01691cam0 22003851 450 SOBE0006719120210713100810.0880204324820210713d1990 |||||ita|0103 baitaITCodice del diritto alimentare annotato con la giurisprudenzaaggiornato al 15 marzo 1990[a cura di] Gianclaudio Andreis, Giuseppe Bertolino, Neva Monaricon la collaborazione di Rossella GallioTorinoUTETc19903 volumi19 cmCodici e leggi annotatiVol. 1.: Parte generale; Vol. 2.: Parte speciale A-L; Vol. 3.: Parte speciale M-Z001LAEC000174922001 *Codici e leggi annotatiAndreis, GianclaudioSOBA00021554070Bertolino, GiuseppeSOBA00021555070Monari, NevaSOBA00021556070Gallio, RossellaSOBA00021557070*ItaliaAF00024563070423419ITUNISOB20210713RICAUNISOBUNISOB340|Ita|Cod95479UNISOB340|Ita|Cod95480UNISOB340|Ita|Cod95481SOBE00067191M 102 Monografia moderna SBNM340|Ita|Cod000005-1SI95479menleUNISOBUNISOB20210713100638.020210713100706.0menle340|Ita|Cod000005-2SI95480menleUNISOBUNISOB20210713100719.020210713100741.0menle340|Ita|Cod000005-3SI95481menleUNISOBUNISOB20210713100743.020210713100810.0menleCodice del diritto alimentare annotato con la giurisprudenza1823896UNISOB01586oam 2200517 450 991071194970332120201022105649.0(CKB)5470000002488595(OCoLC)992996894(OCoLC)995470000002488595(EXLCZ)99547000000248859520170707d2017 ua 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierThe fight goes on the Islamic State's continuing military efforts in liberated cities /Daniel Milton Muhammad al-`UbaydiWest Point, NY :Combating Terrorism Center at West Point, United States Military Academy,2017.1 online resource (IV, 16 pages) color illustrations, color map"June 2017."Includes bibliographical references.Fight goes on TacticsGuerrilla warfareIraqGuerrilla warfareSyriaCivil warIraqSyriaHistoryCivil War, 2011-Tactics.Guerrilla warfareGuerrilla warfareCivil warMilton Daniel J(Daniel James),1398567Combating Terrorism Center (U.S.),DIDDIDOCLCQOCLCOOCLCFOCLCAOCLGPOBOOK9910711949703321The fight goes on3547842UNINA05058nam 2200649Ia 450 991083001640332120170810195456.01-282-30785-197866123078500-470-31656-X0-470-31727-2(CKB)1000000000687558(EBL)469783(OCoLC)264615241(SSID)ssj0000337794(PQKBManifestationID)11276869(PQKBTitleCode)TC0000337794(PQKBWorkID)10293861(PQKB)11780383(MiAaPQ)EBC469783(PPN)159354552(EXLCZ)99100000000068755819830210d1983 uy 0engur|n|---|||||txtccrForecasting with univariate Box-Jenkins models[electronic resource] 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.0-471-09023-9 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 Alan1944-89085MiAaPQMiAaPQMiAaPQBOOK9910830016403321Forecasting with univariate box-Jenkins models196473UNINA