LEADER 00773nam0-22002531i-450- 001 990006363480403321 005 19980601 035 $a000636348 035 $aFED01000636348 035 $a(Aleph)000636348FED01 035 $a000636348 100 $a19980601d1995----km-y0itay50------ba 105 $a--------00-yy 200 1 $aEntrepreneurs, Institutions and Economic Change$ethe economic thought of J. A. Schumpeter (1905-1925)$fNicolo De Vecchi 210 $aAldershot$cElgar$d1995 700 1$aDe Vecchi,$bNicolò$0367519 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990006363480403321 952 $aXV F1 105$b34048*$fFGBC 959 $aFGBC 996 $aEntrepreneurs, Institutions and Economic Change$9659104 997 $aUNINA DB $aGIU01 LEADER 01971oam 2200589 450 001 9910715354203321 005 20210420113051.0 035 $a(CKB)5470000002510492 035 $a(OCoLC)761298367 035 $a(OCoLC)995470000002510492 035 $a(EXLCZ)995470000002510492 100 $a20111115j196404 ua 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA wind-compensation method and results of its application to flight tests of twelve trailblazer rocket vehicles /$fby Allen B. Henning, Reginald R. Lundstrom, and Jean C. Keating 210 $aWashington, D.C. $cNational Aeronautics and Space Administration$dApril 1964 215 $a1 online resource (81 pages) $cillustrations 225 1 $aNASA/TN ;$vD-2053 300 $a"April 1964." 320 $aIncludes bibliographical references (page 19). 606 $aFlight tests$2nasat 606 $aCompensation$2nasat 606 $aRocket vehicles$2nasat 606 $aLaunch vehicles (Astronautics)$2fast 606 $aRockets (Aeronautics)$2fast 606 $aSounding rockets$2fast 615 7$aFlight tests. 615 7$aCompensation. 615 7$aRocket vehicles. 615 7$aLaunch vehicles (Astronautics) 615 7$aRockets (Aeronautics) 615 7$aSounding rockets. 700 $aHenning$b Allen B.$01415841 702 $aLundstrom$b Reginald R. 702 $aKeating$b Jean C. 712 02$aUnited States.$bNational Aeronautics and Space Administration, 801 0$bOCLCE 801 1$bOCLCE 801 2$bOCLCQ 801 2$bOCLCF 801 2$bOCLCO 801 2$bOCLCQ 801 2$bGPO 801 2$bOCLCO 801 2$bGPO 906 $aBOOK 912 $a9910715354203321 996 $aA wind-compensation method and results of its application to flight tests of twelve trailblazer rocket vehicles$93519028 997 $aUNINA LEADER 07147nam 2200673 450 001 9910827811503321 005 20230727193926.0 010 $a1-118-74522-1 035 $a(CKB)3710000000391753 035 $a(CaPaEBR)ebrary11041415 035 $a(SSID)ssj0001613456 035 $a(PQKBManifestationID)16338025 035 $a(PQKBTitleCode)TC0001613456 035 $a(PQKBWorkID)14914329 035 $a(PQKB)11215693 035 $a(MiAaPQ)EBC1895570 035 $a(JP-MeL)3000065402 035 $a(Au-PeEL)EBL1895570 035 $a(CaPaEBR)ebr11041415 035 $a(CaONFJC)MIL770015 035 $a(OCoLC)908071059 035 $a(MiAaPQ)EBC7104020 035 $a(Au-PeEL)EBL7104020 035 $a(EXLCZ)993710000000391753 100 $a20150416h20152015 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIntroduction to time series analysis and forecasting /$fDouglas C. Montgomery, Cheryl L. Jennings, Murat Kulahci 205 $aSecond edition. 210 1$aHoboken, New Jersey :$cWiley,$d2015. 210 4$d2015 215 $a1 online resource (671 p.) 225 1 $aWiley Series in Probability and Statistics 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-118-74511-6 311 $a1-118-74512-4 320 $aIncludes bibliographical references and index. 327 $aIntro -- Introduction to Time Series Analysis and Forecasting -- Contents -- Preface -- 1 Introduction to Forecasting -- 1.1 The Nature and Uses of Forecasts -- 1.2 Some Examples of Time Series -- 1.3 The Forecasting Process -- 1.4 Data for Forecasting -- 1.4.1 The Data Warehouse -- 1.4.2 Data Cleaning -- 1.4.3 Imputation -- 1.5 Resources for Forecasting -- Exercises -- 2 Statistics Background for Forecasting -- 2.1 Introduction -- 2.2 Graphical Displays -- 2.2.1 Time Series Plots -- 2.2.2 Plotting Smoothed Data -- 2.3 Numerical Description of Time Series Data -- 2.3.1 Stationary Time Series -- 2.3.2 Autocovariance and Autocorrelation Functions -- 2.3.3 The Variogram -- 2.4 Use of Data Transformations and Adjustments -- 2.4.1 Transformations -- 2.4.2 Trend and Seasonal Adjustments -- 2.5 General Approach to Time Series Modeling and Forecasting -- 2.6 Evaluating and Monitoring Forecasting Model Performance -- 2.6.1 Forecasting Model Evaluation -- 2.6.2 Choosing Between Competing Models -- 2.6.3 Monitoring a Forecasting Model -- 2.7 R Commands for Chapter 2 -- Exercises -- 3 Regression Analysis and Forecasting -- 3.1 Introduction -- 3.2 Least Squares Estimation in Linear Regression Models -- 3.3 Statistical Inference in Linear Regression -- 3.3.1 Test for Significance of Regression -- 3.3.2 Tests on Individual Regression Coefficients and Groups of Coefficients -- 3.3.3 Confidence Intervals on Individual Regression Coefficients -- 3.3.4 Confidence Intervals on the Mean Response -- 3.4 Prediction of New Observations -- 3.5 Model Adequacy Checking -- 3.5.1 Residual Plots -- 3.5.2 Scaled Residuals and PRESS -- 3.5.3 Measures of Leverage and Influence -- 3.6 Variable Selection Methods in Regression -- 3.7 Generalized and Weighted Least Squares -- 3.7.1 Generalized Least Squares -- 3.7.2 Weighted Least Squares -- 3.7.3 Discounted Least Squares. 327 $a3.8 Regression Models for General Time Series Data -- 3.8.1 Detecting Autocorrelation: The Durbin-Watson Test -- 3.8.2 Estimating the Parameters in Time Series Regression Models -- 3.9 Econometric Models -- 3.10 R Commands for Chapter 3 -- Exercises -- 4 Exponential Smoothing Methods -- 4.1 Introduction -- 4.2 First-Order Exponential Smoothing -- 4.2.1 The Initial Value, -- 4.2.2 The Value of l -- 4.3 Modeling Time Series Data -- 4.4 Second-Order Exponential Smoothing -- 4.5 Higher-Order Exponential Smoothing -- 4.6 Forecasting -- 4.6.1 Constant Process -- 4.6.2 Linear Trend Process -- 4.6.3 Estimation of -- 4.6.4 Adaptive Updating of the Discount Factor -- 4.6.5 Model Assessment -- 4.7 Exponential Smoothing for Seasonal Data -- 4.7.1 Additive Seasonal Model -- 4.7.2 Multiplicative Seasonal Model -- 4.8 Exponential Smoothing of Biosurveillance Data -- 4.9 Exponential Smoothers and Arima Models -- 4.10 R Commands for Chapter 4 -- Exercises -- 5 Autoregressive Integrated Moving Average (ARIMA) Models -- 5.1 Introduction -- 5.2 Linear Models for Stationary Time Series -- 5.2.1 Stationarity -- 5.2.2 Stationary Time Series -- 5.3 Finite Order Moving Average Processes -- 5.3.1 The First-Order Moving Average Process, MA(1) -- 5.3.2 The Second-Order Moving Average Process, MA(2) -- 5.4 Finite Order Autoregressive Processes -- 5.4.1 First-Order Autoregressive Process, AR(1) -- 5.4.2 Second-Order Autoregressive Process, AR(2) -- 5.4.3 General Autoregressive Process, AR() -- 5.4.4 Partial Autocorrelation Function, PACF -- 5.5 Mixed Autoregressive-Moving Average Processes -- 5.5.1 Stationarity of ARMA(p, q) Process -- 5.5.2 Invertibility of ARMA(p, q) Process -- 5.5.3 ACF and PACF of ARMA(p, q) Process -- 5.6 Nonstationary Processes -- 5.6.1 Some Examples of ARIMA(p, d, q) Processes -- 5.7 Time Series Model Building -- 5.7.1 Model Identification. 327 $a5.7.2 Parameter Estimation -- 5.7.3 Diagnostic Checking -- 5.7.4 Examples of Building ARIMA Models -- 5.8 Forecasting Arima Processes -- 5.9 Seasonal Processes -- 5.10 Arima Modeling of Biosurveillance Data -- 5.11 Final Comments -- 5.12 R Commands for Chapter 5 -- Exercises -- 6 Transfer Functions and Intervention Models -- 6.1 Introduction -- 6.2 Transfer Function Models -- 6.3 Transfer Function-Noise Models -- 6.4 Cross-Correlation Function -- 6.5 Model Specification -- 6.6 Forecasting with Transfer Function-Noise Models -- 6.7 Intervention Analysis -- 6.8 R Commands for Chapter 6 -- Exercises -- 7 Survey of Other Forecasting Methods -- 7.1 Multivariate Time Series Models and Forecasting -- 7.1.1 Multivariate Stationary Process -- 7.1.2 Vector ARIMA Models -- 7.1.3 Vector AR (VAR) Models -- 7.2 State Space Models -- 7.3 Arch and Garch Models -- 7.4 Direct Forecasting of Percentiles -- 7.5 Combining Forecasts to Improve Prediction Performance -- 7.6 Aggregation and Disaggregation of Forecasts -- 7.7 Neural Networks and Forecasting -- 7.8 Spectral Analysis -- 7.9 Bayesian Methods in Forecasting -- 7.10 Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures -- 7.11 R Commands for Chapter 7 -- Exercises -- APPENDIX A Statistical Tables -- APPENDIX B Data Sets for Exercises -- APPENDIX C Introduction to R -- BASIC CONCEPTS IN R -- Bibliography -- Index -- EULA. 410 0$aWiley series in probability and statistics. 606 $aForecasting 606 $aTime-series analysis 615 0$aForecasting. 615 0$aTime-series analysis. 676 $a519.55 700 $aMontgomery$b Douglas C.$09293 702 $aJennings$b Cheryl L. 702 $aKulahci$b Murat 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910827811503321 996 $aIntroduction to time series analysis and forecasting$91909391 997 $aUNINA