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1. Shrinkage estimation [2018]
 Cham, Switzerland : Springer, [2018]
 Description
 Book — 1 online resource. Digital: text file; PDF.
 Summary

 Chapter 1. Decision Theory Preliminaries.
 Chapter 2. Estimation of a normal mean vector I.
 Chapter 3. Estimation of a normal mean vector II.
 Chapter 4. Spherically symmetric distributions.
 Chapter 5. Estimation of a mean vector for spherically symmetric distributions I: known scale.
 Chapter 6. Estimation of a mean vector for spherically symmetric distributions II: with a residual.
 Chapter 7. Restricted Parameter Spaces.
 Chapter 8. Loss and Confidence Level Estimation..
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9783030021849 20190114
 Ramsay, J. O. (James O.), author.
 New York, NY : Springer, [2017]
 Description
 Book — xvii, 230 pages : illustrations ; 25 cm.
 Summary

 1. Introduction to Dynamic Models 1.1 Six Examples of Input/Output Dynamics 1.1.1 Smallpox in Montreal 1.1.2 Spread of Disease Equations 1.1.3 Filling a Container 1.1.4 Head Impact and Brain Acceleration 1.1.5 Compartment models and pharmacokinetics 1.1.6 Chinese handwriting 1.1.7 Where to go for More Dynamical Systems 1.2 What This Book Undertakes 1.3 Mathematical Requirements 1.4 Overview
 2 DE notation and types 2.1 Introduction and Chapter Overview 2.2 Notation for Dynamical Systems 2.2.1 Dynamical System Variables 2.2.2 Dynamical System Parameters 2.2.3 Dynamical System Data Configurations 2.2.4 Mathematical Background 2.3 The Architecture of Dynamic Systems 2.4 Types of Differential Equations 2.4.1 Linear Differential Equations 2.4.2 Nonlinear Dynamical Systems 2.4.3 Partial Differential Equations 2.4.4 Algebraic and Other Equations 2.5 Data Configurations 2.5.1 Initial and Boundary Value Configurations 2.5.2 Distributed Data Configurations 2.5.3 Unobserved or Lightly Observed Variables 2.5.4 Observational Data and Measurement Models 2.6 Differential Equation Transformations 2.7 A Notation Glossary
 3 Linear Differential Equations and Systems 3.1 Introduction and Chapter Overview 3.2 The First Order Stationary Linear Buffer 3.3 The Second Order Stationary Linear Equation 3.4 The mth Order Stationary Linear Buffer 3.5 Systems of Linear Stationary Equations 3.6 A Linear System Example: Feedback Control 3.7 Nonstationary Linear Equations and Systems 3.7.1 The First Order Nonstationary Linear Buffer 3.7.2 First Order Nonstationary Linear Systems 3.8 Linear Differential Equations Corresponding to Sets of Functions 3.9 Green's Functions for Forcing Function Inputs
 4 Nonlinear Differential Equations 4.1 Introduction and Chapter Overview 4.2 The Soft Landing Modification 4.3 Existence and Uniqueness Results 4.4 Higher Order Equations 4.5 Input/Output Systems 4.6 Case Studies 4.6.1 Bounded Variation: The Catalytic Equation 4.6.2 Rate Forcing: The SIR Spread of Disease System 4.6.3 From Linear to Nonlinear: The FitzHughNagumo Equations 4.6.4 Nonlinear Mutual Forcing: The Tank Reactor Equations 4.6.5 Modeling Nylon Production
 5 Numerical Solutions 5.1 Introduction 5.2 Euler Methods 5.3 RungeKuttaMethods 5.4 Collocation Methods 5.5 Numerical Problems 5.5.1 Stiffness 5.5.2 Discontinuous Inputs 5.5.3 Constraints and Transformations <
 6 Qualitative Behavior 6.1 Introduction 6.2 Fixed Points 6.2.1 Stability 6.3 Global Analysis and Limit Cycles 6.3.1 Use of Conservation Laws 6.3.2 Bounding Boxes 6.4 Bifurcations 6.4.1 Transcritical Bifurcations 6.4.2 Saddle Node Bifurcations 6.4.3 Pitchfork Bifurcations 6.4.4 Hopf Bifurcations 6.5 Some Other Features 6.5.1 Chaos 6.5.2 FastSlow Systems 6.6 Nonautonomous Systems 6.7 Commentary
 7 Trajectory Matching 7.1 Introduction 7.2 GaussNewton Minimization 7.2.1 Sensitivity Equations 7.2.2 Automatic Differentiation 7.3 Inference 7.4 Measurements on Multiple Variables 7.4.1 Multivariate GaussNewton Method 7.4.2 VariableWeighting using Error Variance 7.4.3 Estimating s2 7.4.4 Example: FitzHughNagumoModels 7.4.5 Practical Problems: Local Minima 7.4.6 Initial Parameter Values for the Chemostat Data 7.4.7 Identifiability 7.5 Bayesian Methods 7.6 Multiple Shooting and Collocation 7.7 Fitting Features 7.8 Applications: Head Impacts
 8 Gradient Matching 8.1 Introduction 8.2 Smoothing Methods and Basis Expansions 8.3 Fitting the Derivative 8.3.1 Optimizing Integrated Squared Error (ISSE) 8.3.2 Gradient Matching for the Refinery Data 8.3.3 Gradient Matching and the Chemostat Data 8.4 System Misspecification and Diagnostics 8.4.1 Diagnostic Plots 8.5 Conducting Inference 8.5.1 Nonparametric Smoothing Variances 8.5.2 Example: Refinery Data 8.6 Related Methods and Extensions 8.6.1 Alternative Smoothing Method 8.6.2 Numerical Discretization Methods 8.6.3 Unobserved Covariates 8.6.4 Nonparametric Models 8.6.5 Sparsity and High Dimensional ODEs 8.7 Integral Matching 8.8 Applications: Head Impacts
 9 Profiling for Linear Systems 9.1 Introduction and Chapter Overview 9.2 Parameter Cascading 9.2.1 Two Classes of Parameters 9.2.2 Defining Coefficients as Functions of Parameters 9.2.3 Data/Equation Symmetry 9.2.4 Inner Optimization Criterion J 9.2.5 The Least Squares Cascade Coefficient Function 9.2.6 The Outer Fitting Criterion H 9.3 Choosing the Smoothing Parameter r 9.4 Confidence Intervals for Parameters 9.4.1 Simulation Sample Results 9.5 MultiVariable Systems 9.6 Analysis of the Head Impact Data 9.7 A Feedback Model for Driving Speed 9.7.1 TwoVariable First Order Cruise Control Model 9.7.2 OneVariable Second Order Cruise Control Model 9.8 The Dynamics of the Canadian Temperature Data 9.9 Chinese Handwriting 9.10 Complexity Bases 9.11 Software and Computation 9.11.1 Rate Function Specifications 9.11.2 Model Term Specifications 9.11.3 Memoization
 10 Nonlinear Profiling 10.1 Introduction and Chapter Overview 10.2 Parameter Cascading for Nonlinear Systems 10.2.1 The Setup for Parameter Cascading 10.2.2 Parameter Cascading Computations 10.2.3 Some Helpful Tips 10.2.4 Nonlinear Systems and Other Fitting Criteria 10.3 LotkaVolterra 10.4 Head Impact 10.5 Compound Model for Blood Ethanol 10.6 Catalytic model for growth 10.7 Aromate Reactions References Glossary Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781493971886 20171002
 Online
Science Library (Li and Ma)
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QA278 .R354 2017  Unknown 
 Gooijer, Jan G. De, author.
 Cham, Switzerland : Springer, [2017]
 Description
 Book — 1 online resource.
 Online

 EBSCOhost Access limited to 1 user
 Google Books (Full view)
 Yi, Grace Y.
 New York : Springer, c2017.
 Description
 Book — 1 online resource.
 Summary

 Inference Framework and Method. Measurement Error and Misclassification: Introduction. Survival Data with Measurement Error. Recurrent Event Data with Measurement Error. Longitudinal Data with Covariate Measurement Error. MultiState Models with ErrorProne Data. CaseControl Studies with Measurement Error or Misclassification. Analysis with Error in Responses. Miscellaneous Topics. Appendix. References.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781493966387 20170925
5. Multivariate analysis with LISREL [2016]
 Jöreskog, K. G., author.
 [Cham] : Springer, 2016.
 Description
 Book — 1 online resource.
 Summary

 Preface
 Getting Started
 Regression Models
 Generalized Linear Models
 Multilevel Analysis
 Principal Components (PCA)
 Exploratory Factor Analysis (EFA)
 Confirmatory Factor Analysis(CFA)
 Structural Equation Models (SEM) with Latent Variables
 Analysis of Longitudinal Data
 Multiple Groups
 Appendix
 Basic Matrix Algebra and Statistics
 Testing Normality
 Computational Notes on Censored Regression
 Normal Scores
 Assessment of Fit
 General Statistical Theory
 Iteration Algorithms
 References. .
 Phadia, Eswar G., author.
 Second edition.  Switzerland : Springer International, [2016]
 Description
 Book — 1 online resource (xvii, 327 pages).
 Summary

 Prior Processes. Inference Based on Complete Data. Inference Based on Incomplete Data.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9783319327884 20160912
7. Bayesian nonparametric data analysis [2015]
 Müller, Peter, author.
 Cham, Switzerland : Springer, [2015]
 Description
 Book — 1 online resource.
 Seber, G. A. F. (George Arthur Frederick), 1938 author.
 Cham : Springer, [2015]
 Description
 Book — 1 online resource.
 Summary

 1.Preliminaries.
 2. The Linear Hypothesis. 3.Estimation. 4.Hypothesis Testing. 5.Inference Properties. 6.Testing Several Hypotheses. 7.Enlarging the Model. 8.Nonlinear Regression Models. 9.Multivariate Models. 10.Large Sample Theory: ConstraintEquation Hypotheses. 11.Large Sample Theory: FreedomEquation Hypotheses. 12.Multinomial Distribution. Appendix. Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9783319219295 20160619
 Marshall, Albert W.
 2nd ed.  New York : Springer Science+Business Media, LLC, 2011.
 Description
 Book — xxvii, 909 p. : ill.
 Online

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10. Targeted learning [electronic resource] : causal inference for observational and experimental data [2011]
 Laan, M. J. van der.
 New York : Springer, c2011.
 Description
 Book — xlix, 626 p.
 Online

 dx.doi.org SpringerLink
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 Thas, O. (Olivier)
 New York ; London : Springer, c2010.
 Description
 Book — xviii, 353 p. : ill. ; 24 cm.
 Summary

 OneSample Problems. Preliminaries (Building Blocks). Graphical Tools. Smooth Tests. Methods Based on the Empirical Distribution Function. TwoSample and KSample Problems. Preliminaries (Building Blocks). Graphical Tools. Some Important TwoSample Tests. Smooth Tests. Methods Based on the Empirical Distribution Function. Two Final Methods and Some Final Thoughts.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387927091 20160605
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12. Design of observational studies [2010]
 Rosenbaum, Paul R.
 New York : Springer, c2010.
 Description
 Book — xviii, 384 p. : ill. ; 25 cm.
 Summary

 Introduction. Matching to control bias from measured covariates. Addressing bias from covariates that were not measured.
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QA279 .R668 2010  Unknown 
 Zhang, Heping (Professor)
 2nd ed.  New York : Springer, c2010.
 Description
 Book — xiv, 259 p.
 Online

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14. Spatial statistics and modeling [2010]
 Gaetan, Carlo.
 New York : Springer, c2010.
 Description
 Book — xv, 297 p. : ill., maps ; 25 cm.
 Summary

 Second order spatial models and geostatistics. GibbsMarkov random fields on networks. Spatial point processes. Simulation of spatial models. Statistics for spatial models.
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QA278.2 .G34 2010  Unknown 
 Bai, Zhidong.
 2nd ed.  New York ; London : Springer, c2010.
 Description
 Book — 1 online resource.
 Summary

 Introduction. Wigner matrices and semicircular law. Sample covariance matrices and the MarcenkoPastur law. Product of two random matrices. Limits of extreme eigenvalues. Spectrum separation. Semicircle law for Hadamard products. Convergence rates of ESD. CLT for linear spectral statistics. Eigenvectors of sample covariance matrices. Circular law. Some applications of RMT.
 (source: Nielsen Book Data)
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 dx.doi.org SpringerLink
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 Thas, O. (Olivier)
 New York ; London : Springer, 2009.
 Description
 Book — 1 online resource (xviii, 353 p.) : ill.
 Summary

 OneSample Problems. Preliminaries (Building Blocks). Graphical Tools. Smooth Tests. Methods Based on the Empirical Distribution Function. TwoSample and KSample Problems. Preliminaries (Building Blocks). Graphical Tools. Some Important TwoSample Tests. Smooth Tests. Methods Based on the Empirical Distribution Function. Two Final Methods and Some Final Thoughts.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387927091 20160605
 Hastie, Trevor.
 2nd ed.  New York : Springer, c2009.
 Description
 Book — xxii, 745 p. : ill. ; 24 cm.
 Summary

 Introduction. Overview of supervised learning. Linear methods for regression. Linear methods for classification. Basis expansions and regularization. Kernel smoothing methods. Model assessment and selection. Model inference and averaging. Additive models, trees, and related methods. Boosting and additive trees. Neural networks. Support vector machines and flexible discriminants. Prototype methods and nearestneighbors. Unsupervised learning.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387848570 20160619
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Marine Biology Library (Miller), Science Library (Li and Ma)
Marine Biology Library (Miller)  Status 

Missing  
Q325.75 .H37 2009  Unavailable 
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Q325.75 .H37 2009  Unknown 
Science Library (Li and Ma)  Status 

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Q325.75 .H37 2009  Unknown On reserve at Li and Ma Science Library 2hour loan 
Q325.75 .H37 2009  Unknown On reserve at Li and Ma Science Library 2hour loan 
Q325.75 .H37 2009  Unknown On reserve at Li and Ma Science Library 2hour loan 
STATS315A01
 Course
 STATS315A01  Modern Applied Statistics: Learning
 Instructor(s)
 Hastie, Trevor John
18. Introduction to nonparametric estimation [2009]
 Tsybakov, A. B. (Alexandre B.)
 [English ed.].  New York : Springer, c2009.
 Description
 Book — xii, 214 p. : ill. ; 25 cm.
 Summary

 Nonparametric estimators. Lower bounds on the minimax risk. Asymptotic efficiency and adaptation. Appendix. References. Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387790510 20160528
This is a revised and extended version of the French book. The main changes are in Chapter 1 where the former Section 1. 3 is removed and the rest of the material is substantially revised. Sections 1. 2. 4, 1. 3, 1. 9, and 2. 7. 3 are new. Each chapter now has the bibliographic notes and contains the exercises section. I would like to thank Cristina Butucea, Alexander Goldenshluger, Stephan Huckenmann, Yuri Ingster, Iain Johnstone, Vladimir Koltchinskii, Alexander Korostelev, Oleg Lepski, Karim Lounici, Axel Munk, Boaz Nadler, AlexanderNazin, PhilippeRigollet, AngelikaRohde, andJonWellnerfortheir valuable remarks that helped to improve the text. I am grateful to Centre de Recherche en Economie et Statistique (CREST) and to Isaac Newton Ins tute for Mathematical Sciences which provided an excellent environment for ?nishing the work on the book. My thanks also go to Vladimir Zaiats for his highly competent translation of the French original into English and to John Kimmel for being a very supportive and patient editor. Alexandre Tsybakov Paris, June 2008 Preface to the French Edition The tradition of considering the problem of statistical estimation as that of estimation of a ?nite number of parameters goes back to Fisher. However, parametric models provide only an approximation, often imprecise, of the  derlying statistical structure. Statistical models that explain the data in a more consistent way are often more complex: Unknown elements in these models are, in general, some functions having certain properties of smoo ness.
(source: Nielsen Book Data) 9781441927095 20160611
Science Library (Li and Ma)
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QA278.8 .T7913 2009  Unknown 
 Tsybakov, A. B. (Alexandre B.)
 English ed.  New York ; London : Springer, c2009.
 Description
 Book — 1 online resource (xii, 214 p.) : ill.
 Summary

 Nonparametric estimators. Lower bounds on the minimax risk. Asymptotic efficiency and adaptation. Appendix. References. Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387790510 20160612
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20. Monte Carlo and quasiMonte Carlo sampling [2009]
 Lemieux, Christiane, 1972
 New York : Springer, c2009.
 Description
 Book — xvi, 373 p. : ill. ; 25 cm.
 Summary

 The Monte Carlo method. Sampling from known distributions. Pseudorandom number generators. Variance reduction techniques. QuasiMonte Carlo constructions. Using quasiMonte Carlo constructions. Using quasiMonte Carlo in practice. Financial applications. Beyond numerical integration. Review of algebra. Error and variance analysis for Halton sequences. References. Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387781648 20160528
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Science Library (Li and Ma)  Status 

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QA298 .L46 2009  Unknown 
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