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Modern Applied Statistics With S

Statistics for Spatial Data.

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive interpretation of the field properties. On the computational side, GFs are hampered with the big n problem, since the cost of factorizing dense matrices is cubic in the dimension. Although computational power today is at an all time high, this fact seems still to be a computational bottleneck in many applications. Along with GFs, there is the class of Gaussian Markov random fields (GMRFs) which are discretely indexed. The Markov property makes the precision matrix involved sparse, which enables the use of numerical algorithms for sparse matrices, that for fields in R-2 only use the square root of the time required by general algorithms. The specification of a GMRF is through its full conditional distributions but its marginal properties are not transparent in such a parameterization. We show that, using an approximate stochastic weak solution to (linear) stochastic partial differential equations, we can, for some GFs in the Matern class, provide an explicit link, for any triangulation of R-d, between GFs and GMRFs, formulated as a basis function representation. The consequence is that we can take the best from the two worlds and do the modelling by using GFs but do the computations by using GMRFs. Perhaps more importantly, our approach generalizes to other covariance functions generated by SPDEs, including oscillating and non-stationary GFs, as well as GFs on manifolds. We illustrate our approach by analysing global temperature data with a non-stationary model defined on a sphere. (Less)

https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1467-9868.2011.00777.x

An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

We propose a comparison approach for hierarchical spatio-temporal models, considering both goodness of fit and model complexity, as well as computational load. Here we implement different models on particulate matter, but the proposed "set of criteria" can be used for general environmental phenomena where a hierarchical structure is suitable, a large scale trend is included and a choice of a spatio-temporal covariance function needs.

Comparing spatio-temporal hierarchical models for air quality data

An analysis of temporal and spatial patterns in Italian hospitalization rates for multiple diagnosis.

In current times Internet and social media have become almost unavoidable tools to support research and decision making processes in various fields. Nevertheless, the collection and use of data retrieved from these types of sources pose different challenges. In a previous paper we compared the efficiency of three alternative methods used to retrieve geolocated tweets over an entire country (United Kingdom). One method resulted as the best compromise in terms of both the effort needed to set it and quantity/quality of data collected. In this work we further check, in term of content, whether the three compared methods are able to produce “similar information”. In particular, we aim at checking whether there are differences in the level of sentiment estimated using tweets coming from the three methods. In doing so, we take into account both a cross-section and a longitudinal perspective. Our results confirm that our current best option does not show any significant difference in the sentiment, producing globally scores in between the scores obtained using the two alternative methods. Thus, such a flexible and reliable method can be implemented in the data collection of geolocated tweets in other countries and for other studies based on the sentiment analysis.

/pdf/comparing-methods-to-retrieve-tweets-a-sentiment-approach-1c2ue3ad53.pdf

Comparing Methods to Retrieve Tweets: a Sentiment Approach

The R package CMC to calculate the Cronbach-Mesbah curve

Multiresolution decomposition is commonly understood as a procedure to capture scaledependent
features in random signals. Such methods were first established for image processing and typically rely on raster or regularly gridded data. In this article, we extend a particular multiresolution decomposition procedure to areal count data, i.e. discrete irregularly gridded data. More specifically, we incorporate in a new model concept and distributions from the so-called Besag–York–Mollie model to include a priori demographical knowledge. These adaptions and subsequent changes in the computation schemes are carefully outlined below, whereas the main idea of the original multiresolution decomposition remains. Finally, we show the extension’s feasibility by applying it to oral cavity cancer counts in Germany.

https://www.zora.uzh.ch/id/eprint/206790/1/Furrer_MultiresolutionDecompositionOfArealCount_GRASPA2019.pdf

Multiresolution decomposition of areal count data

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