Asymptotic theory of cepstral random fields

Asymptotic Theory of Cepstral Random Fields by Tucker

Random fields play a central role in the analysis of spatially correlated data and, as a result, have a significant impact on a broad array of scientific applications. This paper studies the cepstral random field model, providing recursive formulas that connect the spatial cepstral coefficients to an equivalent moving-average random field, which facilitates easy computation of the autocovarianc...

Asymptotic Theory for Linear-Chain Conditional Random Fields

In this theoretical paper we develop an asymptotic theory for Linear-Chain Conditional Random Fields (L-CRFs) and apply it to derive conditions under which the Maximum Likelihood Estimates (MLEs) of the model weights are strongly consistent. We first define L-CRFs for infinite sequences and analyze some of their basic properties. Then we establish conditions under which ergodicity of the observ...

Asymptotic Theory for Random Forests

Random forests have proven to be reliable predictive algorithms in many application areas. Not much is known, however, about the statistical properties of random forests. Several authors have established conditions under which their predictions are consistent, but these results do not provide practical estimates of random forest errors. In this paper, we analyze a random forest model based on s...

Random Fields Theory

Non-Asymptotic Theory of Random Matrices

1 Definition The topic in this lecture is Subgaussian random variables. We start with the definition, and discuss some properties they hold. Definition 1 (Subgaussian random variables). A random variable X is subgaussian if ∃c, C such that P(|x| > t) ≤ Ce −ct 2 ∀t ≥ 0. (1) As the name suggests, the notion of subgaussian random variables is a generalization of Gaussian random variables. Both the...