01015nam a2200289 i 450099100110373970753620020507111322.0940318s1980 de ||| | eng b10174151-39ule_instLE00642223ExLDip.to Fisicaita53(06)53(082.2)53.8.253.8.8Beleznay, F.44761New developments in semiconductor physics /edited by F. Beleznay...[et al.]Berlin :Springer-Verlag,1980v, 276 p. ;24 cm.Lecture notes in physics / edited by J. Ehlers...[et al.] ;122Semiconductors.b1017415117-02-1727-06-02991001103739707536LE006 53.8.2 BEL12006000176743le006-E0.00-l- 00000.i1021345427-06-02New developments in semiconductor physics189592UNISALENTOle00601-01-94ma -engde 0100764oam 2200229z- 450 991100651080332120160811171918.01-5231-5575-2(CKB)2430000000037263(EXLCZ)99243000000003726320121018c2000uuuu -u- -engIntegrated design and operation of water treatment facilities /Susumu KawamuraNew YorkJohn Wiley & SonsWater treatment plantsDesign and constructionWater treatment plantsDesign and construction.628.1/62Kawamura Susumu1823579BOOK9911006510803321Integrated design and operation of water treatment facilities4390313UNINA05173nam 2200709Ia 450 991101917010332120200520144314.09786612307850978128230785812823078519780470316566047031656X97804703172730470317272(CKB)1000000000687558(EBL)469783(OCoLC)264615241(SSID)ssj0000337794(PQKBManifestationID)11276869(PQKBTitleCode)TC0000337794(PQKBWorkID)10293861(PQKB)11780383(MiAaPQ)EBC469783(PPN)159354552(Perlego)2760745(EXLCZ)99100000000068755819830210d1983 uy 0engur|n|---|||||txtccrForecasting with univariate Box-Jenkins models concepts and cases /Alan PankratzNew York Wileyc19831 online resource (587 p.)Wiley series in probability and mathematical statistics. Probability and mathematical statistics.,0271-6356Description based upon print version of record.9780471090236 0471090239 Includes bibliography and index.Forecasting With Univariate Box- Jenkins Models CONCEPTS AND CASES; CONTENTS; PART I. BASIC CONCEPTS; 1 Overview; 1.1 Planning and forecasting; 1.2 What this book is about; 1.3 Time-series data; 1.4 Single-series (univariate) analysis; 1.5 When may UBJ models be used?; 1.6 The Box-Jenkins modeling procedure; 1.7 UBJ models compared with other models; Summary; Questions and problems; 2 Introduction to Box-Jenkins analysis of a single data series; 2.1 Differencing; 2.2 Deviations from the mean2.3 Two analytical tools: the estimated autocorrelation function (acf) and estimated partial autocorrelation function (pacf)Summary; Questions and problems; 3 Underlying statistical principles; 3.1 Process, realization, and model; 3.2 Two common processes; 3.3 Statistical inference at the identification stage; Summary; Appendix 3 A: Expected value rules and definitions; Questions and problems; 4 An introduction to the practice of ARIMA modeling; 4.1 What is a good model?; 4.2 Two examples of UBJ-ARIMA modeling; Summary; Questions and problems; 5 Notation and the interpretation of ARIMA models5.1 Three processes and ARIMA (p,d,q) notation5.2 Backshift notation; 5.3 Interpreting ARIMA models I: optimal extrapolation of past values of a single series; 5.4 Interpreting ARIMA models II: rationalizing them from their context; 5.5 Interpreting ARIMA models III: ARIMA(O,d,q) models as exponentially weighted moving averages; Summary; Questions and problems; 6 Identification: stationary models; 6.1 Theoretical acfs and pacf's for five common processes; 6.2 Stationarity; 6.3 Invertibility; 6.4 Deriving theoretical acf's for the MA(1) process6.5 Deriving theoretical acf's for the AR(1) processSummary; Appendix 6A: The formal conditions for stationarity and invertibility; Appendix 6B Invertibility, uniqueness,and forecast performance; Questions and problems; 7 Identification: nonstationary models; 7.1 Nonstationary mean; 7.2 Nonstationary variance; 7.3 Differencing and deterministic trends; Summary; Appendix 7A: Integration; 8 Estimation; 8.1 Principles of estimation; 8.2 Nonlinear least-squares estimation; 8.3 Estimation-stage results: have we found a good model?; Summary; Appendix 8A: Marquardt's compromise; 8A.1 Overview8A.2 Application to an MA(1)Appendix 8B: Backcasting; 8B.1 Conditional least squares; 8B.2 Unconditional least squares; 9 Diagnostic checking; 9.1 Are the random shocks independent?; 9.2 Other diagnostic checks; 9.3 Reformulating a model; Summary; Questions and problems; 10 Forecasting; 10.1 The algebra of ARIMA forecasts; 10.2 The dispersion of ARIMA forecasts; 10.3 Forecasting from data in logarithmic form; 10.4 The optimality of ARIMA forecasts; Summary; Appendix 10A:The complementarity of ARIMA models and econometric models; Questions and problems; 11 Seasonal and other periodic models11.1 Periodic dataExplains the concepts and use of univariate Box-Jenkins/ARIMA analysis and forecasting through 15 case studies. Cases show how to build good ARIMA models in a step-by-step manner using real data. Also includes examples of model misspecification. Provides guidance to alternative models and discusses reasons for choosing one over another.Wiley Series in Probability and StatisticsTime-series analysisPrediction theoryTime-series analysis.Prediction theory.519.54519.55Pankratz Alan1944-89085MiAaPQMiAaPQMiAaPQBOOK9911019170103321Forecasting with univariate box-Jenkins models196473UNINA