2,196 research outputs found
Inference:A Contribution to the collection "Stochastic Geometry: Highlights, Interactions and New Perspectives"
Get PDF(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the title ‘Inference'.)This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus is on spatial point processes.(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the title ‘Inference'.)This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus is on spatial point processes.</p
MCMC Computations for Bayesian Mixture Models Using Repulsive Point Processes
Get PDFRepulsive mixture models have recently gained popularity for Bayesian cluster detection. Compared to more traditional mixture models, repulsive mixture models produce a smaller number of well-separated clusters. The most commonly used methods for posterior inference either require to fix a priori the number of components or are based on reversible jump MCMC computation. We present a general framework for mixture models, when the prior of the "cluster centers" is a finite repulsive point process depending on a hyperparameter, specified by a density which may depend on an intractable normalizing constant. By investigating the posterior characterization of this class of mixture models, we derive a MCMC algorithm which avoids the well-known difficulties associated to reversible jump MCMC computation. In particular, we use an ancillary variable method, which eliminates the problem of having intractable normalizing constants in the Hastings ratio. The ancillary variable method relies on a perfect simulation algorithm, and we demonstrate this is fast because the number of components is typically small. In several simulation studies and an application on sociological data, we illustrate the advantage of our new methodology over existing methods, and we compare the use of a determinantal or a repulsive Gibbs point process prior model. Supplementary files for this article are available online
sj-pdf-1-jcb-10.1177_0271678X211052588 - Supplemental material for Reliability and validity of the mean flow index (Mx) for assessing cerebral autoregulation in humans: A systematic review of the methodology
No full textSupplemental material, sj-pdf-1-jcb-10.1177_0271678X211052588 for Reliability and validity of the mean flow index (Mx) for assessing cerebral autoregulation in humans: A systematic review of the methodology by Markus Harboe Olsen, Christian Gunge Riberholt, Jesper Mehlsen, Ronan MG Berg and Kirsten Møller in Journal of Cerebral Blood Flow & Metabolism</p
Discussion of "Modern Statistics for Spatial Point Processes"
No full textThe paper ‘Modern statistics for spatial point processes' by Jesper Møller and Rasmus P. Waagepetersen is based on a special invited lecture given by the authors at the 21st Nordic Conference on Mathematical Statistics, held at Rebild, Denmark, in June 2006. At the conference, Antti Penttinen and Eva B. Vedel Jensen were invited to discuss the paper. We here present the comments from the two invited discussants and from a number of other scholars, as well as the authors' responses to these comments. Below Figure 1, Figure 2, etc., refer to figures in the paper under discussion, while Figure A, Figure B, etc., refer to figures in the current discussion. All numbered sections and formulas refer to the paper.The paper ‘Modern statistics for spatial point processes' by Jesper Møller and Rasmus P. Waagepetersen is based on a special invited lecture given by the authors at the 21st Nordic Conference on Mathematical Statistics, held at Rebild, Denmark, in June 2006. At the conference, Antti Penttinen and Eva B. Vedel Jensen were invited to discuss the paper. We here present the comments from the two invited discussants and from a number of other scholars, as well as the authors' responses to these comments. Below Figure 1, Figure 2, etc., refer to figures in the paper under discussion, while Figure A, Figure B, etc., refer to figures in the current discussion. All numbered sections and formulas refer to the paper
Parametric methods for spatial point processes
Get PDF(This text is submitted for the volume ‘A Handbook of Spatial Statistics' edited by A.E. Gelfand, P. Diggle, M. Fuentes, and P. Guttorp, to be published by Chapmand and Hall/CRC Press, and planned to appear as Chapter 4.4 with the title ‘Parametric methods'.)1 IntroductionThis chapter considers inference procedures for parametric spatial point process models. The widespread use of sensible but ad hoc methods based on summary statistics of the kind studied in Chapter 4.3 have through the last two decades been supplied by likelihood based methods for parametric spatial point process models. The increasing development of such likelihood based methods, whether frequentist or Bayesian, has lead to more objective and efficient statistical procedures. When checking a fitted parametric point process model, summary statistics and residual analysis (Chapter 4.5) play an important role in combination with simulation procedures. Simulation free estimation methods based on composite likelihoods or pseudo likelihoods are discussed in Section 3. Markov chain Monte Carlo (MCMC) methods have had an increasing impact on the development of simulationbased likelihood inference, where maximum likelihood inference is studied in Section 4, and Bayesian inference in Section 5. On one hand, as the development in computer technology and computational statistics continues,computationally-intensive simulation-based methods for likelihood inference probably will play a increasing role for statistical analysis of spatial point patterns. On the other hand, since larger and larger point pattern dataset are expected to be collected in the future, and the simulation free methods are 1 much faster, they may continue to be of importance, at least at a preliminary stage of a parametric spatial point process analysis, where many different parametric models may quickly be investigated. Much of this review is inspired by the monograph Møller andWaagepetersen (2003) and the discussion paper Møller andWaagepetersen (2007). Other recent textbooks related to the topic of this chapter include Baddeley, Gregori, Mateu, Stoica and Stoyan (2006), Diggle (2003), Illian, Penttinen, Stoyan and Stoyan (2008), and Van Lieshout (2000). Readers interested in background material on MCMC algorithms for spatial point processes are referred to Geyer and Møller (1994), Geyer (1999), Møller and Waagepetersen (2003), and the references therein. Notice the comments and corrections to Møller and Waagepetersen (2003) at www.math.aau.dk/~jm.</p
Correction:Automatic emphysema detection using weakly labeled HRCT lung images (PLoS ONE (2018) 13:10 (e0205397) DOI: 10.1371/journal.pone.0205397)
Get PDF\u3cp\u3eThe last six authors, Sofia Paschaloudi, Morten Vuust, Jesper Carl, Ulla Møller Weinreich, Lasse Riis Østergaard, and Marleen de Bruijne, should not have been attributed equal contribution to this work.\u3c/p\u3
Manden Der Ikke Ville Være Høflig:Historien om den danske koloni læge Agner Møller
No full textAgner Møller tog i 20erne i hollandsk tjeneste i Hollandsk Ostindien, som så mange andre danske læger. Han var en kritisk observatør af hollandsk kolonialisme og indsamlede betydningsfulde genstande til Nationalmuseets Etnografiske Samling. Samlingen af etnografika fra øen Nias, er en af verdens største og bedst registrerede
Manden Der Ikke Ville Være Høflig:Historien om den danske koloni læge Agner Møller
No full textAgner Møller tog i 20erne i hollandsk tjeneste i Hollandsk Ostindien, som så mange andre danske læger. Han var en kritisk observatør af hollandsk kolonialisme og indsamlede betydningsfulde genstande til Nationalmuseets Etnografiske Samling. Samlingen af etnografika fra øen Nias, er en af verdens største og bedst registrerede
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