Essays about: "Wishart matrix"
Found 4 essays containing the words Wishart matrix.
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1. Dynamic Covariance Modelling Using Generalised Wishart Processes
University essay from Lunds universitet/Matematisk statistikAbstract : Modern portfolio theory was pioneered by Markowitz who formulated the mean-variance problem, without which any discussion on quantitative approaches to portfolio selection would be incomplete. The framework boils down to finding the expected return $\mu$ and covariance $\Sigma$, after which the solution is proportional to $\Sigma^{-1}\mu$. READ MORE
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2. Antieigenvalues of Wishart Matrices
University essay from Linköpings universitet/Matematisk statistik; Linköpings universitet/Tekniska fakultetenAbstract : In this thesis we derive the distribution for the first antieigenvalue for a random matrix with distribution W ∼ Wp(n, Ip) for p = 2 and p = 3. For p = 2 we present a proof that the first antieigenvalue has distribution β((n−1)/2, 1). READ MORE
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3. A Review of Gaussian Random Matrices
University essay from Linköpings universitet/Matematisk statistik; Linköpings universitet/Tekniska fakultetenAbstract : While many university students get introduced to the concept of statistics early in their education, random matrix theory (RMT) usually first arises (if at all) in graduate level classes. This thesis serves as a friendly introduction to RMT, which is the study of matrices with entries following some probability distribution. READ MORE
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4. A Mixed Frequency Steady-State Bayesian Vector Autoregression: Forecasting the Macroeconomy
University essay from Uppsala universitet/Statistiska institutionenAbstract : This thesis suggests a Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is proposed to sample the posterior distribution. READ MORE
