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[null,"1.","Record Nr.",null,"UNINA9910796194003321"]
[null,null,"Autore",null,"Mettra Mélanie"]
[null,null,"Titolo",null,"Le débarquement de Normandie : overlord, l'opération décisive de la seconde guerre mondiale / / par Mélanie Mettra ; avec la collaboration de Antoine Baudry"]
[null,null,"Pubbl/distr/stampa",null,"[Place of publication not identified] : , : 50Minutos.es, , [2014]"]
[{"$nulls":4},"©2014"]
[null,null,"ISBN",null,"2-8062-5421-3"]
[null,null,"Descrizione fisica",null,"1 online resource (39 p.)"]
[null,null,"Collana",null,"Grandes batailles ; ; Numéro 1"]
[null,null,"Disciplina",null,"940.542142"]
[null,null,"Soggetti",null,"World War, 1939-1945 - Campaigns - France - Normandy"]
[null,null,"Lingua di pubblicazione",null,"Francese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Sommario/riassunto","Découvrez enfin tout ce qu'il faut savoir sur le débarquement de Normandie en moins d'une heure !Nous sommes à l'aube du 6 juin 1944. Il est 6 heures du matin lorsque les forces alliées débarquent sur les plages de Normandie. L'enjeu est de taille : il s'agit de porter un coup fatal au IIIe Reich d'Adolf Hitler et de libérer les territoires occupés. Le jour le plus long vient tout juste de se lever. Tout en nous plongeant au cœur de ce conflit majeur, ce livre n'omet aucun détail. Vous y trouverez : des explications sur le conflit qui se mondialise, le profil des acteurs qui ont activement pa"]
[null,"2.","Record Nr.",null,"UNINA9910254205303321"]
[null,null,"Titolo",null,"Causal Inference in Econometrics / / edited by Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016"]
[null,null,"ISBN",null,"3-319-27284-5"]
[null,null,"Edizione",null,"[1st ed. 2016.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XI, 638 p. 106 illus., 15 illus. in color.)"]
[null,null,"Collana",null,"Studies in Computational Intelligence, , 1860-9503 ; ; 622"]
[null,null,"Disciplina",null,"330.015195"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Social sciences - Mathematics"]
[{"$nulls":4},"Security systems"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Mathematics in Business, Economics and Finance"]
[{"$nulls":4},"Security Science and Technology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- Part I  Fundamental Theory -- Validating Markov Switching VAR Through Spectral Representations -- 1 Introduction -- 2 Spectra of Markov Switching VAR -- 2.1 The Case of Hidden Markov Process -- 2.2 The Case of MS VAR(p) -- 3 Frequency Variability in Real Data -- 4 Conclusion -- References -- Rapid Optimal Lag Order Detection  and Parameter Estimation of Standard  Long Memory Time Series -- 1 Introduction -- 2 Preliminaries -- 2.1 Fractionally Differenced Long Memory Processes -- 3 State Space Representation of an ARFIMA Time Series -- 3.1 State Space Representation of ARFIMA Model -- 3.2 KF and Estimation Process -- 4 Simulation Results -- 5 Empirical Evidence -- 6 Concluding Remarks -- References -- Spatial Econometric Analysis: Potential Contribution to the Economic Analysis  of Smallholder Development -- 1 Introduction -- 2 Advances in Data to Capture Spatial Heterogeneity -- 2.1 GIS and GPS Mapping -- 2.2 Big Data -- 2.3 Increased Availability of Panel Data Sets -- 3 Review of Existing Literature on Spatial Econometric Analysis of Smallholder Development -- 3.1 Recent Progress in Spatial Econometric Modelling -- 3.2 Use of Spatial Econometric Analysis to Study Spillovers and Spatial Interaction -- 3.3 Environmental and Land Use Applications -- 3.4 Accounting for "]
[{"$nulls":4},"Space in Analyses of Technology Adoption and Productivity -- 4 Potential Areas for Analysis Using Spatial Econometric Methods: Examples from the Philippines -- 4.1 Spatial Heterogeneity in the Rural Sector  of the Philippines: Example of Rice Ecosystems -- 4.2 Assessment of Smallholder Response to Rural Development Interventions -- 4.3 Measuring, Decomposing and Explaining TFP Growth  in Smallholder Farming -- 5 Prospects and Conclusions -- References -- Consistent Re-Calibration in Yield Curve Modeling: An Example -- 1 Introduction."]
[{"$nulls":4},"2 Hull--White Extended Discrete-Time Vasiček Model -- 2.1 Discrete-Time (One-Factor) Vasiček Model -- 2.2 Hull--White Extended Version of the Vasiček Model -- 2.3 Calibration of Hull--White Extension -- 3 Consistent Re-Calibration Models -- 3.1 Consistent Re-Calibration Algorithm -- 3.2 Heath-Jarrow-Morton Representation of the CRC Algorithm -- 4 Real World Dynamics and Market-Price of Risk -- 5 Choice of Parameter Process -- 5.1 Pricing Model Approach Interpretation -- 5.2 Historical Calibration of the Prediction Model -- 5.3 Continuous-Time Modeling Motivated Inference -- 6 Conclusions -- 7 Swiss Currency CHF Example -- References -- Autoregressive Conditional Duration Model with an Extended Weibull Error Distribution -- 1 Introduction -- 2 Extended Weibull Distribution -- 2.1 Properties of EW Distribution -- 3 ACD Model with EW Distribution -- 4 Bayesian Estimation Methodology -- 5 Simulation Study -- 5.1 Random Variates Generation -- 5.2 Simulation -- 6 Empirical Analysis -- 6.1 Trade Duration Data -- 6.2 Daily Range Data -- 7 Conclusion -- References -- Across-the-Board Spending Cuts Are Very Inefficient: A Proof -- 1 Formulation of the Problem: Are Across-the-Board Spending Cuts Economically Reasonable -- 2 Let Us Formulate the Problem in Precise Terms -- 3 Analysis of the Problem -- References -- Invariance Explains Multiplicative  and Exponential Skedactic Functions -- 1 Why Are Multiplicative and Exponential Skedactic Functions Empirically Successful: Formulation  of the Problem -- 2 Natural Invariances -- 3 Case of Scale Invariance: Definitions and the Main Result -- 4 Case of Shift-Invariance: Definitions and the Main Result -- 5 General Case -- 6 Proofs -- References -- Why Some Families of Probability Distributions Are Practically  Efficient: A Symmetry-Based Explanation -- 1 Formulation of the Problem -- 2 Our Main Idea."]
[{"$nulls":4},"3 Which Objective Functions Are Invariant? -- 4 Which Constraints Are Invariant? -- 5 Invariant Objective Functions and Constraints: Summary -- 6 Resulting Distributions -- 6.1 All Constraints Are Both Shift- and Scale-Invariant, Objective Function is Entropy -- 6.2 All Constraints Are Both Shift- and Scale-Invariant, Objective Function is Generalized Entropy -- 6.3 All Constraints Are Scale-Invariant Relative  to the Same Value x0, Objective Function is Entropy -- 6.4 All Constraints Are Shift-Invariant, Objective Function Is Entropy -- 6.5 All Constraints Are Shift-Invariant, Objective Function Is Generalized Entropy -- 6.6 Different Constraints Have Different Symmetries, Objective Function Is Entropy -- 6.7 Different Constraints Have Different Symmetries, Objective Function is Generalized Entropy -- 7 Conclusion -- References -- The Multivariate Extended Skew Normal Distribution and Its Quadratic Forms -- 1 Introduction -- 2 The Multivariate Extended Skew Normal Distribution -- 3 Extended Noncentral Skew Chi-Square Distributions -- 4 The Distribution of Quadratic Form of Y -- References -- Multiple Copula Regression Function  and Directional Dependence Under Multivariate Non-exchangeable Copulas -- 1 Introduction -- 2 Multivariate Copula Based Directional Dependence -- 2.1 Multivariate Non-exchangeable Copulas -- 2.2 Directional Dependence Using Copula-Based Multiple Regression -- 3 Multivariate Non-exchangeable "]
[{"$nulls":4},"Copulas and Their Application to Directional Dependence -- 3.1 Skew Normal Copulas -- 3.2 Multivariate Non-exchangeable Generalized  FGM Copula -- References -- On Consistency of Estimators Based  on Random Set Vector Observations -- 1 Introduction -- 2 Characterization of the Joint Belief Function of Discrete Random Set Vector -- 3 Bivariate CAR Models -- 4 The Likelihood Function of Random Set Vector Observations -- References."]
[{"$nulls":4},"Brief Introduction to Causal Compositional Models -- 1 Introduction -- 2 Notation and Basic Concepts -- 3 Compositional Models -- 4 Causal Models -- 5 Intervention -- 6 Hidden Variables -- 7 Conclusions -- References -- A New Proposal to Predict Corporate Bankruptcy in Italy During the 2008 Economic Crisis -- 1 Introduction -- 2 The Algorithm -- 3 Experimental Results -- 3.1 The Data -- 3.2 Performance Assessment -- 3.3 Results -- 4 Conclusions -- References -- Part II  Applications -- The Inflation Hedging Ability of Domestic Gold in Malaysia -- 1 Introduction -- 2  Gold investment in Malaysia -- 3 Literature Review -- 4  Data and Methodology -- 5 Results -- References -- To Determine the Key Factors for Citizen  in Selecting a Clinic/Division in Thailand -- 1 Introduction -- 2 Literature Review -- 2.1 Decision Customers Make Before Going  to Clinic/Hospital -- 2.2 Grey Relational Analysis (GRA) -- 3 Questionnaire Design -- 4 Analysis Results -- 5 Conclusion -- References -- ARIMA Versus Artificial Neural Network  for Thailand's Cassava Starch Export Forecasting -- 1 Introduction -- 2 Literature Review -- 3 Cassava Starch Export Time Series -- 4 Forecasting Accuracy Measures -- 5 ARIMA Models for Cassava Starch Export Forecasting -- 5.1 ARIMA Models -- 5.2 Forecasting Accuracy of the ARIMA Models -- 6 Artificial Neural Network Models for Cassava Starch Export Forecasting -- 6.1 Input Layer -- 6.2 Output Layer -- 6.3 Hidden Layer -- 7 Comparison of the ANN Models with the ARIMA Models -- 8 Conclusion -- References -- Copula Based Volatility Models and Extreme Value Theory for Portfolio Simulation  with an Application to Asian Stock Markets -- 1 Introduction -- 2 Methodology -- 2.1 Marginal Models -- 2.2 The Distributions of Standardized Residuals -- 2.3 Copula Approach -- 2.4 Portfolio Simulation -- 3 Empirical Results -- 4 Conclusions -- References."]
[{"$nulls":4},"Modeling Dependence of Health Behaviors Using Copula-Based Bivariate Ordered Probit -- 1 Introduction -- 2 Data -- 3 Copula-Based Bivariate Ordered Probit Models -- 4 Results and Discussion -- 4.1 Factors Affecting Alcohol Consumption and Physical Activity Behaviors -- 4.2 Factors Affecting Tobacco Consumption and Physical Activity Behaviors -- 4.3 Factors Affecting Alcohol Consumption and Tobacco Consumption Behaviors -- 4.4 Dependence Measures of Health Behaviors Pairs -- 5 Concluding Remarks -- References -- Reinvestigating the Effect of Alcohol Consumption on Hypertension Disease -- 1 Introduction -- 2 Data -- 3 Switching Regression Model for Level of Hypertension -- 4 Results and Discussion -- 4.1 Binary Choice Equation for Alcohol Consumption -- 4.2 Factors Affecting Hypertension Level for Non-alcohol Users -- 4.3 Factors Affecting Hypertension Level for Alcohol Users -- 4.4 Effect of Alcohol Consumption on Blood Pressure Level -- 5 Concluding Remarks -- References -- Optimizing Stock Returns Portfolio Using  the Dependence Structure Between Capital  Asset Pricing Models: A Vine Copula-Based Approach -- 1 Introduction -- 2 Copulas and Vine Copulas -- 2.1 Vine Copulas -- 2.2 Drawable Vine (D-vine) -- 2.3 Canonical Vine (C-vine) -- 3 An Application and Empirical Results -- 3.1 Capital Asset Pricing Model:CAPM -- 3.2 Optimal Portfolio with Conditional Value  at Risk via Vine-Copulas -- 3.3 Data -- 3.4 "]
[{"$nulls":4},"Experimental Results -- 4 Concluding Remarks -- References -- Analysis of Transmission and Co-Movement of Rice Export Prices Between Thailand  and Vietnam -- 1 Introduction -- 2 Methodology -- 2.1 VAR Models -- 2.2 Copulas -- 2.3 Model Validation -- 3 Empirical Results -- 3.1 The Data -- 3.2 Causality Tests and Impulse Response -- 3.3 Estimate Results of Copulas -- 4 Conclusions -- References."]
[{"$nulls":4},"Modeling Co-Movement and Risk Management of Gold and Silver  Spot Prices."]
[null,null,null,"Sommario/riassunto","This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies."]