Statistical inference for spatial Poisson processes by Kutoyants, Yu. A.

Cover of: Statistical inference for spatial Poisson processes | Kutoyants, Yu. A.

Published by Springer in New York .

Written in English

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Subjects:

  • Poisson processes,
  • Parameter estimation

Edition Notes

Includes bibliographical references (p. 265-274) and index.

Book details

StatementYu. A. Kutoyants.
SeriesLecture notes in statistics ;, 134, Lecture notes in statistics (Springer-Verlag) ;, v. 134.
Classifications
LC ClassificationsQA274.42 .K87 1998
The Physical Object
Paginationvii, 276 p. ;
Number of Pages276
ID Numbers
Open LibraryOL360254M
ISBN 10038798562X
LC Control Number98020301

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There are even chapters on statistical estimation problems for inhomogeneous Poisson processes in asymptotic. Poisson processes are quite popular in applied research and therefore they attract the attention of many statisticians.

There are a lot of good books on point processes and many of them contain chapters devoted to statistical inference for general and partic­ ular models of processes. This book is designed for specialists needing an introduction to statistical Statistical inference for spatial Poisson processes book in spatial statistics and its applications.

One of the author's themes is to show how these techniques give new insights into classical procedures (including new examples in likelihood theory) and newer statistical paradigms such as Monte-Carlo inference and by: Statistical Inference for Spatial Poisson Processes.

Kutoyants (auth.) This work is devoted to several problems of parametric (mainly) and nonparametric estimation through the observation of Poisson processes defined on general spaces.

Poisson processes are quite popular in applied research and therefore they attract the attention of many statisticians. Statistical Inference for Spatial Poisson Processes New York, NY: Springer New York, Online-Ressource (DE) Material Type: Internet resource: Document Type: Book, Internet Resource: All Authors / Contributors: Yu A Kutoyants.

Statistical Inference and Simulation for Spatial Point Processes Chapman & Hall/CRC Monographs on Statistics & Applied Probability: Authors: Jesper Moller, Rasmus Plenge Waagepetersen: Edition. Buy a cheap copy of Statistical Inference for Spatial Processes by B.

Ripley - A gently used book at a great low price. Free shipping in the US. Discount books. Let the stories live on. Affordable books. Statistical inference for spatial Poisson processes Rasmus Waagepetersen Department of Mathematics Aalborg University Denmark Febru 1/17 Densities for Poisson processes (The distribution of) A point process X has density f (is absolutely continuous) wrt.

(the distribution of) a point process Y if P(X ∈ F) = E1[Y ∈ F]f(Y). Although other good books on spatial point processes are available, this is the first text to tackle difficult issues of simulation-based inference for such processes.

[T]he text is remarkably easy to follow. The authors have a very impressive knack for explaining complicated topics very clearly. [This book] will no doubt Cited by: Statistical Inference for Spatial Poisson Processes (Paperback) Yu A. Kutoyants Published by Springer-Verlag New York Inc., United States ()Price Range: $ - $ Spatial point processes play a fundamental role in spatial statistics and today they are an active area of research with many new applications.

Although other published works address different aspects of spatial point processes, most of the classical literature deals only with nonparametric methods, and a thorough treatment of the theory and Price: $ Get this from a library.

Statistical inference for spatial Poisson processes. [Yu A Kutoyants]. Statistical Inference and Simulation for Spatial Point Processes (Chapman & Hall/CRC Monographs on Statistics and Applied Probability Book ) - Kindle edition by Waagepetersen, Rasmus Plenge.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Statistical Inference and Simulation for Spatial Manufacturer: CRC Press. “Spatial statistics aims to develop models and statistical inference methods for observations that have a distinct spatial location component.

The book under review presents theory simulation and statistical applications in a well-structured manner and even covers some modern topics from the very recent journal literature. Author: Carlo Gaetan, Xavier Guyon.

Statistical inference for spatial Poisson processes. [Yu A Kutoyants] -- The book discusses the estimation theory for the wide class of inhomogeneous Poisson processes. The consistency, limit distributions and the convergence of moments of parameter estimators are.

Poisson regression for count data Non-linear regression Smoothing and Generalized Additive Models (GAM) Geographically weighted regression (GWR) Spatial series and spatial autoregression SAR models CAR models Spatial filtering models 17 Time series analysis.

Empty-space distance for a spatial pattern Distance from a point pattern to another spatial pattern Theory for edge corrections Palm distribution FAQ.

PART III: STATISTICAL INFERENCE. Poisson Models (download pdf) Introduction Poisson point process models Fitting Poisson models in spatstat Statistical inference for Poisson models Alternative. This book covers the best-known spatial models for three types of spatial data: geostatistical data (stationarity, intrinsic models, variograms, spatial regression and space-time models), areal data (Gibbs-Markov fields and spatial auto-regression) and point pattern data (Poisson, Cox, Gibbs and Markov point processes).

Statistical Inference Stochastic Processes provides information pertinent to the theory of stochastic processes. This book discusses stochastic models that are increasingly used in scientific research and describes some of their applications.

Organized into three parts encompassing 12 chapters, this book begins with an overview of the basic. Statistical Inference for Spatial Processes by B. Ripley,available at Book Depository with free delivery worldwide. Book Description.

Modern Statistical Methodology and Software for Analyzing Spatial Point Patterns. Spatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data.

Making the techniques accessible to non-mathematicians, the authors draw on their 25 years of. Statistical Inference Stochastic Processes provides information pertinent to the theory of stochastic processes.

This book discusses stochastic models that are increasingly used in scientific research and describes some of their applications. Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling.

Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering. This chapter gives a brief introduction to spatial point processes, with a view to applications.

The three sections focus on the construction of point process models, the simulation of point processes, and statistical inference. For further background, we recommend [Daley et al., Probability and its applications (New York). description: Product Description: This book is designed for specialists needing an introduction to statistical inference in spatial statistics and its of the author's themes is to show how these techniques give new insights into classical procedures (including new examples in likelihood theory) and newer statistical paradigms such as Monte-Carlo inference and pseudo.

Get this from a library. Statistical inference and simulation for spatial point processes. [Jesper Møller; Rasmus Plenge Waagepetersen] -- "Technology now makes available huge amounts of spatial point process data, and new applications are continually arising in fields as diverse as astronomy, forestry, image analysis, and epidemiology.

“Spatial statistics aims to develop models and statistical inference methods for observations that have a distinct spatial location component. The book under review presents theory simulation and statistical applications in a well-structured manner and even covers some modern topics from the very recent journal literature.

Price: $ Thus, the population, sample, and inference constitute a so-called spatial statistic trinity (SST), providing a new framework for spatial statistics, including sampling and inference.

This paper shows that it greatly simplifies the choice of method in spatial sampling and inferences. Journals & Books; Help Using recent results for composite likelihood and for spatial point processes, we develop tools for statistical inference, including intensity approximations, variance estimators, localised tests for the significance of a covariate effect, and global tests of homogeneity.

For Poisson spatial point processes, local. Statistical inference for spatial processes. [Brian D Ripley; University of Cambridge.] This book introduces statistical inference in spatial statistics and its applications. Rating: (not yet rated) 0 with reviews - Be the first.

Subjects: Spatial analysis (Statistics). Statistical inference for piecewise-deterministic Markov processes | Azais, Romain; Bouguet, Florian | download | B–OK. Download books for free. Find books. (source: Nielsen Book Data) Summary Modern Statistical Methodology and Software for Analyzing Spatial Point Patterns Spatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial.

spatial point processes 93 point processes on euclidean spaces 93 the poisson process 96 moment measures 98 stationarity concepts and product densities finite point processes the papangelou conditional intensity markov point processes likelihood inference for poisson processes   Kottas A, Sanso B.

Bayesian Mixture Modeling for Spatial Poisson Process Intensities, with Applications to Extreme Value Analysis. Journal of Statistical Planning and Inference (Special Issue on Bayesian Inference for Stochastic Processes) ; – MacEachern S.

Estimating normal means with a conjugate style Dirichlet process prior. "Statistical Methods for Spatial Data Analysis" answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial book is a comprehensive and illustrative.

The Poisson point process is a highly useful and used random object. But we now need to simulate it on a computer, which will be the theme of the future entries. Further reading.

The Wikipedia article is a good starting point. The best book on the Poisson point process is the monograph Poisson processes by Kingman. Statistical Inference; Statistical Inference and Prediction in Climatology: A Bayesian Approach; Statistical Inference for Discrete Time Stochastic Processes; Statistical Inference for Ergodic Diffusion Processes; Statistical Inference for Financial Engineering; Statistical Inference for Spatial Poisson Processes.

poisson processes by thinning. Naval Research Logistics Quarterly, –, Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall, Boca Raton.

Purely spatial processes, Papangelou intensity and the Georgii-Zessin Nguyen formula. For point processes in R2, there is no natural ordering as there is in. I Diggle, P.J. () Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition CRC/Chapman & Hall.

I Waller and Gotway (, Chapter 5) Applied Spatial Statistics for Public Health Data. New York: Wiley. I M˝ller, J. and Waagepetersen () Statistical Inference and Simulation for Spatial Point Processes. Boca Raton, FL. Estimation and statistical inference for space-time point processes Stephen Lynn Rathbun Poisson process (Diggle, ,), and the Markov point process (Diggle et al., ).

Although a number of parameter—estimation methods have been proposed for spatial point processes, very little is known about the properties of the resulting.Cordy introduced design-based inference for spatial sampling designs.

For a sample of fixed size m i, define the inclusion density π i (t) = ∑ j = 1 m i f i j (t), where f ij (t) is the marginal density for the jth covariate sample site.

Alternatively, the covariates may be sampled according to Poisson point processes with known intensities.Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.

Bayesian inference is an important technique in statistics, and especially in mathematical an updating is particularly important in the dynamic analysis of a sequence of data.

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