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Setting Up R on the Cloud
In this chapter we discuss the practical realities in setting up an analytical environment based on R in the cloud, including various cloud providers...
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Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms
Continuous time models with sampled data possess several advantages over conventional discrete time series and panel models (cf., e.g. special issue...
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Maximum-Likelihood Asymptotic Inference for Autoregressive Hilbertian Processes
The autoregressive Hilbertian process framework has been introduced in Bosq (
2000 ). This book provides the nonparametric estimation of the... -
Interactive and Dynamic Graphics
Interactive and dynamic statistical graphics enable data analysts in all fields to carry out visual investigations leading to insights into...
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Observed and Predicted Climate Change
The Intergovernmental Panel on Climate Change (IPCC) was established jointly by the World Meteorological Organization (WMO) and the United Nations...
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The 2005 Neyman Lecture: Dynamic Indeterminism in Science
Jerzy Neyman's life history and some of his contributions to applied statistics are reviewed. In a 1960 article he wrote: "Currently in the period of...
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Spatio-temporal modeling of particulate matter concentration through the SPDE approach
In this work, we consider a hierarchical spatio-temporal model for particulate matter (PM) concentration in the North-Italian region Piemonte. The...
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Multivariate Spatial Analysis of Climate Change Projections
The goal of this work is to characterize the annual temperature for regional climate models. Of interest for impacts studies, these profiles and the...
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Modeling Space–Time Dynamics of Aerosols Using Satellite Data and Atmospheric Transport Model Output
Kernel-based models for space–time data offer a flexible and descriptive framework for studying atmospheric processes. Nonstationary and anisotropic...
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Introduction and Background
This chapter gives an introduction to the problem area and motivates the research into stochastic models of ocean waves. The importance of knowledge...
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Manipulating Data
R has different types of data storage such as lists, arrays, and data frames. This can be confusing for some analysts with a pure background in...
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Climate Projections Using Bayesian Model Averaging and Space–Time Dependence
Projections of future climatic changes are a key input to the design of climate change mitigation and adaptation strategies. Current climate change...
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Functional Median Polish
This article proposes functional median polish, an extension of univariate median polish, for one-way and two-way functional analysis of variance...
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Comparing and Blending Regional Climate Model Predictions for the American Southwest
We consider the problem of forecasting future regional climate. Our method is based on blending different members of an ensemble of regional climate...
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Bayesian factor analysis for spatially correlated data: application to cancer incidence data in Scotland
A hierarchical Bayesian factor model for multivariate spatially correlated data is proposed. Multiple cancer incidence data in Scotland are jointly...
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Bayesian univariate space-time hierarchical model for map** pollutant concentrations in the municipal area of Taranto
An analysis of air quality data is provided for the municipal area of Taranto (Italy) characterized by high environmental risks as decreed by the...