{"product_id":"statistical-topics-and-stochastic-models-for-dependent-data-with-applications-hardback-9781786306036","title":"Statistical Topics and Stochastic Models for Dependent Data with Applications (Hardback) 9781786306036","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eStatistical Topics and Stochastic Models for Dependent Data with Applications\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eVlad Stefan Barbu (Edited by), V Barbu (Author), Nicolas Vergne (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781786306036, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 27 November 2020\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e288 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xi\u003cbr\u003e\u003ci\u003eVlad Stefan BARBU and Nicolas VERGNE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1. Markov and Semi-Markov Processes \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Variable Length Markov Chains, Persistent Random Walks: A Close Encounter \u003c\/b\u003e\u003cb\u003e3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePeggy CÉNAC, Brigitte CHAUVIN, Frédéric PACCAUT and Nicolas POUYANNE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction 3\u003c\/p\u003e \u003cp\u003e1.2. VLMCs: definition of the model 6\u003c\/p\u003e \u003cp\u003e1.3. Definition and behavior of PRWs 9\u003c\/p\u003e \u003cp\u003e1.3.1. PRWs in dimension one 9\u003c\/p\u003e \u003cp\u003e1.3.2. PRWs in dimension two 13\u003c\/p\u003e \u003cp\u003e1.4. VLMC: existence of stationary probability measures 15\u003c\/p\u003e \u003cp\u003e1.5. Where VLMC and PRW meet 19\u003c\/p\u003e \u003cp\u003e1.5.1. Semi-Markov chains and Markov additive processes 19\u003c\/p\u003e \u003cp\u003e1.5.2. PRWs induce semi-Markov chains 20\u003c\/p\u003e \u003cp\u003e1.5.3. Semi-Markov chain of the α-LIS in a stable VLMC 22\u003c\/p\u003e \u003cp\u003e1.5.4. The meeting point 23\u003c\/p\u003e \u003cp\u003e1.6. References 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Bootstraps of Martingale-difference Arrays Under the Uniformly Integrable Entropy \u003c\/b\u003e\u003cb\u003e29\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSalim BOUZEBDA and Nikolaos LIMNIOS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction and motivation 29\u003c\/p\u003e \u003cp\u003e2.2. Some preliminaries and notation 30\u003c\/p\u003e \u003cp\u003e2.3. Main results 35\u003c\/p\u003e \u003cp\u003e2.4. Application for the semi-Markov kernel estimators 36\u003c\/p\u003e \u003cp\u003e2.5. Proofs 41\u003c\/p\u003e \u003cp\u003e2.6. References 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. A Review of the Dividend Discount Model: From Deterministic to Stochastic Models \u003c\/b\u003e\u003cb\u003e47\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGuglielmo D’AMICO and Riccardo DE BLASIS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Introduction 47\u003c\/p\u003e \u003cp\u003e3.2. General model 48\u003c\/p\u003e \u003cp\u003e3.3. Gordon growth model and extensions 50\u003c\/p\u003e \u003cp\u003e3.3.1. Gordon model 50\u003c\/p\u003e \u003cp\u003e3.3.2. Two-stage model 51\u003c\/p\u003e \u003cp\u003e3.3.3. H model 52\u003c\/p\u003e \u003cp\u003e3.3.4. Three-stage model 52\u003c\/p\u003e \u003cp\u003e3.3.5. N-stage model 53\u003c\/p\u003e \u003cp\u003e3.3.6. Other extensions 53\u003c\/p\u003e \u003cp\u003e3.4. Markov chain stock models 54\u003c\/p\u003e \u003cp\u003e3.4.1. Hurley and Johnson model 54\u003c\/p\u003e \u003cp\u003e3.4.2. Yao model 56\u003c\/p\u003e \u003cp\u003e3.4.3. Markov stock model 57\u003c\/p\u003e \u003cp\u003e3.4.4. Multivariate Markov chain stock model 61\u003c\/p\u003e \u003cp\u003e3.5. Conclusion 64\u003c\/p\u003e \u003cp\u003e3.6. References 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Estimation of Piecewise-deterministic Trajectories in a Quantum Optics Scenario \u003c\/b\u003e\u003cb\u003e69\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRomain AZA\u003c\/i\u003e\u003ci\u003eЇS and Bruno LEGGIO\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Introduction 69\u003c\/p\u003e \u003cp\u003e4.1.1. The postulates of quantum mechanics 69\u003c\/p\u003e \u003cp\u003e4.1.2. Dynamics of open quantum Markovian systems 71\u003c\/p\u003e \u003cp\u003e4.1.3. Stochastic wave function: quantum dynamics as PDPs 74\u003c\/p\u003e \u003cp\u003e4.1.4. Estimation for PDPs 76\u003c\/p\u003e \u003cp\u003e4.2. Problem formulation 77\u003c\/p\u003e \u003cp\u003e4.2.1. Atom-field interaction 77\u003c\/p\u003e \u003cp\u003e4.2.2. Piecewise-deterministic trajectories 78\u003c\/p\u003e \u003cp\u003e4.2.3. Measures 80\u003c\/p\u003e \u003cp\u003e4.3. Estimation procedure 80\u003c\/p\u003e \u003cp\u003e4.3.1. Strategy 80\u003c\/p\u003e \u003cp\u003e4.3.2. Least-square estimators 82\u003c\/p\u003e \u003cp\u003e4.3.3. Numerical experiments 83\u003c\/p\u003e \u003cp\u003e4.4. Physical interpretation 86\u003c\/p\u003e \u003cp\u003e4.5. Concluding remarks 87\u003c\/p\u003e \u003cp\u003e4.6. References 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Identification of Patterns in a Semi-Markov Chain \u003c\/b\u003e\u003cb\u003e91\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBrenda Ivette GARCIA-MAYA and Nikolaos LIMNIOS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Introduction 91\u003c\/p\u003e \u003cp\u003e5.2. The prefix chain 93\u003c\/p\u003e \u003cp\u003e5.3. The semi-Markov setting 94\u003c\/p\u003e \u003cp\u003e5.4. The hitting time of the pattern 100\u003c\/p\u003e \u003cp\u003e5.5. A genomic application 102\u003c\/p\u003e \u003cp\u003e5.6. Concluding remarks 106\u003c\/p\u003e \u003cp\u003e5.7. References 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2. Autoregressive Processes \u003c\/b\u003e\u003cb\u003e109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Time Changes and Stationarity Issues for Continuous Time Autoregressive Processes of Order \u003ci\u003ep\u003c\/i\u003e \u003c\/b\u003e\u003cb\u003e111\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eValérie GIRARDIN and Rachid SENOUSSI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction 111\u003c\/p\u003e \u003cp\u003e6.2. Basics 112\u003c\/p\u003e \u003cp\u003e6.3. Stationary AR processes 114\u003c\/p\u003e \u003cp\u003e6.3.1. Formulas for the two first-order moments 114\u003c\/p\u003e \u003cp\u003e6.3.2. Examples 116\u003c\/p\u003e \u003cp\u003e6.3.3. Conditions for stationarity of CAR\u003csub\u003e1\u003c\/sub\u003e(\u003ci\u003ep\u003c\/i\u003e) processes 118\u003c\/p\u003e \u003cp\u003e6.4. Time transforms 125\u003c\/p\u003e \u003cp\u003e6.4.1. Properties of time transforms 125\u003c\/p\u003e \u003cp\u003e6.4.2. MS processes 131\u003c\/p\u003e \u003cp\u003e6.5. Conclusion 132\u003c\/p\u003e \u003cp\u003e6.6. Appendix 133\u003c\/p\u003e \u003cp\u003e6.7. References 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Sequential Estimation for Non-parametric Autoregressive Models \u003c\/b\u003e\u003cb\u003e139\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eOuerdia ARKOUN, Jean-Yves BRUA and Serguei PERGAMENCHTCHIKOV\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Introduction 139\u003c\/p\u003e \u003cp\u003e7.2. Main conditions 141\u003c\/p\u003e \u003cp\u003e7.3. Pointwise estimation with absolute error risk 142\u003c\/p\u003e \u003cp\u003e7.3.1. Minimax approach 142\u003c\/p\u003e \u003cp\u003e7.3.2. Adaptive minimax approach 144\u003c\/p\u003e \u003cp\u003e7.3.3. Non-adaptive procedure 145\u003c\/p\u003e \u003cp\u003e7.3.4. Sequential kernel estimator 148\u003c\/p\u003e \u003cp\u003e7.3.5. Adaptive sequential procedure 151\u003c\/p\u003e \u003cp\u003e7.4. Estimation with quadratic integral risk 153\u003c\/p\u003e \u003cp\u003e7.4.1. Passage to a discrete time regression model 155\u003c\/p\u003e \u003cp\u003e7.4.2. Model selection 159\u003c\/p\u003e \u003cp\u003e7.4.3. Main results 161\u003c\/p\u003e \u003cp\u003e7.5. References 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3. Divergence Measures and Entropies \u003c\/b\u003e\u003cb\u003e167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Inference in Parametric and Semi-parametric Models: The Divergence-based Approach \u003c\/b\u003e\u003cb\u003e169\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMichel BRONIATOWSKI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 169\u003c\/p\u003e \u003cp\u003e8.1.1. Csiszár divergences, variational form 170\u003c\/p\u003e \u003cp\u003e8.1.2. Dual form of the divergence and dual estimators in parametric models 172\u003c\/p\u003e \u003cp\u003e8.1.3. Decomposable discrepancies 178\u003c\/p\u003e \u003cp\u003e8.2. Models and selection of statistical criteria 183\u003c\/p\u003e \u003cp\u003e8.3. Non-regular cases: the interplay between the model and the criterion 184\u003c\/p\u003e \u003cp\u003e8.3.1. Test statistics 185\u003c\/p\u003e \u003cp\u003e8.4. References 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. Dynamics of the Group Entropy Maximization Processes and of the Relative Entropy Group Minimization Processes Based on the Speed-gradient Principle \u003c\/b\u003e\u003cb\u003e189\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVasile PREDA and Irina B\u003c\/i\u003e\u003ci\u003eĂNCESCU\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Introduction 190\u003c\/p\u003e \u003cp\u003e9.1.1. The SG principle 191\u003c\/p\u003e \u003cp\u003e9.1.2. Entropy groups 193\u003c\/p\u003e \u003cp\u003e9.2. Group entropies and the SG principle 196\u003c\/p\u003e \u003cp\u003e9.2.1. Total energy constraint 199\u003c\/p\u003e \u003cp\u003e9.3. Relative entropy group and the SG principle 202\u003c\/p\u003e \u003cp\u003e9.3.1. Equilibrium stability 205\u003c\/p\u003e \u003cp\u003e9.3.2. Total energy constraint 205\u003c\/p\u003e \u003cp\u003e9.4. A new (\u003ci\u003eG\u003c\/i\u003e, \u003ci\u003ea\u003c\/i\u003e) power relative entropy group and the SG principle 206\u003c\/p\u003e \u003cp\u003e9.5. Conclusion 210\u003c\/p\u003e \u003cp\u003e9.6. References 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10. Inferential Statistics Based on Measures of Information and Divergence \u003c\/b\u003e\u003cb\u003e215\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlex KARAGRIGORIOU and Christos MESELIDIS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1. Introduction 215\u003c\/p\u003e \u003cp\u003e10.2. Divergence measures 216\u003c\/p\u003e \u003cp\u003e10.2.1. ϕ-Divergences 216\u003c\/p\u003e \u003cp\u003e10.2.2. α-Divergences 217\u003c\/p\u003e \u003cp\u003e10.2.3. Bregman divergences 218\u003c\/p\u003e \u003cp\u003e10.3. Properties of divergence measures 219\u003c\/p\u003e \u003cp\u003e10.4. Model selection criteria 220\u003c\/p\u003e \u003cp\u003e10.5. Goodness of fit tests 222\u003c\/p\u003e \u003cp\u003e10.5.1. Simple null hypothesis 222\u003c\/p\u003e \u003cp\u003e10.5.2. Composite null hypothesis 223\u003c\/p\u003e \u003cp\u003e10.6. Simulation study 227\u003c\/p\u003e \u003cp\u003e10.7. References 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11. Goodness-of-Fit Tests Based on Divergence Measures for Frailty Models \u003c\/b\u003e\u003cb\u003e235\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFilia VONTA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1. Introduction 235\u003c\/p\u003e \u003cp\u003e11.2. The proposed goodness-of-fit test 236\u003c\/p\u003e \u003cp\u003e11.3. Main results 240\u003c\/p\u003e \u003cp\u003e11.4. Frailty models 243\u003c\/p\u003e \u003cp\u003e11.5. Simulations 244\u003c\/p\u003e \u003cp\u003e11.5.1. Linear models for the estimation of critical values 247\u003c\/p\u003e \u003cp\u003e11.5.2. Size of the test 248\u003c\/p\u003e \u003cp\u003e11.6. References 250\u003c\/p\u003e \u003cp\u003eList of Authors 253\u003c\/p\u003e \u003cp\u003eIndex 257\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Education [\u003ca title=\"See our other books on Education\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Education%20%5BJN%5D%22\"\u003eJN\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446749884696,"sku":"9781786306036","price":102.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781786306036.jpg?v=1785112767","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/statistical-topics-and-stochastic-models-for-dependent-data-with-applications-hardback-9781786306036","provider":"Freshly Printed Books","version":"1.0","type":"link"}