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1. Dynamic Mixed Models for Familial Longitudinal Data
by Brajendra C. Sutradhar
出版情報: New York, NY : Springer Science + Business Media, LLC, 2011
シリーズ名: Springer Series in Statistics ;
オンライン: http://dx.doi.org/10.1007/978-1-4419-8342-8
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2. Bayesian Theory and Methods with Applications
by Vladimir Savchuk, Chris P. Tsokos
出版情報: Paris : Atlantis Press, 2011
シリーズ名: Atlantis Studies in Probability and Statistics ; 1
オンライン: http://dx.doi.org/10.2991/978-94-91216-14-5
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3. Maximum Penalized Likelihood Estimation : Volume II: Regression
by Vincent N. LaRiccia, Paul P. Eggermont
出版情報: New York, NY : Springer-Verlag New York, 2009
シリーズ名: Springer Series in Statistics ;
オンライン: http://dx.doi.org/10.1007/b12285
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4. The Pleasures of Statistics : The Autobiography of Frederick Mosteller
by Frederick Mosteller ; edited by Stephen E. Fienberg, David C. Hoaglin, Judith M. Tanur
出版情報: New York, NY : Springer Science+Business Media, LLC, 2010
オンライン: http://dx.doi.org/10.1007/978-0-387-77956-0
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5. Inference for Diffusion Processes : With Applications in Life Sciences
by Christiane Fuchs
出版情報: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013
オンライン: http://dx.doi.org/10.1007/978-3-642-25969-2
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Introduction
Stochastic Modelling in Life Sciences
Stochastic Differential Equations and Diffusions in a Nutshell
Approximation of Markov Jump Processes by Diffusions
Diffusion Models in Life Sciences
Parametric Inference for Discretely-observed Diffusions
Bayesian Inference for Diffusions with Low-frequency Observations
Application I: Spread of Influenza
Application II: Analysis of Molecular Binding
Conclusion and Outlook
Benchmark Models
Miscellaneous
Supplementary Material for Application I
Supplementary Material for Application II
Notation
References
Introduction
Stochastic Modelling in Life Sciences
Stochastic Differential Equations and Diffusions in a Nutshell
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6. Regression : Models, Methods and Applications
by Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
出版情報: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013
オンライン: http://dx.doi.org/10.1007/978-3-642-34333-9
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Introduction
Regression Models
The Classical Linear Model
Extensions of the Classical Linear Model
Generalized Linear Models
Categorical Regression Models
Mixed Models
Nonparametric Regression
Structured Additive Regression
Quantile Regression
A Matrix Algebra
B Probability Calculus and Statistical Inference
Bibliography
Index
Introduction
Regression Models
The Classical Linear Model
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7. Complex Models and Computational Methods in Statistics
edited by Matteo Grigoletto, Francesco Lisi, Sonia Petrone
出版情報: Milano : Springer Milan : Imprint: Springer, 2013
シリーズ名: Contributions to Statistics ;
オンライン: http://dx.doi.org/10.1007/978-88-470-2871-5
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A new unsupervised classification technique through nonlinear non parametric mixed effects models
Estimation approaches for the apparent diffusion coefficient in Rice-distributed MR signals
Longitudinal patterns of financial product ownership: a latent growth mixture approach
Computationally efficient inference procedures for vast dimensional realized covariance models
A GPU software library for likelihood-based inference of environmental models with large datasets
Theoretical Regression Trees: a tool for multiple structural-change models analysis
Some contributions to the theory of conditional Gibbs partitions
Estimation of traffic matrices for LRD traffic
A Newton's method for benchmarking time series
Spatial smoothing for data distributed over non-planar domains
Volatility swings in the US financial markets
Semicontinuous regression models with skew distributions
Classification of multivariate linear-circular data with nonignorable missing values
Multidimensional connected set detection in clustering based on nonparametric density estimation
Using integrated nested Laplace approximations for modelling spatial healthcare utilization
Supply function prediction in electricity auctions
A hierarchical bayesian model for RNA-Seq data
A new unsupervised classification technique through nonlinear non parametric mixed effects models
Estimation approaches for the apparent diffusion coefficient in Rice-distributed MR signals
Longitudinal patterns of financial product ownership: a latent growth mixture approach
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8. An Introduction to Order Statistics
by Mohammad Ahsanullah, Valery B Nevzorov, Mohammad Shakil
出版情報: Paris : Atlantis Press : Imprint: Atlantis Press, 2013
シリーズ名: Atlantis Studies in Probability and Statistics ; 3
オンライン: http://dx.doi.org/10.2991/978-94-91216-83-1
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Basic definitions
Distributions of order statistics
Sample quantiles and ranges
Representations for order statistics
Conditional distributions of order statistics.-Order statistics for discrete distributions
Moments of order statistics: general relations
Moments of uniform and exponential order statistics
Moment relations for order statistics: normal distribution
Asymptotic behavior of the middle and intermediate order statistics
Asymptotic behavior of the extreme order statistics
Some properties of estimators based on order statistics
Minimum variance linear unbiased estimators
Minimum variance linear unbiased estimators and predictors based on censored samples
Estimation of parameters based on fixed number of sample quantiles
Order statistics from extended samples
Order statistics and record values
Characterizations of distributions based on properties of order statistics
Order statistics and record values based on Fα distributions
Generalized order statistics
Compliments and problems
Basic definitions
Distributions of order statistics
Sample quantiles and ranges
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9. Developments in Statistical Evaluation of Clinical Trials
edited by Kees van Montfort, Johan Oud, Wendimagegn Ghidey
出版情報: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014
オンライン: http://dx.doi.org/10.1007/978-3-642-55345-5
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Preface
List of contributors
1.Statistical Models and Methods for Incomplete Data in Randomized Clinical Trials. M.A. McIsaac and R.J. Cook
2.Bayesian Decision Theory and the Design and Analysis of Randomized Clinical Trials. A.R. Willan
3.Designing Multi-Arm Multi-Stage Clinical Studies. T.Jaki
4.Statistical Approaches to Improving Trial Efficiency and Conduct. J.Pogue, P. J. Devereaux and S.Yusuf
5.Competing Risk and Survival Analysis. K.van Montfort, P.Fennema and W.Ghidey
6.Recent Developments in Group-Sequential Designs. J.M. S. Wason
7.Statistical Inference for Non-Inferiority of a Diagnostic Procedure Compared to an Alternative Procedure, Based on the Difference in Correlated Proportions from Multiple Raters. H.Saeki and T.Tango
8.Design and Analysis of Clinical Trial Simulations. K.Kuribayashi
9.Causal Effect Estimation and Dose Adjustment in Exposure-Response Relationship Analysis. J.Wang
10.Different Methods to Analyse Results of a Randomised Controlled Trial with More Than one Follow-up Measurement. J.W. R. Twisk
11.Statistical Methods for the Assessment of Clinical Relevance. M.Kieser
12.Statistical Considerations in the Use of Composite Endpoints in Time to Event Analyses. R.J. Cook and K.-A.Lee
13.Statistical Validation of Surrogate Markers in Clinical Trials. A.Alonso, G.Molenberghs and G.van Breukelen
14.Biomarker-Based Designs of Phase III Clinical Trials for Personalized Medicine. S.Matsui, T.Nonaka and Y.Choai
15.Dose-Finding Models for Two-Agent Combination Phase I Trials. A.Hirakawa and S.Matsui
16.Multi-State Models Used in Oncology Trials. B.Gaschler-Markefski, K.Schiefele, J.Hocke and F.Fleischer
17.Review of Designs for Accommodating Patients’ or Physicians’ Preferences in Randomized Controlled Trials. A.S. Ismaila and S.D. Walter
18.Dose Finding Methods in Oncology: From the Maximum Tolerated Dose to the Recommended Phase II Dose. X.Paoletti and A.Doussau
Preface
List of contributors
1.Statistical Models and Methods for Incomplete Data in Randomized Clinical Trials. M.A. McIsaac and R.J. Cook
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10. Rankings and Preferences : New Results in Weighted Correlation and Weighted Principal Component Analysis with Applications. 1st ed. 2015
by Joaquim Pinto da Costa
出版情報: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015
シリーズ名: SpringerBriefs in Statistics ;
オンライン: http://dx.doi.org/10.1007/978-3-662-48344-2
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Introduction
The Weighted Rank Correlation Coefficient rW
The Weighted Rank Correlation Coefficient rW2
A Weighted Principal Component Analysis, WPCA1: Application to Gene Expression Data
A Weighted Principal Component Analysis (WPCA2) for Time Series Data
Weighted Clustering of Time Series
Appendix
References
Introduction
The Weighted Rank Correlation Coefficient rW
The Weighted Rank Correlation Coefficient rW2