Foto 1 di 1

Galleria
Foto 1 di 1

Ne hai uno da vendere?
Statistical Methods for Spatial Data Analysis by Oliver Schabenberger: New
US $107,17
CircaEUR 91,57
Condizione:
Nuovo
Libro nuovo, intatto e non letto, in perfette condizioni, senza pagine mancanti o danneggiate. Per maggiori dettagli, consulta l'inserzione del venditore.
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
Spedizione:
Gratis Standard Shipping.
Oggetto che si trova a: Sparks, Nevada, Stati Uniti
Consegna:
Consegna prevista tra il gio 21 ago e il mar 26 ago a 94104
Restituzioni:
Restituzioni entro 30 giorni. Le spese di spedizione del reso sono a carico dell'acquirente..
Pagamenti:
Fai shopping in tutta sicurezza
Il venditore si assume la piena responsabilità della messa in vendita dell'oggetto.
Numero oggetto eBay:284285909872
Specifiche dell'oggetto
- Condizione
- Book Title
- Statistical Methods for Spatial Data Analysis
- Publication Date
- 2004-12-20
- Pages
- 488
- ISBN
- 9781584883227
Informazioni su questo prodotto
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
1584883227
ISBN-13
9781584883227
eBay Product ID (ePID)
43546082
Product Key Features
Number of Pages
512 Pages
Language
English
Publication Name
Statistical Methods for Spatial Data Analysis
Subject
Probability & Statistics / General
Publication Year
2004
Type
Textbook
Subject Area
Mathematics
Series
Chapman and Hall/Crc Texts in Statistical Science Ser.
Format
Hardcover
Dimensions
Item Height
1.3 in
Item Weight
36.9 Oz
Item Length
10.3 in
Item Width
7.3 in
Additional Product Features
Intended Audience
College Audience
LCCN
2004-043107
Dewey Edition
22
Illustrated
Yes
Dewey Decimal
001.4/22
Table Of Content
INTRODUCTION The Need for Spatial Analysis Types of Spatial Data Autocorrelation-Concept and Elementary Measures Autocorrelation Functions The Effects of Autocorrelation on Statistical Inference Chapter Problems SOME THEORY ON RANDOM FIELDS Stochastic Processes and Samples of Size One Stationarity, Isotropy, and Heterogeneity Spatial Continuity and Differentiability Random Fields in the Spatial Domain Random Fields in the Frequency Domain Chapter Problems MAPPED POINT PATTERNS Random, Aggregated, and Regular Patterns Binomial and Poisson Processes Testing for Complete Spatial Randomness Second-Order Properties of Point Patterns The Inhomogeneous Poisson Process Marked and Multivariate Point Patterns Point Process Models Chapter Problems SEMIVARIOGRAM AND COVARIANCE FUNCTION ANALYSIS AND ESTIMATION Introduction Semivariogram and Covariogram Covariance and Semivariogram Models Estimating the Semivariogram Parametric Modeling Nonparametric Estimation and Modeling Estimation and Inference in the Frequency Domain On the Use of Non-Euclidean Distances in Geostatistics Supplement: Bessel Functions Chapter Problems SPATIAL PREDICTION AND KRIGING Optimal Prediction in Random Fields Linear Prediction-Simple and Ordinary Kriging Linear Prediction with a Spatially Varying Mean Kriging in Practice Estimating Covariance Parameters Nonlinear Prediction Change of Support On the Popularity of the Multivariate Gaussian Distribution Chapter Problems SPATIAL REGRESSION MODELS Linear Models with Uncorrelated Errors Linear Models with Correlated Errors Generalized Linear Models Bayesian Hierarchical Models Chapter Problems SIMULATION OF RANDOM FIELDS Unconditional Simulation of Gaussian Random Fields Conditional Simulation of Gaussian Random Fields Simulated Annealing Simulating from Convolutions Simulating Point Processes Chapter Problems NON-STATIONARY COVARIANCE Types of Non-Stationarity Global Modeling Approaches Local Stationarity SPATIO-TEMPORAL PROCESSES A New Dimension Separable Covariance Functions Non-Separable Covariance Functions The Spatio-Temporal Semivariogram Spatio-Temporal Point Processes
Synopsis
Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. 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 data. This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields, non-stationary covariance, and spatio-temporal processes. Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself., Statistical Methods for Spatial Data Analysis is a comprehensive treatment of statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. The authors deliver an outstanding treatment of semivariogram estimation and modeling, spatial analysis in the spectral domain, and spatial regression, covering linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data and succinctly discussing Bayesian hierarchical models. The book concludes with a review of simulation, non-stationary covariance, and spatio-temporal processes.
LC Classification Number
QA278.2.S32 2004
Descrizione dell'oggetto fatta dal venditore
Informazioni sul venditore professionale
Informazioni su questo venditore
AlibrisBooks
98,6% di Feedback positivi•2,0 milioni oggetti venduti
Registrato come venditore professionale
Feedback sul venditore (515.500)
- m***m (2311)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoI’m thrilled with my recent purchase . The website was user-friendly, and the product descriptions were accurate. Customer service was prompt and helpful, answering all my questions. My order arrived quickly, well-packaged, and the product exceeded my expectations in quality. I’m impressed with the attention to detail and the overall experience. I’ll definitely shop here again and highly recommend from this seller to others. Thank you for a fantastic experience!
- a***n (54)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoMistakenly ordered a paperback that I thought was a hardcover, not sellers fault; it was described properly on the listing. Seller still processed a refund the day I went to return the item and let me keep the item anyway. A+++ service. Book arrived quickly in great condition and for a great price. Thank you so much! Amazing seller!
- n***c (96)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoseller was communicative about my shipment, media mail took a while and tracking wasn't updated frequently, but seller communicated to me very quickly on status. the item came new and wrapped as described, though the packaging in it was packed wasn't sturdy and falling apart when it got to me.