This work describes the selection, design, theory, and application of tests for normality. It covers robust estimation, test power, and univariate and multivariate normality. It contains tests for multivariate normality and coordinate-dependent and invariant approaches.
Gaussian processes can be viewed as a far-reaching infinite-dimensional extension of classical normal random variables. Their theory presents a powerful range of tools for probabilistic modeling in various academic and technical domains such as Statistics, Forecasting, Finance, …
The application of reinforcement learning (RL) to algorithmic trading is, in many ways, a perfect match. Trading is fundamentally a problem of making decisions under uncertainty, and reinforcement learning is a family of methods for solving such problems. Indeed, many researcher…
The statistical modelling of extreme precipitation structures is essential in many aspects such as assessing and managing risks resulting from the occurrence of such extreme events for agricultural purposes, in particular. Typically, daily precipitation time series with many zer…
Randomized trials are the gold standard for assessing causal relationships between an intervention and a clinical outcome. The challenge with randomized trials is that they are very expensive to perform, often take place at different times and locations, take many years to compl…
Nowadays, the concept of personalization has gained huge popularity and every decision maker would like to optimize the decisions by exploiting personal data. Personalized decision making has been applied to scenarios including web content recommendation, clinical trials, hiring…
This dissertation includes three working papers showing my research on the macroeconomic impact of uncertainty and bubbles. The first two papers look at uncertainty shocks in a dynamic stochastic general equilibrium framework, while the last one focuses on economic bubbles and c…
An important analysis in fluorescence spectroscopy is molecular dynamics, in particular, quantification of diffusion coefficient. In this research, we apply a convolutional neural network (CNN) to determine the diffusion coefficient of freely diffusing molecules. We construct th…