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Development Of Modern Statistics And Related Topics


Development Of Modern Statistics And Related Topics
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Development Of Modern Statistics And Related Topics


Development Of Modern Statistics And Related Topics
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Author : Yaoting Zhang
language : en
Publisher: World Scientific
Release Date : 2003

Development Of Modern Statistics And Related Topics written by Yaoting Zhang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Mathematics categories.


An interview with Professor Yaoting Zhang / Qiwei Yao and Zhaohai Li -- Significance level in interval mapping / David O. Siegmund and Benny Yakir -- An asymptotic Pythagorean identity / Zhiliang Ying -- A Monte Carlo gap test in computing HPD regions / Ming-Hui Chen [und weitere] -- Estimating restricted normal means using the EM-type algorithms and IBF sampling / Ming Tan, Guo-Liang Tian and Hong-Bin Fang -- An example of algorithm mining: covariance adjustment to accelerate EM and Gibbs / Chuanhai Liu -- Large deviations and deviation inequality for kernel density estimator in L[symbol]-distance / Liangzhen Lei, Liming Wu and Bin Xie -- Local sensitivity analysis of model misspecification / Guobing Lu -- Empirical likelihood confidence intervals for the difference of two quantiles of a population / Yongsong Qin and Yuehua Wu -- Exponential inequalities for spatial processes and uniform convergence rates for density estimation / Qiwei Yao -- A skew regression model for inference of stock volatility / Tuhao J. Chen and Hanfeng Chen -- Explicit transitional dynamics in growth models / Danyang Xie -- A fiscal federalism approach to optimal taxation and intergovernmental transfers in a dynamic model / Liutang Gong and Heng-Fu Zou -- Sharing catastrophe risk under model uncertainty / Xiaodong Zhu -- Ranked set sampling: a methodology for observational economy / Zehua Chen -- Some recent advances on response-adaptive randomized designs / Feifang Hu -- A childhood epidemic model with birthrate-dependent transmission / Yingcun Xia -- Linear regression analysis with observations subject to interval censoring / Linxiong Li -- When can the Haseman-Elston procedure for quantitative trait loci be improved? Insights from optimal design theory / Zhaohai Li, Minyu Xie and Joseph L. Gastwirth -- A semiparametric method for mapping quantitative trait loci / Jian Huang and Kai Wang -- Structure mixture regression models / Hongtu Zhu and Heping Zhang



The World Bank Research Program 2004


The World Bank Research Program 2004
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Author : World Bank
language : en
Publisher: World Bank Publications
Release Date : 2005

The World Bank Research Program 2004 written by World Bank and has been published by World Bank Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Business & Economics categories.


The World Bank's research is intended to address critical issues and problems facing member governments in developing and transition economies. How can the governments of the poorest countries generate enough revenue to provide the education and health services essential to reducing poverty and promoting growth and development? How can poor countries attract investors to build the infrastructure their economies need? How can they develop systems to bring clean water to the 2 billion people without it today? How can they train teachers and bring to class the 115 million children who have not yet received any education? And how can rich countries be persuaded to lower market barriers, helping to reverse the decline in export prices for poor countries that has left them earning less from trade today than in the 1970s? These are the types of questions that are addressed in this edition of 'The World Bank Research Program: Abstracts from Current Studies'. This volume reports on research projects initiated, under way, or completed from July 2003 through June 2004. It covers 151 research projects on several broad development related issues, including agriculture, health, education, environment, infrastructure, investment climate, and more. The abstract for each project describes the questions addressed, the analytic methods used, the findings to date, and policy implications.



Advances In Statistical Modeling And Inference


Advances In Statistical Modeling And Inference
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Author : Vijay Nair
language : en
Publisher: World Scientific
Release Date : 2007

Advances In Statistical Modeling And Inference written by Vijay Nair and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Mathematics categories.


There have been major developments in the field of statistics over the last quarter century, spurred by the rapid advances in computing and data-measurement technologies. These developments have revolutionized the field and have greatly influenced research directions in theory and methodology. Increased computing power has spawned entirely new areas of research in computationally-intensive methods, allowing us to move away from narrowly applicable parametric techniques based on restrictive assumptions to much more flexible and realistic models and methods. These computational advances have also led to the extensive use of simulation and Monte Carlo techniques in statistical inference. All of these developments have, in turn, stimulated new research in theoretical statistics. This volume provides an up-to-date overview of recent advances in statistical modeling and inference. Written by renowned researchers from across the world, it discusses flexible models, semi-parametric methods and transformation models, nonparametric regression and mixture models, survival and reliability analysis, and re-sampling techniques. With its coverage of methodology and theory as well as applications, the book is an essential reference for researchers, graduate students, and practitioners.



Statistical Decision Theory And Related Topics V


Statistical Decision Theory And Related Topics V
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Author : Shanti S. Gupta
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Statistical Decision Theory And Related Topics V written by Shanti S. Gupta and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.


The Fifth Purdue International Symposium on Statistical Decision The was held at Purdue University during the period of ory and Related Topics June 14-19,1992. The symposium brought together many prominent leaders and younger researchers in statistical decision theory and related areas. The format of the Fifth Symposium was different from the previous symposia in that in addition to the 54 invited papers, there were 81 papers presented in contributed paper sessions. Of the 54 invited papers presented at the sym posium, 42 are collected in this volume. The papers are grouped into a total of six parts: Part 1 - Retrospective on Wald's Decision Theory and Sequential Analysis; Part 2 - Asymptotics and Nonparametrics; Part 3 - Bayesian Analysis; Part 4 - Decision Theory and Selection Procedures; Part 5 - Probability and Probabilistic Structures; and Part 6 - Sequential, Adaptive, and Filtering Problems. While many of the papers in the volume give the latest theoretical developments in these areas, a large number are either applied or creative review papers.



Nonparametric Statistical Methods And Related Topics


Nonparametric Statistical Methods And Related Topics
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Author : Francisco J. Samaniego
language : en
Publisher: World Scientific
Release Date : 2011

Nonparametric Statistical Methods And Related Topics written by Francisco J. Samaniego and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Mathematics categories.


Review papers. 1. On the scholarly work of P.K. Bhattacharya / P. Hall and F.J. Samaniego. 2. The propensity score and its role in causal inference / C. Drake and T. Loux. 3. Recent tests for symmetry with multivariate and structured data: a review / S.G. Meintanis and J. Ngatchou-Wandji -- Papers on general nonparametric inference. 4. On robust versions of classical tests with dependent data / J. Jiang. 5. Density estimation by sampling from stationary continuous time parameter associated processes / G.G. Roussas and D. Bhattacharya. 6. A Short proof of the Feigin-Tweedie theorem on the existence of the mean functional of a Dirichlet process / J. Sethuraman. 7. Max-min Bernstein polynomial estimation of a discontinuity in distribution / K.-S. Song. 8. U-statistics based on higher-order spacings / D.D. Tung and S.R. Jammalamadaka. 9. Nonparametric models for non-Gaussian longitudinal data / N. Zhang, H.-G. Muller and J.-L. Wang -- Papers on aspects of linear or generalized linear models. 10. Better residuals / R. Beran. 11. The use of Peters-Belson regression in legal cases / E. Bura, J.L. Gastwirth and H. Hikawa. 12. On a hybrid approach to parametric and nonparametric regression / P. Burman and P. Chaudhuri. 13. Nonparametric regression models with integrated covariates / Z. Cai. 14. A dynamic test for misspecification of a linear model / M.P. McAssey and F. Hsieh. 15. The principal component decomposition of the basic martingale / W. Stute -- Papers on time series analysis. 16. Fast scatterplot smoothing using blockwise least squares fitting / A. Aue and T.C.M. Lee. 17. Some recent advances in semiparametric estimation of the GARCH model / J. Di and A. Gangopadhyay. 18. Extreme dependence in multivariate time series: a review / R. Sen and Z. Tan. 19. Dynamic mixed models for irregularly observed water quality data / R.H. Shumway -- Papers on asymptotic theory. 20. Asymptotic behavior of the kernel density estimators for nonstationary dependent random variables with binned data / J.-F. Lenain, M. Harel and M.L. Puri. 21. Convergence rates of an improved isotonic regression estimator / H. Mukerjee. 22. Asymptotic distribution of the smallest eigenvalue of Wishart(N, n) When N, n ' [symbol] such that N/n --> 0 / D. Paul



Bayesian Missing Data Problems


Bayesian Missing Data Problems
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Author : Ming T. Tan
language : en
Publisher: CRC Press
Release Date : 2009-08-26

Bayesian Missing Data Problems written by Ming T. Tan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-26 with Mathematics categories.


Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae discovered by one of the author in 1995. Applying the Bayesian approach to important real-wor



Doing Bayesian Data Analysis


Doing Bayesian Data Analysis
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Author : John Kruschke
language : en
Publisher: Academic Press
Release Date : 2014-11-11

Doing Bayesian Data Analysis written by John Kruschke and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-11 with Mathematics categories.


Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes' rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and JAGS software - Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis) - Coverage of experiment planning - R and JAGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment - Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs



Contemporary Multivariate Analysis And Design Of Experiments In Celebration Of Prof Kai Tai Fang S 65th Birthday


Contemporary Multivariate Analysis And Design Of Experiments In Celebration Of Prof Kai Tai Fang S 65th Birthday
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Author : Jianqing Fan
language : en
Publisher: World Scientific
Release Date : 2005-03-22

Contemporary Multivariate Analysis And Design Of Experiments In Celebration Of Prof Kai Tai Fang S 65th Birthday written by Jianqing Fan and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-03-22 with Mathematics categories.


This book furthers new and exciting developments in experimental designs, multivariate analysis, biostatistics, model selection and related subjects. It features articles contributed by many prominent and active figures in their fields. These articles cover a wide array of important issues in modern statistical theory, methods and their applications. Distinctive features of the collections of articles are their coherence and advance in knowledge discoveries.



The Statistics Of Gene Mapping


The Statistics Of Gene Mapping
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Author : David Siegmund
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-27

The Statistics Of Gene Mapping written by David Siegmund and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-27 with Medical categories.


This book details the statistical concepts used in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The R programming language is developed in the text.



Linear And Generalized Linear Mixed Models And Their Applications


Linear And Generalized Linear Mixed Models And Their Applications
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Author : Jiming Jiang
language : en
Publisher: Springer Nature
Release Date : 2021-03-22

Linear And Generalized Linear Mixed Models And Their Applications written by Jiming Jiang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-22 with Medical categories.


This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data analysis.