書籍詳細

書籍詳細




洋書

モンテカルロ・シミュレーションにもとづく統計モデル化

Monte-Carlo Simulation-Based Statistical Modeling

(Icsa Book Series in Statistics)

Chen, Ding-geng (EDT)   Chen, John D. (EDT)

Springer 2017/02
473 p. 64 illus., 33 in color 24 cm   
装丁: Hrd    装丁について
テキストの言語: ENG    出版国: DE
ISBN: 9789811033063
KCN: 1027406161
紀伊國屋書店 選定タイトル
標準価格:¥18,873(本体 ¥17,158)   
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納期について
DDC: 570
KDC: F16 確率・統計
関連書リスト: SB2937B データサイエンス 2018
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Annotation

This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications.

Full Description

This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications.
Detailed information

Table of Contents

Part 1: Monte-Carlo Techniques.- 1. Overview of Monte-Carlo Techniques.- 2. On Improving the Efficiency of the Monte-Carlo Methods Using Ranked Simulated Approach.- 3. Joint generation of Different Types of Data with Specified Marginal and Association Structures for Simulation Purposes.- 4. Quantifying the Uncertainty in Optimal Experimental Schemes via Monte-Carlo Simulations.- 5. Normal and Non-normal Data Simulations for the Evaluation of Two-sample Location Tests.- 6. Understanding dichotomization from Monte-Carlo Simulations.- Part 2: Monte-Carlo Methods in Missing Data.- 7. Hybrid Monte-Carlo in Multiple Missing Data Imputations with Application to a Bone Fracture Data.- 8. Methods for Handling Incomplete Longitudinal Data due to Missing at Random Dropout.- 9. Applications of Simulation for Missing Data Issues in Longitudinal Clinical Trials.- 10. Application of Markov Chain Monte Carlo Multiple Imputation Method to Deal with Missing Data From the Mechanism of MNAR in Sensitivity Analysis for a Longitudinal Clinical Trial.- 11. Fully Bayesian Methods for Missing Data under Ignitability Assumption.- Part 3: Monte-Carlo in Statistical Modellings.- 12. Markov-Chain Monte-Carlo Methods in Statistical modelling.- 13. Monte-Carlo Simulation in Modeling for Hierarchical Linear Mixed Models.- 14. Monte-Carlo Simulation of Correlated Binary Responses.- 15. Monte Carlo Methods in Financial Modeling.- 16. Bayesian Intensive Computations in Elliptical Models.