000 04164nam a22004815i 4500
001 978-981-10-3307-0
003 DE-He213
005 20200812133348.0
007 cr nn 008mamaa
008 170202s2017 si | s |||| 0|eng d
020 _a9789811033070
_9978-981-10-3307-0
024 7 _a10.1007/978-981-10-3307-0
_2doi
040 _cCUS
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMED090000
_2bisacsh
072 7 _aPBT
_2thema
072 7 _aMBNS
_2thema
082 0 4 _a519.5
_223
245 1 0 _aMonte-Carlo Simulation-Based Statistical Modeling
_h[electronic resource] /
_cedited by Ding-Geng (Din) Chen, John Dean Chen.
250 _a1st ed. 2017.
264 1 _aSingapore :
_bSpringer Singapore :
_bImprint: Springer,
_c2017.
300 _aXX, 430 p. 64 illus., 33 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aICSA Book Series in Statistics,
_x2199-0980
505 0 _aPart 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. .
520 _aThis 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. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.
650 0 _aStatistics .
650 0 _aBiostatistics.
650 1 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_0https://scigraph.springernature.com/ontologies/product-market-codes/S17030
650 2 4 _aBiostatistics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/L15020
700 1 _aChen, Ding-Geng (Din).
700 1 _aChen, John Dean.
830 0 _aICSA Book Series in Statistics,
_x2199-0980
856 4 0 _uhttps://doi.org/10.1007/978-981-10-3307-0
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
942 _cEBK
999 _c207121
_d207121