LEADER 05423nam 22006494a 450 001 9910143557803321 005 20170810185144.0 010 $a1-280-28762-4 010 $a9786610287628 010 $a0-470-02474-7 010 $a0-470-02697-9 035 $a(CKB)1000000000355614 035 $a(EBL)242945 035 $a(OCoLC)173349038 035 $a(SSID)ssj0000148540 035 $a(PQKBManifestationID)11177018 035 $a(PQKBTitleCode)TC0000148540 035 $a(PQKBWorkID)10224752 035 $a(PQKB)11745906 035 $a(MiAaPQ)EBC242945 035 $a(PPN)151204241 035 $a(EXLCZ)991000000000355614 100 $a20030724d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEnvironmental statistics$b[electronic resource] $emethods and applications /$fVic Barnett 210 $aChichester, West Sussex, England ;$aHoboken, NJ $cJ. Wiley$dc2004 215 $a1 online resource (318 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-48971-9 320 $aIncludes bibliographical references (p. [267]-284) and index. 327 $aEnvironmental Statistics; Contents; Preface; Chapter 1 Introduction; 1.1 Tomorrow is too Late!; 1.2 Environmental Statistics; 1.3 Some Examples; 1.3.1 'Getting it all together'; 1.3.2 'In time and space'; 1.3.3 'Keep it simple'; 1.3.4 'How much can we take?'; 1.3.5 'Over the top'; 1.4 Fundamentals; 1.5 Bibliography; PART I EXTREMAL STRESSES: EXTREMES, OUTLIERS, ROBUSTNESS; Chapter 2 Ordering and Extremes: Applications, models, inference; 2.1 Ordering the Sample; 2.1.1 Order statistics; 2.2 Order-based Inference; 2.3 Extremes and Extremal Processes; 2.3.1 Practical study and empirical models 327 $ageneralized extreme-value distributions2.4 Peaks over Thresholds and the Generalized Pareto Distribution; Chapter 3 Outliers and Robustness; 3.1 What is an Outlier?; 3.2 Outlier Aims and Objectives; 3.3 Outlier-Generating Models; 3.3.1 Discordancy and models for outlier generation; 3.3.2 Tests of discordancy for specific distributions; 3.4 Multiple Outliers: Masking and Swamping; 3.5 Accommodation: Outlier-Robust Methods; 3.6 A Possible New Approach to Outliers; 3.7 Multivariate Outliers; 3.8 Detecting Multivariate Outliers; 3.8.1 Principles; 3.8.2 Informal methods; 3.9 Tests of Discordancy 327 $a3.10 Accommodation3.11 Outliers in linear models; 3.12 Robustness in General; PART II COLLECTING ENVIRONMENTAL DATA: SAMPLING AND MONITORING; Chapter 4 Finite-Population Sampling; 4.1 A Probabilistic Sampling Scheme; 4.2 Simple Random Sampling; 4.2.1 Estimating the mean, X; 4.2.2 Estimating the variance, S(2); 4.2.3 Choice of sample size, n; 4.2.4 Estimating the population total, X(T); 4.2.5 Estimating a proportion, P; 4.3 Ratios and Ratio Estimators; 4.3.1 The estimation of a ratio; 4.3.2 Ratio estimator of a population total or mean; 4.4 Stratified (simple) Random Sampling 327 $a4.4.1 Comparing the simple random sample mean and the stratified sample mean4.4.2 Choice of sample sizes; 4.4.3 Comparison of proportional allocation and optimum allocation; 4.4.4 Optimum allocation for estimating proportions; 4.5 Developments of Survey Sampling; Chapter 5 Inaccessible and Sensitive Data; 5.1 Encountered Data; 5.2 Length-Biased or Size-Biased Sampling and Weighted Distributions; 5.2.1 Weighted distribution methods; 5.3 Composite Sampling; 5.3.1 Attribute Sampling; 5.3.2 Continuous variables; 5.3.3 Estimating mean and variance; 5.4 Ranked-Set Sampling 327 $a5.4.1 The ranked-set sample mean5.4.2 Optimal estimation; 5.4.3 Ranked-set sampling for normal and exponential distributions; 5.4.4 Imperfect ordering; Chapter 6 Sampling in the Wild; 6.1 Quadrat Sampling; 6.2 Recapture Sampling; 6.2.1 The Petersen and Chapman estimators; 6.2.2 Capture-recapture methods in open populations; 6.3 Transect Sampling; 6.3.1 The simplest case: strip transects; 6.3.2 Using a detectability function; 6.3.3 Estimating f (y); 6.3.4 Modifications of approach; 6.3.5 Point transects or variable circular plots; 6.4 Adaptive Sampling 327 $a6.4.1 Simple models for adaptive sampling 330 $aIn modern society, we are ever more aware of the environmental issues we face, whether these relate to global warming, depletion of rivers and oceans, despoliation of forests, pollution of land, poor air quality, environmental health issues, etc. At the most fundamental level it is necessary to monitor what is happening in the environment - collecting data to describe the changing scene. More importantly, it is crucial to formally describe the environment with sound and validated models, and to analyse and interpret the data we obtain in order to take action. Environmental Statistics 410 0$aWiley series in probability and statistics. 606 $aMathematical statistics 606 $aEnvironmental sciences$xStatistical methods 615 0$aMathematical statistics. 615 0$aEnvironmental sciences$xStatistical methods. 676 $a363.70072 676 $a519.5 700 $aBarnett$b Vic$012011 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143557803321 996 $aEnvironmental statistics$9725980 997 $aUNINA