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Record Nr. |
UNINA9910283256803321 |
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Autore |
Dossetti, Maria |
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Titolo |
La nuova filiazione : accertamento e azioni di stato / Maria Dossetti, Mimma Moretti, Carola Moretti |
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Pubbl/distr/stampa |
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Bologna : Zanichelli, 2017 |
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ISBN |
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Descrizione fisica |
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Collana |
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Le riforme del diritto italiano ; 26 |
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Altri autori (Persone) |
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Moretti, Mimma |
Moretti, Carola |
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Disciplina |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910831067803321 |
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Autore |
Geweke John |
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Titolo |
Contemporary Bayesian econometrics and statistics [[electronic resource] /] / John Geweke |
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Pubbl/distr/stampa |
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Hoboken, N.J., : John Wiley, c2005 |
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ISBN |
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1-280-27761-0 |
9786610277612 |
0-470-23694-9 |
0-471-74473-5 |
0-471-74472-7 |
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Descrizione fisica |
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1 online resource (322 p.) |
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Collana |
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Wiley Series in Probability and Statistics ; ; v.537 |
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Disciplina |
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330.015195 |
330.01519542 |
330/.01/519542 |
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Soggetti |
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Econometrics |
Bayesian statistical decision theory |
Decision making - Mathematical models |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Contemporary Bayesian Econometrics and Statistics; Contents; Preface; 1. Introduction; 1.1 Two Examples; 1.1.1 Public School Class Sizes; 1.1.2 Value at Risk; 1.2 Observables, Unobservables, and Objects of Interest; 1.3 Conditioning and Updating; 1.4 Simulators; 1.5 Modeling; 1.6 Decisionmaking; 2. Elements of Bayesian Inference; 2.1 Basics; 2.2 Sufficiency, Ancillarity, and Nuisance Parameters; 2.2.1 Sufficiency; 2.2.2 Ancillarity; 2.2.3 Nuisance Parameters; 2.3 Conjugate Prior Distributions; 2.4 Bayesian Decision Theory and Point Estimation; 2.5 Credible Sets; 2.6 Model Comparison |
2.6.1 Marginal Likelihoods2.6.2 Predictive Densities; 3. Topics in Bayesian Inference; 3.1 Hierarchical Priors and Latent Variables; 3.2 Improper Prior Distributions; 3.3 Prior Robustness and the Density Ratio Class; 3.4 Asymptotic Analysis; 3.5 The Likelihood Principle; 4. Posterior Simulation; 4.1 Direct Sampling; 4.2 Acceptance and Importance Sampling; 4.2.1 Acceptance Sampling; 4.2.2 Importance |
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Sampling; 4.3 Markov Chain Monte Carlo; 4.3.1 The Gibbs Sampler; 4.3.2 The Metropolis-Hastings Algorithm; 4.4 Variance Reduction; 4.4.1 Concentrated Expectations; 4.4.2 Antithetic Sampling |
4.5 Some Continuous State Space Markov Chain Theory4.5.1 Convergence of the Gibbs Sampler; 4.5.2 Convergence of the Metropolis-Hastings Algorithm; 4.6 Hybrid Markov Chain Monte Carlo Methods; 4.6.1 Transition Mixtures; 4.6.2 Metropolis within Gibbs; 4.7 Numerical Accuracy and Convergence in Markov Chain Monte Carlo; 5. Linear Models; 5.1 BACC and the Normal Linear Regression Model; 5.2 Seemingly Unrelated Regressions Models; 5.3 Linear Constraints in the Linear Model; 5.3.1 Linear Inequality Constraints |
5.3.2 Conjectured Linear Restrictions, Linear Inequality Constraints, and Covariate Selection5.4 Nonlinear Regression; 5.4.1 Nonlinear Regression with Smoothness Priors; 5.4.2 Nonlinear Regression with Basis Functions; 6. Modeling with Latent Variables; 6.1 Censored Normal Linear Models; 6.2 Probit Linear Models; 6.3 The Independent Finite State Model; 6.4 Modeling with Mixtures of Normal Distributions; 6.4.1 The Independent Student-t Linear Model; 6.4.2 Normal Mixture Linear Models; 6.4.3 Generalizing the Observable Outcomes; 7. Modeling for Time Series |
7.1 Linear Models with Serial Correlation7.2 The First-Order Markov Finite State Model; 7.2.1 Inference in the Nonstationary Model; 7.2.2 Inference in the Stationary Model; 7.3 Markov Normal Mixture Linear Model; 8. Bayesian Investigation; 8.1 Implementing Simulation Methods; 8.1.1 Density Ratio Tests; 8.1.2 Joint Distribution Tests; 8.2 Formal Model Comparison; 8.2.1 Bayes Factors for Modeling with Common Likelihoods; 8.2.2 Marginal Likelihood Approximation Using Importance Sampling; 8.2.3 Marginal Likelihood Approximation Using Gibbs Sampling |
8.2.4 Density Ratio Marginal Likelihood Approximation |
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Sommario/riassunto |
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Tools to improve decision making in an imperfect worldThis publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data.The b |
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