Spatiotemporal Analysis of Urban Heatwaves Using Tukey G-and-H Random Field Models

Tukey g-and-h Random Fields

We propose a new class of trans-Gaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable t...

Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter es...

Analysis of spatial distribution of Tehran Metropolis urban services using models of urban planning

The process of spatial distribution of urban services in order to provide equitable access to opportunities and reduced regional disparities, and earning the highest citizen satisfaction are among the main challenges facing urban management. This requires knowledge of the current status of spatial distribution of public services in the city, followed by optimal resource allocation under varying...

Texture analysis and simulations using Markov random field models

The main goal of texture analysis is to extract useful textural information from an image. The use of Bayesian methods is an approach, which seeks to provide a unified framework in modeling within many different image processes. In this work, spatial behavior of the auto-logistic model in a rectangular lattice would be investigated, concentrating the first-order neighborhood structures. A simpl...

Simulating Univariate and Multivariate Tukey g-and-h Distributions Based on the Method of Percentiles