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020 _a9789811033070
_9978-981-10-3307-0
024 7 _a10.1007/978-981-10-3307-0
_2doi
040 _aISI Library, Kolkata
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.
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 _aStatistical methods.
650 1 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/S17030
650 2 4 _aBiostatistics.
_0http://scigraph.springernature.com/things/product-market-codes/L15020
700 1 _aChen, Ding-Geng (Din).
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aChen, John Dean.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9789811033063
776 0 8 _iPrinted edition:
_z9789811033087
776 0 8 _iPrinted edition:
_z9789811098390
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
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c427410
_d427410