors on factor loadings are introduced with attractive computational properties, with operating characteristics explored through a number of simulated and real data examples. In spite of the methodological advances over the past decade...

## On Bayesian Analyses of Functional Regression, Correlated Functional Data and Non-homogeneous Computer Models

Current frontiers in complex stochastic modeling of high-dimensional processes include major emphases on so-called functional data: problems in which the data are snapshots of curves and surfaces representing fundamentally important scientific quantities. This thesis explores new Bayesian methodologies for functional data analysis.

The first part of the thesis places emphasis on the role of factor models in functional data analysis. Data reduction becomes mandatory when dealing with such high-dimensional data, more so when data are available on a large number of individuals. In Chapter 2 we present a novel Bayesian framework which employs a latent factor construction to represent each variable by a low dimensional summary. Further, we explore the important issue of modeling and analyzing the relationship of functional data with other covariate and outcome variables simultaneously measured on the same subjects.

The second part of the thesis is concerned with the analysis of circadian data. The focus is on the identification of circadian genes that is, genes whose expression levels appear to be rhythmic through time with a period of approximately 24 hours. While addressing this goal, most of the current literature does not account for the potential dependence across genes. In Chapter 4...