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[null,"1.","Record Nr.",null,"UNISA996387096703316"]
[null,null,"Autore",null,"Sanderson Robert <1587-1663.>"]
[null,null,"Titolo",null,"De juramenti promissorii obligatione prælectiones septem [[electronic resource] ] : habitæ in schola theologicà Oxon. termino Michaelis ann. Dom. MDCXLVI / / a Roberto Sandersono ... præmissa oratione ab codem habita cum publicam professionem auspicaretur 26 Octob. 1646"]
[null,null,"Pubbl/distr/stampa",null,"Londini, : Typis T.R. & E.M. prostat vænale apud O. Pullen & A. Crook ..., 1647"]
[null,null,"Descrizione fisica",null,"[32], 221 p"]
[null,null,"Soggetti",null,"Oaths - Biblical teaching"]
[{"$nulls":4},"Loyalty oaths"]
[null,null,"Lingua di pubblicazione",null,"Latino"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Running title: De juramenti obligatione."]
[{"$nulls":4},"Reproduction of original in: Cambridge University Library."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references in marginal notes."]
[null,null,null,"Sommario/riassunto","eebo-0021"]
[null,"2.","Record Nr.",null,"UNISA996466399803316"]
[null,null,"Titolo",null,"Festschrift in honor of R. Dennis Cook : fifty years of contribution to statistical science / / Efstathia Bura, Bing Li, editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-69009-1"]
[null,null,"Descrizione fisica",null,"1 online resource (200 pages)"]
[null,null,"Disciplina",null,"519.5"]
[null,null,"Soggetti",null,"Statistics"]
[{"$nulls":4},"Mathematical statistics"]
[{"$nulls":4},"Estadística matemàtica"]
[{"$nulls":4},"Homenatges"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Foreword -- A Tribute to Professor R. Dennis Cook -- Contents -- Using Mutual Information to Measure the Predictive Powerof Principal Components -- 1 Introduction -- 2 Overview of Previous Results -- 3 Conditional Mutual Information -- 3.1 Under the Linear Model -- 3.2 Beyond the Linear Regression Model -- 3.3 Beyond the Normal Distribution -- 4 Discussion -- References -- A Robust Estimation Approach for Mean-Shiftand Variance-Inflation Outliers -- 1 Introduction -- 2 Our Proposal and Some Background -- 2.1 A Generalized Setting -- 2.2 Some Technical Background -- 2.3 Our Proposal -- 2.4 Graphical Diagnostics -- 3 Simulation Study -- 4 Real-Data Examples -- 5 Final Remarks -- References -- Estimating Sufficient Dimension Reduction Spaces by Invariant Linear Operators -- 1 Introduction -- 2 Invariant Linear Operators -- 3 Invariant Linear Operator and Its Eigenvectors -- 4 Some Important Members of T Y|X -- 4.1 Sliced Average Variance Estimation -- 4.2 SIR-II -- 4.3 Contour Regression -- 4.4 Directional Regression -- 5 Two Estimation Methods Based on Invariant Operators -- 5.1 Iterative Invariant Transformations (IIT) -- 5.2 Nonparametrically Boosted Inverse Regression (NBIR) -- 6 Numerical Study -- 7 Concluding Remarks -- References -- Testing Model Utility for Single Index Models Under High Dimension -- 1 Introduction -- 2 Generalized SNR for Single Index Models -- 2.1 "]
[{"$nulls":4},"Notation -- 2.2 A Brief Review of the Sliced Inverse Regression (SIR) -- 2.3 Generalized Signal-to-Noise Ratio of Single Index Models -- 2.4 Global Testing for Single Index Models -- 3 The Optimal Test for Single Index Models -- 3.1 The Detection Boundary of Linear Regression -- 3.2 Single Index Models -- 3.3 Optimal Test for SIMa -- 3.4 Computationally Efficient Test -- 3.5 Practical Issues -- 4 Numerical Studies -- 5 Discussion -- Appendix: Proofs -- Assisting Lemmas."]
[{"$nulls":4},"Proof of Theorems -- References -- Sliced Inverse Regression for Spatial Data -- 1 Introduction -- 2 SIR for iid Data -- 3 SIR for Time Series Data -- 4 SIR for Spatial Data -- 5 Performance Evaluation of SSIR -- 6 Discussion -- References -- Model-Based Inverse Regression and Its Applications -- 1 Introduction -- 1.1 Model-Based Inverse Reduction -- 1.2 Sufficient Reduction in Applications -- 2 Inverse Reduction for Multivariate Count Data -- 2.1 Multinomial Inverse Regression in Text Analysis -- 2.2 Predictive Learning in Metagenomics via Inverse Regression -- 2.3 Poisson Graphical Inverse Regression -- 3 Inverse Reduction and Its Dual -- 3.1 Reduction via Principal Coordinate Analysis -- 3.2 A Supervised Inverse Regression Model -- 4 Adaptive Independence Test via Inverse Regression -- 5 Cook's Contributions on Model-Based Sufficient Reduction -- References -- Sufficient Dimension Folding with Categorical Predictors -- 1 Introduction -- 2 Review on Sufficient Dimension Folding -- 3 Sufficient Dimension Folding with Categorical Predictors -- 4 Estimation Methods -- 4.1 Individual Direction Ensemble Method -- 4.2 Least Squares Folding Approach (LSFA) -- 4.3 Objective Function Optimization Method -- 5 Estimation of Structural Dimensions -- 6 Numerical Analysis -- 6.1 Simulation Studies -- 6.1.1 Part I (Continuous Y, Forward Model) -- 6.1.2 Part II (Discrete Y, Inverse Model) -- 6.2 Application -- 7 Discussion -- 8 Appendix -- 8.1 Proofs -- 8.2 Additional Simulation and Data Analysis -- Three Histograms for the Real Data -- The Bootstrap Confidence Interval Plots for Real Data -- References -- Sufficient Dimension Reduction Through Independenceand Conditional Mean Independence Measures -- 1 Introduction -- 2 Estimating SY|X Through α-Distance Covariance -- 2.1 α-Distance Covariance -- 2.2 Estimation of the Central Space."]
[{"$nulls":4},"3 Estimating SE(Y|X) Through α-Martingale Difference Divergence -- 3.1 α-Martingale Difference Divergence -- 3.2 Estimation of the Central Mean Space -- 4 Simulation Studies -- 4.1 Model Setup -- 4.2 Comparisons of Estimating the Central Space -- 4.3 Comparisons of Estimating the Central Mean Space -- 5 Analysis of the Iris Data -- 6 Conclusion -- Appendix -- References -- Cook's Fisher Lectureship Revisited for Semi-supervised DataReduction -- 1 Introduction -- 2 Dimension Reduction by Isotonic Models -- 2.1 Construction of Isotonic Model -- 2.2 Maximum Likelihood Estimation of Γ -- 3 Numerical Examples -- 4 Real Data Example -- 5 Discussion -- References."]