https://www.wsl.ch/staff/niklaus.zimmermann/papers/ecomod135_147.pdf

Predictive habitat distribution models in ecology

Statistics for Spatial Data.

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Modern Applied Statistics With S

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

Functional data analysis (FDA) is a relatively new branch in statistics. Experiments where a complete function is observed for each individual give rise to functional data. In this work we focus on the case of functional data presenting spatial dependence. The three classic types of spatial data structures (geostatistical data, point patterns, and areal data) can be combined with functional data as it is shown in the examples of each situation provided here. We also review some contributions in the literature on spatial functional data. Copyright © 2009 John Wiley & Sons, Ltd.

Statistics for spatial functional data: some recent contributions

In this article, we propose two methods for estimating space and space-time covariance functions from a Gaussian random field, based on the composite likelihood idea. The first method relies on the maximization of a weighted version of the composite likelihood function, while the second one is based on the solution of a weighted composite score equation. This last scheme is quite general and could be applied to any kind of composite likelihood. An information criterion for model selection based on the first estimation method is also introduced. The methods are useful for practitioners looking for a good balance between computational complexity and statistical efficiency. The effectiveness of the methods is illustrated through examples, simulation experiments, and by analyzing a dataset on ozone measurements.

/pdf/estimating-space-and-space-time-covariance-functions-for-1iviwnynfd.pdf

Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach

In various scientific fields properties are represented by functions varying over space. In this paper, we present a methodology to make spatial predictions at non-data locations when the data values are functions. In particular, we propose both an estimator of the spatial correlation and a functional kriging predictor. We adapt an optimization criterion used in multivariable spatial prediction in order to estimate the kriging parameters. The curves are pre-processed by a non-parametric fitting, where the smoothing parameters are chosen by cross-validation. The approach is illustrated by analyzing real data based on soil penetration resistances.

Ordinary kriging for function-valued spatial data

The paper compares non-parametric (design-based) and parametric (model-based) approaches to the analysis of data in the form of replicated spatial point patterns in two or more experimental groups. Basic questions for data of this kind concern estimating the properties of the underlying spatial point process within each experimental group, and comparing the properties between groups. A non-parametric approach, building on work by Diggle et. al. (1991), summarizes each pattern by an estimate of the reduced second moment measure or K-function (Ripley (1977)) and compares mean K-functions between experimental groups using a bootstrap testing procedure. A parametric approach fits particular classes of parametric model to the data, uses the model parameter estimates as summaries and tests for differences between groups by comparing fits with and without the assumption of common parameter values across groups. The paper discusses how either approach can be implemented in the specific context of a single-factor replicated experiment and uses simulations to show how the parametric approach can be more efficient when the underlying model assumptions hold, but potentially misleading otherwise.

https://www.jstor.org/stable/pdfplus/1428191.pdf

A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns

Kriging an overview | ScienceDirect TopicsSpatial Statistics | Journal | ScienceDirect.com by ElsevierGIS at CDC | cdc.govCRAN Task View: Handling and Analyzing Spatio-Temporal DataUNIVERSIDAD AUTONOMA DEL ESTADO DE MEXICO FACULTAD ...A novel framework for spatiotemporal prediction of GeoHealthEEL YonseiMachine learning for data-driven discovery in solid Earth The Effect of Soil Sampling Density and Spatial ???----?????????????????????????????Downscale climate data with machine learning | Learn ArcGISGitHub James-Thorson-NOAA/VAST: Spatio-temporal ...A Review and Framework for Categorizing Current Research Spatial analysis WikipediaDavid B. Dunson | Scholars@DukeDevelopment and evaluation of a spatial decision support More than 75 percent decline over 27 years in total flying Dr Prafull Kumar Singh – CUSBSpecies Distribution Modelling: Contrasting presence-only Geospatial Analysis 6th Edition, 2018 de Smith MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology Prafull SINGH | Professor (Associate) | Ph.D Spatial And Spatio Temporal Geostatistical Modeling And Chapter 15 Building a Shiny app to upload and visualize A Bayesian Approach to Estimate the Spatial Distribution Access Denied LiveJournalGitHub sshuair/awesome-gis: ?Awesome GIS is a collection CRAN Task View: Analysis of Spatial DataA Simulation Study of Hierarchical Bayesian Fusion Spatial Chapter 8 Temporal Models | Bayesian inference with INLACross-correlation WikipediaChapter 7 Spatial Models | Bayesian inference with INLAThe River Continuum Concept

Jorge Mateu

Papers

Statistics for spatial functional data: some recent contributions

Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach

Ordinary kriging for function-valued spatial data

A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns

Spatial and Spatio-Temporal Geostatistical Modeling and Kriging: Montero/Spatial and Spatio-Temporal Geostatistical Modeling and Kriging