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

## Probabilidade do erro do tipo I nas cartas X e S de Shewhart sob não normalidade; Probability of type I error in X and S Shewhart Control Charts under non-normality

## An empirical power comparison of univariate goodness-of-fit tests for normality

## A Two One-Sided Parametric Tolerance Interval Test for Control of Delivered Dose Uniformity—Part 3—Investigation of Robustness to Deviations from Normality

## Estimating Latent Variable Interactions With Non-Normal Observed Data: A Comparison of Four Approaches

## Power and Sample Size Determination in the Rasch Model: Evaluation of the Robustness of a Numerical Method to Non-Normality of the Latent Trait

## Detecting ARCH Effects in Non-Gaussian Time Series

## Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

## Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

## The effects of non-normality and nonlinearity of the Navier–Stokes operator on the dynamics of a large laminar separation bubble

## Logique et tests d'hypotheses: reflexions sur les problemes mal poses en econometrie

## Bayesian test of normality versus a Dirichlet process mixture alternative

## Non-normality in combustion-acoustic interaction in diffusion flames: a critical revision

## Filtered overlap: speedup, locality, kernel non-normality and Z_A~1

## Multivariate Non-Normality in the WMAP 1st Year Data

## Do probabilistic medium-range temperature forecasts need to allow for non-normality?

## The Consequences of Non-Normality

## Non-normal and Stochastic Amplification in Turbulent Dynamo: Subcritical Case

## Uniaxial Tension of a Class of Compressible Solids With Plastic Non-Normality

## Bayesian Modeling and Computation for Mixed Data

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.

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.

To this end...