000			06116cam a2200697Mi 4500
001			9781315152509
003			FlBoTFG
005			20220509192933.0
006			m o d
007			cr cn|||||||||
008			170721s2017 flua o 000 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781315152509 _q(e-book ; _qPDF)
020			_a1315152509
020			_a9781498752121
020			_a1498752128
020			_a9781498752022
020			_a1498752020
020			_a9781351639019
020			_a1351639013
020			_a9781351648547 _q(electronic bk. : EPUB)
020			_a1351648543 _q(electronic bk. : EPUB)
035			_a(OCoLC)994595046 _z(OCoLC)1003888856 _z(OCoLC)1015209584 _z(OCoLC)1036296568
035			_a(OCoLC-P)994595046
050		4	_aGE45.S73
072		7	_aMAT _x029000 _2bisacsh
072		7	_aNAT _x010000 _2bisacsh
072		7	_aSCI _x026000 _2bisacsh
072		7	_aTQ _2bicssc
082	0	4	_a363.70072/7 _223
082	0	4	_a[E]
100	1		_aGelfand, Alan E., _d1945- _eauthor.
245	1	0	_aHandbook of Environmental and Ecological Statistics / _cAlan E. Gelfand.
250			_aFirst edition.
264		1	_aBoca Raton, FL : _bCRC Press, _c2017.
300			_a1 online resource : _btext file, PDF.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aChapman & Hall/CRC handbooks of modern statistical methods
520	2		_a"This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes. The statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in the environmental processes. In addition, the environmental community has substantially increased its scope of data collection including, e.g., observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial. The contribution of this handbook is to assemble, in roughly 35 chapters, a state-ofthe-art view of this interface"--Provided by publisher.
505	0		_aCover; Half Title; Title Page; Copyright Page; Table of Contents; Preface; 1: Introduction; I: Methodology for Statistical Analysis of Environmental Processes; 2: Modeling for environmental and ecological processes; 2.1 Introduction; 2.2 Stochastic modeling; 2.3 Basics of Bayesian inference; 2.3.1 Priors; 2.3.2 Posterior inference; 2.3.3 Bayesian computation; 2.4 Hierarchical modeling; 2.4.1 Introducing uncertainty; 2.4.2 Random effects and missing data; 2.5 Latent variables; 2.6 Mixture models; 2.7 Random effects; 2.8 Dynamic models; 2.9 Model adequacy; 2.10 Model comparison
505	8		_a2.10.1 Bayesian model comparison2.10.2 Model comparison in predictive space; 2.11 Summary; 3: Time series methodology; 3.1 Introduction; 3.2 Time series processes; 3.3 Stationary processes; 3.3.1 Filtering preserves stationarity; 3.3.2 Classes of stationary processes; 3.3.2.1 IID noise and white noise; 3.3.2.2 Linear processes; 3.3.2.3 Autoregressive moving average processes; 3.4 Statistical inference for stationary series; 3.4.1 Estimating the process mean; 3.4.2 Estimating the ACVF and ACF; 3.4.3 Prediction and forecasting; 3.4.4 Using measures of correlation for ARMA model identification
505	8		_a3.4.5 Parameter estimation3.4.6 Model assessment and comparison; 3.4.7 Statistical inference for the Canadian lynx series; 3.5 Nonstationary time series; 3.5.1 A classical decomposition for nonstationary processes; 3.5.2 Stochastic representations of nonstationarity; 3.6 Long memory processes; 3.7 Changepoint methods; 3.8 Discussion and conclusions; 4: Dynamic models; 4.1 Introduction; 4.2 Univariate Normal Dynamic Linear Models (NDLM); 4.2.1 Forward learning: the Kalman filter; 4.2.2 Backward learning: the Kalman smoother; 4.2.3 Integrated likelihood; 4.2.4 Some properties of NDLMs
505	8		_a4.2.5 Dynamic generalized linear models (DGLM)4.3 Multivariate Dynamic Linear Models; 4.3.1 Multivariate NDLMs; 4.3.2 Multivariate common-component NDLMs; 4.3.3 Matrix-variate NDLMs; 4.3.4 Hierarchical dynamic linear models (HDLM); 4.3.5 Spatio-temporal models; 4.4 Further aspects of spatio-temporal modeling; 4.4.1 Process convolution based approaches; 4.4.2 Models based on stochastic partial differential equations; 4.4.3 Models based on integro-difference equations; 5: Geostatistical Modeling for Environmental Processes; 5.1 Introduction; 5.2 Elements of point-referenced modeling
505	8		_a5.2.1 Spatial processes, covariance functions, stationarity and isotropy5.2.2 Anisotropy and nonstationarity; 5.2.3 Variograms; 5.3 Spatial interpolation and kriging; 5.4 Summary; 6: Spatial and spatio-temporal point processes in ecological applications; 6.1 Introduction -- relevance of spatial point processes to ecology; 6.2 Point processes as mathematical objects; 6.3 Basic definitions; 6.4 Exploratory analysis -- summary characteristics; 6.4.1 The Poisson process-a null model; 6.4.2 Descriptive methods; 6.4.3 Usage in ecology; 6.5 Point process models
505	8		_a6.5.1 Modelling environmental heterogeneity -- inhomogeneous Poisson processes and Cox processes
588			_aOCLC-licensed vendor bibliographic record.
650	0	7	_aNATURE _xEcology. _2bisacsh
650	0	7	_aSCIENCE _xEnvironmental Science. _2bisacsh
650		0	_aEnvironmental sciences _xStatistical methods.
650		0	_aEcology _xStatistical methods.
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
650		7	_aNATURE / Ecology _2bisacsh
650		7	_aSCIENCE / Environmental Science _2bisacsh
700	1		_aFuentes, Montserrat, _eauthor.
700	1		_aHoeting, Jennifer A. _q(Jennifer Ann), _d1966- _eauthor.
700	1		_aSmith, Richard Lyttleton, _eauthor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781315152509
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
938			_aTaylor & Francis _bTAFR _n9781315152509
999			_c126875 _d126875