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A functional connectivity approach for modeling cross-sectional dependence with an application to the estimation of hedonic housing prices in Paris
This paper proposes a functional connectivity approach, inspired by brain imaging literature, to model cross-sectional dependence. Using a varying...
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GME Estimation of Spatial Structural Equations Models
The objective of this paper is to develop a GME formulation for the class of spatial structural equations models (S-SEM). In this respect, two...
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Estimating critical values for testing the i.i.d. in standardized residuals from GARCH models in finite samples
Taking into account that the BDS test—which is used as a misspecification test applied to standardized residuals from the GARCH(1,1) model—is...
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Nonparametric Regression with Multiple Predictors
In this chapter we describe how the methods described in Chaps. 10 and 11 may be extended to the situation in which there are multiple predictors. We...
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Charts with R
Charts are particularly important in Six Sigma projects. The aim of a chart is usually to support the interpretation of data. Hence, providing an...
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Estimating second order characteristics of point processes with known independent noise
The analysis of point patterns often begins with a test of complete spatial randomness using summaries such as the emptyspace function F or the...
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Exploring and Discovering Data
In this chapter, we discuss different approaches for exploring data. Data exploration is probably the single most important step in any data...
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The Grammar of Graphics
The Grammar of Graphics, or GOG, denotes a system with seven orthogonal components. By orthogonal, we mean there are seven graphical...
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Looking at Multivariate Data: Visualisation
According to Chambers, Cleveland, Kleiner, and Tukey (1983), “there is no statistical tool that is as powerful as a well-chosen graph”. Certainly...
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Factor Modeling for High Dimensional Time Series
We briefly compare an econometric factor model and a statistical factor model, the latter being to capture the linear dynamic structure of the data...
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Changes of Basis
The previous chapters have focused on the definition of a model, and corresponding estimator or sampler, for some random vector...
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Continuous Time-Varying Kriging for Spatial Prediction of Functional Data: An Environmental Application
Spatially correlated functional data are present in a wide range of environmental disciplines and, in this context, efficient prediction of curves is...
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Multidimensional Modelling
In principle, the extension to multidimensional \(\underline{z}\)...
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A general science-based framework for dynamical spatio-temporal models
Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict...
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Priors for Bayesian adaptive spline smoothing
Adaptive smoothing has been proposed for curve-fitting problems where the underlying function is spatially inhomogeneous. Two Bayesian adaptive...
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Regression Models with STARMA Errors: An Application to the Study of Temperature Variations in the Antarctic Peninsula
Motivated by spatio-temporal problems that occur in many areas such as environment, geography etc., we propose multivariate regression models with...
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Inverse Problems
An understanding of forward and inverse problems [12, 301] lies at the heart of any large estimation problem. Abstractly, most physical systems can...
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Sampling and Monte Carlo Methods
The matter of statistical sampling was discussed in Chapter 2: Prior Samplingin Section 2.5.2, and Posterior Sampling in Section 2.5.4. Given a...
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Static Estimation and Sampling
This chapter derives the two fundamental linear estimators: Section 3.1: The non-Bayesian linear least-squares estimator Section 3.2: The Bayesian...