Development Of Modern Statistics And Related Topics: In Celebration Of Prof Yaoting Zhang's 70th Birthday


Book Description

This book encompasses a wide range of important topics. The articles cover the following areas: asymptotic theory and inference, biostatistics, economics and finance, statistical computing and Bayesian statistics, and statistical genetics. Specifically, the issues that are studied include large deviation, deviation inequalities, local sensitivity of model misspecification in likelihood inference, empirical likelihood confidence intervals, uniform convergence rates in density estimation, randomized designs in clinical trials, MCMC and EM algorithms, approximation of p-values in multipoint linkage analysis, use of mixture models in genetic studies, and design and analysis of quantitative traits.




Development of Modern Statistics and Related Topics


Book Description

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




Doing Bayesian Data Analysis


Book Description

There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and 'rusty' calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and BUGS 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 BUGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment




Statistica Sinica


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Mathematical Reviews


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Book Review Index


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Every 3rd issue is a quarterly cumulation.




Advances in Statistical Modeling and Inference


Book Description

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.







The Elements of Financial Econometrics


Book Description

A compact, master's-level textbook on financial econometrics, focusing on methodology and including real financial data illustrations throughout. The mathematical level is purposely kept moderate, allowing the power of the quantitative methods to be understood without too much technical detail.